Currently, I am the program director of the master's program on Master's Programme in Computer and Systems Sciences,  I am responsible and examinator for the Internet of Things, Design for Complex and Dynamic Contexts and Network Security in the master's program, and Computer architecture in the bachelor's program. I have been responsible for the following Ph.D. courses: IoT Models and Application and Distributed Data Processing with a focus on Distributed Ledger Technology. I am an examiner of bachelor and master theses at the department.

Currently my research focuses on Distributed Systems, Distributed Data Processing in Distributed IoT, Cognitive Edge Continuum,  Tactile Internet,  and large-scale decentralized systems( Blockchain), Decentralization and Spatial Computing for Real Metaverse, Pervasive computing and Adversarial machine learning.  

I urval från Stockholms universitets publikationsdatabas

• Delay-Sensitive Resource Allocation for IoT Systems in 5G O-RAN Networks

  2024. Ramin Firouzi, Rahim Rahmani Chianeh. Internet of Things 25C

  Artikel

  The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency

  Läs mer om Delay-Sensitive Resource Allocation for IoT Systems in 5G O-RAN Networks

• Secure Data Sharing in Internet of Vehicles based on Blockchain and Attribute-based Encryption

  2023. Yuhong Li, Ruoyu Chen, Rahim Rahmani Chianeh. 2023 IEEE International Conference on Smart Internet of Things (SmartIoT), 56-63

  Konferens

  Sharing data among vehicles is one of the most important ways to provide safety-related and value-added services to connected vehicles. Nevertheless, access to the shared data must be controlled to prevent the exposure of users' privacy and data leakage or corruption. Attribute-based encryption (ABE) can provide data confidentiality and fine-grained access control. However, the complex and dynamic driving environment of vehicles may cause the attributes of vehicles to change frequently, and thus put a huge burden on the attribute management of the system or degrade the security of the system. In this paper, we propose a secure data sharing method by using ABE and blockchain for Internet of Vehicles. By using ABE, the data owner can stipulate the policy of the data access control based on the attributes of vehicles. The trusted authority is replaced by blockchain, which reduces the burden and solved the problem of single point failure of the trusted authority and increases the transparency of the whole system. An adaptive attribute revocation method is used to balance the revocation time and system cost. Moreover, the shared data are stored in a distributed Inter-Planetary File System (IPFS) to improve the efficiency and security of the data sharing system. The test results show that the proposed method can well satisfy the performance requirement of secure data sharing for Internet of Vehicles.

  Läs mer om Secure Data Sharing in Internet of Vehicles based on Blockchain and Attribute-based Encryption

• SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction

  2023. Mahbub Ul Alam (et al.). Nordic Machine Intelligence 3 (1), 27-47

  Artikel

  The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To address this matter, our study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP). Leveraging the approach of transfer learning with a multi-label-multi-class chest radiography dataset, we aim to interpret predictions pertaining to specific pathology classes. Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation. Notably, Grad-CAM demonstrates the most favorable performance in quantitative evaluation, while the LIME heatmap score segmentation visualization exhibits the highest level of medical significance. Our research underscores both the outcomes and the challenges faced in the holistic approach adopted for assessing these interpretability methods and suggests that a multimodal-based approach, incorporating diverse sources of information beyond chest radiography images, could offer additional insights for enhancing interpretability in the medical domain.

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• Attribute-based Encryption with Flexible Revocation for IoV

  2023. Ruoyu Chen, Yuhong Li, Rahim Rahmani Chianeh. Procedia Computer Science, 131-138

  Konferens

  Attribute-based encryption (ABE) has been used to provide data confidentiality and fine-grained access control in the Internet of Vehicles (IoV). However, the attributes of vehicles in IoV might change frequently due to the movements of vehicles. Thus, the invalid attributes need to be revoked in time and efficiently to ensure the security of the system. In this paper, we propose a data-sharing scheme based on ABE for IoV. By using a binary tree and attribute version keys, flexible revocation can be achieved for IoV. Moreover, the ciphertext can be stored on clouds, and the distribution and revocation of attribute keys can be realized by distributed attribute authorities. We performed the security analysis and proved the security of the proposed scheme. The results showed that the proposed scheme has lower average computing overhead in terms of attribute revocations compared with other schemes based on ABE, and can satisfy the performance requirement of data sharing for IoV.

  Läs mer om Attribute-based Encryption with Flexible Revocation for IoV

• COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning

  2023. Mahbub Ul Alam, Jaakko Hollmén, Rahim Rahmani Chianeh. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 646-653

  Konferens

  COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.

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• FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

  2023. Mahbub Ul Alam, Rahim Rahmani Chianeh. Sensors 23 (2)

  Artikel

  The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.

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• DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices

  2023. Kazi Masum Sadique, Rahim Rahmani Chianeh, Paul Johannesson. Sensors 23 (8)

  Artikel

  The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices’ heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

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• 5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

  2023. Ramin Firouzi, Rahim Rahmani Chianeh. Sensors 23 (1)

  Artikel

  Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.

  Läs mer om 5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems

• Cognitive Controller for 6G-Enabled Edge Autonomic

  2023. Rahim Rahmani Chianeh, Ramin Firouzi, Kazi Masum Sadique. Procedia Computer Science 220, 71-77

  Artikel

  This article proposes a new Artificial Intelligent (AI) and Machine Learning (ML) based framework for 6G-enabled Intelligent edge computing. The framework will be equipped with multiple cognitive controllers to harmoniously control various aspects in distributed intelligence toward edge nodes collaboration. Autonomic cognitive controller for edge computing is a popular computing paradigm where the distributed metadata processing and edge intelligence are performed at edge node in 5G/6G network for management, connectivity and interoperability. Some of studies focused on edge management improvement such as reduce the response time and bandwidth costs. However, the previous approaches are inadequate to support autonomously management for large-scale deployment for connectivity for dynamic and reliable communication. We propose a cognitive controller for edge autonomy and collaboration application development. Finally, we discuss challenges and open issues toward cognitive controller and distributed edge intelligence.

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• Workflow for a User-Driven Access to Digitalised Culture Heritage Collection Data

  2022. Rahim Rahmani Chianeh, Susanne Tienken. Un patrimoine pour l’avenir,une science pour le patrimoine, 401-407

  Konferens

  Les chercheurs et le grand public n'ont pas souvent significativement accès auxcollections du patrimoine culturel. Malgré une croissance de la numérisation, il estdifficile d’avoir accès à d’autres éléments, au-delà de ceux disponibles sur lesplateformes créées à cet effet. Cela empêche de mieux comprendre les données descollections du patrimoine culturel et de s'y engager. En ce sens, cet article décrit uncadre de travail pour la création d'un flux de fusion de données basé sur le contexterelatifs aux données des collections du patrimoine culturel et pour lequel un métaapprentissage distribué est nécessaire. Notre objectif est de promouvoir uneapproche axée sur l'utilisateur et d'améliorer l'accès au patrimoine culturel par lebiais d'infrastructures dans lesquelles les objets, le contexte et les biens immatérielssont connectés.  

  Läs mer om Workflow for a User-Driven Access to Digitalised Culture Heritage Collection Data

• A Distributed SDN Controller for Distributed IoT

  2022. Ramin Firouzi, Rahim Rahmani. IEEE Access 10, 42873-42882

  Artikel

  Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing towards edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in enormous amounts of data. Transmitting all these data to the cloud has recently proven to be a performance bottleneck, as it causes many network issues relating to latency, power consumption, security, privacy, etc. However, existing paradigms do not use edge devices for decision making. The use of distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks to edge devices within the network, rather than sending all data to a central server. In this approach, all computational tasks and data are shared among edge devices. To achieve efficient distribution of IoT intelligence and utilization of network resources, it is necessary to integrate the transport network control with distributed edge and cloud resources to provide dynamic and efficient IoT services. This challenge can be overcome by equipping an edge IoT gateway with the intelligence required to extract information from raw data and to decide whether to actuate itself or to outsource complex tasks to the cloud. Distributed intelligence refers to a collaboration between the cloud and the edge. In this context, we first introduce a distributed SDN-based architecture for IoT that enables IoT gateways to perform IoT processing dynamically at the edge of the network, based on the current state of network resources. Next, we propose an algorithm for selecting clients for the training process of Federated Learning Applications, based on the context information of the network. In the last step, we deploy Federated Learning Applications in our distributed SDN-based architecture, using the gateways to provide distributed intelligence at the edge of the network, and conduct a comprehensive and detailed evaluation of the system from several perspectives.

  Läs mer om A Distributed SDN Controller for Distributed IoT

• Distributed Meta-Learning for Context Based on Culture Heritage Collection Data in an Immersive Reality System

  2022. Rahim Rahmani Chianeh, Susanne Tienken. Procedia Computer Science 210, 70-77

  Artikel

  This article proposes a new workflow for Culture Heritage (CH) Collection Data in which distributed meta-learning with context-based fusion is required. The multidisciplinary research outlines a new paradigm for collaborative creation of a context- based data fusion CH workflow. Whose criterion restricts the search space to the cultural sector to include multiple other CH object types, texts and predominantly analyzes visualizations of CH object metadata. The approach promotes user-driven content creation and offsets economic models, thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet of Things infrastructure to connect objects, context and intangible assets. We enable an online virtual world that incorporates proximity of pervasive information, objects, people, processes, data and places. Finally, we investigate some new ways to achieve immersive experiences via distributed meta-learning computing and point out the necessity to do more with regard to collaborative interaction creation.

  Läs mer om Distributed Meta-Learning for Context Based on Culture Heritage Collection Data in an Immersive Reality System

• Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

  2021. Mahbub Ul Alam, Rahim Rahmani Chianeh. Communications in Computer and Information Science 1400, 366-384

  Artikel

  The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

  Läs mer om Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

• Semantic Enrichment of Vital Sign Streams through Ontology-based Context Modeling using Linked Data Approach

  2021. Sachiko Lim, Rahim Rahmani Chianeh, Paul Johannesson. Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021), 292-299

  Konferens

  The Internet of Things (IoT) creates an ecosystem that connects people and objects through the internet. IoTenabled healthcare has revolutionized healthcare delivery by moving toward a more pervasive, patientcentered, and preventive care model. In the ongoing COVID-19 pandemic, it has also shown a great potential for effective remote patient health monitoring and management, which leads to preventing straining the healthcare system. Nevertheless, due to the heterogeneity of data sources and technologies, IoT-enabled healthcare systems often operate in vertical silos, hampering interoperability across different systems. Consequently, such sensory data are rarely shared nor integrated, which can undermine the full potential of IoT-enabled healthcare. Applying semantic technologies to IoT is a promising approach for fulfilling heterogeneity, contextualization, and situation-awareness requirements for real-time healthcare solutions. However, the enrichment of sensor streams has been under-explored in the existing literature. There is also a need for an ontology that enables effective patient health monitoring and management during infectious disease outbreaks. This study, therefore, aims to extend the existing ontology to allow patient health monitoring for the prevention, early detection, and mitigation of patient deterioration. We evaluated the extended ontology using competency questions and illustrated a proof-of-concept of ontology-based semantic representation of vital sign streams.

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• Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices

  2021. Mahbub Ul Alam, Rahim Rahmani. Sensors 21 (15)

  Artikel

  Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

  Läs mer om Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices

• Context-based Reasoning through Fuzzy Logic for Edge Intelligence

  2021. Ramin Firouzi, Rahim Rahmani Chianeh, Theo Kanter. International Journal of Ubiquitous Systems and Pervasive Networks (JUSPN) 15 (1), 17-25

  Artikel

  With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

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• Dynamic and Decentralized Trust Management for the Internet of Things (IoT) Paradigm

  2021. Kazi Masum Sadique, Rahim Rahmani Chianeh, Paul Johannesson. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), 1017-1026

  Konferens

  Trust is an invisible behavior of any entity. An entity could be a living being or a cyber-physical system. The Internet of Things (IoT) is a connected network of smart objects or things where trusted relationships are crucial. Trust in an entity can increase or decrease based on different parameters and properties of the specific entity. Trusted relationships can dynamically reach based on contextual data collected over time. The heterogeneous behavior of IoT devices makes trust measurement more difficult. The massive deployment of IoT devices and related innovative IoT applications leads to exploring new trust management frameworks for the IoT paradigm. Emerging IoT applications need to trust entities deployed by third-party providers. Innovative external IoT applications need to be dynamically trusted by the IoT devices and IoT gateways. Dynamic trust achievement is a complex process when an entity is new within the network. In this article, we have defined the trust management for IoT and discussed the need for trusted architecture for dynamic IoT infrastructure, and elaborated the requirements of trust management policies. We have also heightened the need for decentralized architecture for trust management for the Internet of Things (IoT). A new edge-centric multi-agent-based dynamic and decentralized trust management model is proposed and simulated to solve the aforementioned issues. The results of this work are useful for further research in the field of trust management for IoT. 

  Läs mer om Dynamic and Decentralized Trust Management for the Internet of Things (IoT) Paradigm

• Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

  2021. Ramin Firouzi, Rahim Rahmani Chianeh, Theo Kanter. Procedia Computer Science, 24-32

  Konferens

  The introduction of distributed-reasoning through ubiquitous instrumentation within the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault-tolerance, traffic, healthcare, so on. Using a ubiquitous controller to interconnect devices in the IoT, however monumental, is still in its embryonic stage, it has the potential to create distributed-intelligent IoT solutions that are more eclient and safer than centric intelligence. It is essential to step in a new direction for designing a distributed intelligent controller for task scheduling as a means to, first, dynamically interact with a smart environment in eclient real-time data processing and, second, react to flexible changes. To cope with these issues, we outline a two-level intelligence schema, using edge computing to enhance distributed IoT. The edge schema pushes the streaming processing capability from cloud to edge devices to better support timely and reliable streaming analytics to improve the performance of smart IoT applications. In this paper, in order to provide better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller to provide low-level intelligence by employing a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

  Läs mer om Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

• Federated Learning for Distributed Reasoning on Edge Computing

  2021. Ramin Firouzi, Rahim Rahmani Chianeh, Theo Kanter. Procedia Computer Science, 419-427

  Konferens

  The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site

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• Edge-based Interoperable Service-driven Information Distribution for Intelligent Pervasive Services

  Xiao Bin (et al.). Pervasive and Mobile Computing

  Artikel

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• Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

  2021. Mahbub Ul Alam, Rahim Rahmani. Biomedical Engineering Systems and Technologies, 366-384

  Kapitel

  The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

  Läs mer om Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

• Gateway Controller with Deep Sensing

  2021. Rahim Rahmani, Ramin Firouzi. International Journal of Communication Networks and Distributed Systems 26 (1), 1-29

  Artikel

  The Internet of Things(IoT) will revolutionize the Future Internet through ubiquitous sensing. One of the challenges of having the hundreds of billions of devices that are estimated to be deployed would be rise of an enormous amount of data, along with the devices ability to manage. This paper presents an approach as a controller solution and designed specifically for autonomous management, connectivity and data interoperability in an IoT gateway. The approach supports distributed IoT nodes with both management and data interoperability with other cloud-based solutions. The concept further allows gateways to easily collect and process interoperability of data from IoT devices. We demonstrated the feasibility of the approach and evaluate its advantages regarding deep sensing and autonomous enabled gateway as an edge computational intelligence.

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• A Blockchain-Assisted Intelligent Transportation System Promoting Data Services with Privacy Protection

  2020. Yuhong Li, Rahim Rahmani, Yiwei Pei. Sensors 20 (9)

  Artikel

  Being able to obtain various environmental and driving data from vehicles is becoming more and more important for the current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privacy protection and security of the current ITSs are hindering users and vehicles providing data. In this paper, we propose a new ITS architecture by using blockchain technology solving the privacy protection and security problems and promoting users and vehicles to provide data to ITSs. The proposed architecture uses blockchain as a trust infrastructure to protect users’ privacy and provide trustworthy services to users. It is also compatible with the legacy ITS infrastructure and services. In addition, the hierarchical organization of chains enables the scalability of the system, and the use of smart contracts provides a flexible way for introducing new services in the ITS. The proposed architecture is demonstrated by a proof of concept implementation based on Ethereum. The test results show that the proposed architecture is feasible.

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• A Scalable Digital Infrastructure for Sustainable Energy Grid Enabled by Distributed Ledger Technology

  2020. Rahim Rahmani, Yuhong Li. International Journal of Ubiquitous Systems and Pervasive Networks (JUSPN) 12 (2), 17-24

  Artikel

  The future of renewable energy transportation and distribution is dynamic and complex, with distributed renewable resources in required distributed control. It is suggested that Distributed Ledger Technology (DLT) is a timely innovation with the potential to facilitate this future. The transition to full renewable energy requires an infrastructure capable of handling intermittent production that has a low marginal cost. This requires a distributed control logic where devices with embedded intelligence coordinate local production, a decentralized energy market where prices are not primarily based on production, and an underlying digital infrastructure to enable both. Simulations and experiments have demonstrated great potential in such a digital infrastructure, but real-life tests have identified scalability as a remaining challenge. In this paper, we propose a DLT-based architecture for the energy grid with the development of existing solution concepts by implementing scalability solutions. To this end, we derive energy market components as a framework for building efficient microgrid. Then, we discuss the microgrid as a case study of such a market according to the required components within energy production, transmission, and distribution; distributed ledger platform operations, IoT device manufacturing,; software development; and research in IoT, edge and cloud computing, and energy systems.

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• A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

  2020. Rahim Rahmani, Ramin Firouzi, Mahbub Ul Alam. Engineering Proceedings 2 (1)

  Artikel

  The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are 1) To reach consensus on the main chain is a set of validators cast public votes to decide on which blocks to finalize and 2) scalability on how to increasing the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large scale IoT devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on various proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where the smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm even we show its behaves when varying parameters like latency or when clustering.

  Läs mer om A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

• An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner

  2020. Ramin Firouzi, Rahim Rahmani, Theo Kanter. The 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020), 102-111

  Konferens

  With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge

  Läs mer om An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner

• Distributed Adaptive Formation Control for Multi-UAV To Enable Connectivity

  2020. Rahim Rahmani, Ramin Firouzi, Theo G Kanter. International Journal of Computer Science Issues 17 (2), 1-7

  Artikel

  There is increasing demand for adaptive control of multi-robot and as well distributing large amount of content to cluster of UAV on operation. In recent years several large-scale accidents have been happened. To facilitate rescue operations and gather information, technology that can access and map inaccessible areas is needed. This paper presents a disruptive approach for address the issues with communication, data collection and data sharing for UAV units in inaccessible or dead zones and We demonstrated feasibility of the approach and evaluate its advantages over the Ad Hoc architecture involving autonomous gateways.

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• Enhancing Data Privacy in the Internet of Things (IoT) using Edge Computing

  2020. Kazi Masum Sadique, Rahim Rahmani Chianeh, Paul Johannesson. Trends in Computational Intelligence, Security and Internet of Things, 231-243

  Konferens

  The vast deployment of the Internet of Things (IoT) is improving human life standards every day. These IoT applications are producing a huge amount of data from the environment where it is deployed. The collected data are mostly including end-user private data or industrial data which are transmit-ted over the internet to the cloud devices for storing, processing, and sharing with the connected applications. Recent IoT data privacy related researches are mostly focused on data privacy within a particular location of the network or at a particular device but none has pointed and listed all the places where the end-user or industrial data privacy risks exist. In this work, we have addressed both technical and management aspects for the enhancement of the privacy of IoT data. We have identified and listed the places where IoT data privacy risks exist, followed by our proposed model for data privacy enhancement in the inter-net of things (IoT) and listed ten suggestions for avoiding data privacy leakage and for IoT data privacy enhancement. The results of this work should be useful for both academic researchers and stakeholders from the industry while designing and implementing new IoT solutions for the enhancement of human society.

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• Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology

  2020. Yuhong Li, Rahim Rahmani, Pan Hui. Sensors 20 (12)

  Artikel

  The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

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• Fog Computing based Trust Solutions for Internet of Things (IoT)

  2020. Kazi Masum Sadique, Rahim Rahmani, Paul Johannesson. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 1-6

  Konferens

  Scientific research is performed based on real life problems. Reproducibility of research result is one of the curtail criteria for any scientific research. Proper documentation about research methodology allows fellow researcher to reproduce the results and to further extend of the research findings. Fog computing-based solution enhances quality of IoT solutions by making a bridge between cloud layer and end devices of IoT paradigm. Also fog computing can also increase security and trust in IoT by processing data at the fog layer which is closer to the source of data where it is produced. But fog computing-based trust solutions for Internet of Things (IoT) is a new trend. Fog computing can be considered as engineering discipline. IoT itself covers many aspects of human life; we can call IoT as a social science research area. In this paper, authors have discussed about different scientific research approaches used in fog computing based trust management in IoT researches.

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• IMSC-EIoTD

  2020. Kazi Masum Sadique, Rahim Rahmani, Paul Johannesson. Sensors 20 (22)

  Artikel

  The Internet of things (IoT) will accommodate several billions of devices to the Internet to enhance human society as well as to improve the quality of living. A huge number of sensors, actuators, gateways, servers, and related end-user applications will be connected to the Internet. All these entities require identities to communicate with each other. The communicating devices may have mobility and currently, the only main identity solution is IP based identity management which is not suitable for the authentication and authorization of the heterogeneous IoT devices. Sometimes devices and applications need to communicate in real-time to make decisions within very short times. Most of the recently proposed solutions for identity management are cloud-based. Those cloud-based identity management solutions are not feasible for heterogeneous IoT devices. In this paper, we have proposed an edge-fog based decentralized identity management and authentication solution for IoT devices (IoTD) and edge IoT gateways (EIoTG). We have also presented a secure communication protocol for communication between edge IoT devices and edge IoT gateways. The proposed security protocols are verified using Scyther formal verification tool, which is a popular tool for automated verification of security protocols. The proposed model is specified using the PROMELA language. SPIN model checker is used to confirm the specification of the proposed model. The results show different message flows without any error.

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• Identity Management in Internet of Things

  2020. Kazi Masum Sadique, Rahim Rahmani, Paul Johannesson. Proceedings of the 2nd International Conference on Communication, Devices and Computing, 495-504

  Konferens

  Internet of things (IoT) network needs to accommodate billions of connected smart objects (things). Connected IoT objects should be able to communicate with each other. Objects should be able to travel between different networks regardless of their locations, network providers, and manufacturers. To allow transparent movement of IoT objects, it is very important to have a trust relationship between these objects. To establish trusted relationship, unique identity is one of the key properties for any IoT object. It will not be possible for the devices to freely move within the networks if we do not have a common identity solution. The use of software-defined networking (SDN) approach in IoT is increasing these days, due to its flexibility and easy adaptability with any network. In this paper, we have presented an SDN-based identity management system which will possibly solve the unique identity problem and increase trust in heterogeneous IoT network.

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• Intelligent context-based healthcare metadata aggregator in internet of medical things platform

  2020. Mahbub Ul Alam, Rahim Rahmani. Procedia Computer Science 175, 411-418

  Artikel

  The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

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• Toward Semantic IoT Load Inference Attention Management for Facilitating Healthcare and Public Health Collaboration

  2020. Sachiko Lim, Rahim Rahmani. The 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH2020), 371-378

  Konferens

  The health of individuals and populations requires concerted and collaborative efforts by healthcare, public health, social care, and personal health management. The inter-sectoral collaborations are more crucial than ever, especially when facing public health crises, including the ongoing pandemic of coronavirus disease-2019 (COVID-19). Although the capabilities of healthcare and public health systems have increased with a dramatic boost in the use of the Internet of Things (IoT), such IoT-enabled systems are often operating in silos. A pressing need, thus, is the seamless integration of those currently incompatible systems. A promising solution is to leverage semantic technologies to increase interoperability among such systems. Therefore, this article aims to: conduct a systematic review on the current state-of-the-art semantic IoT solutions used in health domain; identify the associated challenges; propose a federated edge-cloud semantic IoT architecture to facilitate the healthcare and public health (HC-PH) collaborations for the health and well-being of the individuals and populations.

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• A Blockchain-based Architecture for Stable and Trustworthy Smart Grid

  2019. Yuhong Li (et al.). Procedia Computer Science 155, 410-416

  Artikel

  Smart Grid represents an efficient power transmission, distribution and management system. However, solutions for Smart Grid have raised security and privacy problems. Moreover, with the introduction of renewable energy resources, such as rooftop solar panels and small biogas plants, more and more electricity consumers are involved in the energy generation system. This may cause the power system unstable and/or the waste of the energy. Blockchain is a promising technology for solving these problems in the future energy system on account of its distributed trust, anonymity, data integrity and availability. In this paper, we propose a Blockchain-based architecture for Smart Grid. By using the proposed architecture, electricity consumers can be fully involved in the energy system and tracing the details of the energy they have consumed or generated. At the same time, the stability of the energy system can be kept, reducing the waste of the energy and potential hazard to the electrical equipment.

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• Challenges and Research Directions for Blockchains in the Internet of Things

  2019. Frank Golatowski (et al.). 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS 2019)

  Konferens

  In this paper, we analyze the state of the art in distributed ledger technologies and blockchains and investigate potential applications in the Internet of Things (IoT) domain. Afterwards, we discuss interoperability of blockchains, and their use in smart contracts, and artificial intelligence.

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• Secure Computation on 4G/5G Enabled Internet-of-Things

  2019. Ken Andersson (et al.). Wireless Communications & Mobile Computing 2019

  Artikel

  The rapid development of Internet-of-ings (IoT) techniques in G/ G deployments is witnessing the generation of massive amounts of data which are collected, stored, processed, and presented in an easily interpretable form. Analysis of IoT data helps provide smart services such as smart homes, smart energy, smart health, and smart environments through G and G technologies. At the same time, the threat of the cyberattacks and issues with mobile internet security is becoming increasingly severe, which introduces new challenges for the security of IoT systems and applications and the privacy of individuals thereby. Protecting IoT data privacy while enabling data availability is an urgent but difficult task.

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• Trust in Internet of Things

  2019. Kazi Masum Sadique, Rahim Rahmani, Paul Johannesson. 2018 International Conference on Innovation in Engineering and Technology (ICIET)

  Konferens

  Current technology driven world is moving forward with massive implementation of smart objects. These smart objects or Internet of Things (IoT) nodes are involved in sensing of real time critical data from surroundings. Though these IoT nodes are involved in very specific task but their existence is very important for the complete system. Usually, in an IoT architecture, several heterogeneous nodes connect with a nearest gateway. The gateway itself connects with the world via Internet. IoT nodes need to flexible about connectivity as those may need to leave and join any gateway based on context (time, location etc.). To achieve flexibility, IoT components need a trust relationship between each other. In this paper, we have proposed a mechanism for integrating distributed trust management in IoT by using edge computing technology, considering the scalability and heterogeneous capability of IoT devices.

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• A Scalable Distriubuted Ledger for Internet of Things based on Edge Computing

  2018. Rahim Rahmani, Yuhong Li, Theo Kanter. Proceedings of 2018 Seventh International Conference on Advances in Computing, Communication and Information Technology - CCIT 2018, 41-45

  Konferens

  Internet of Things (IoT) is becoming necessities of people’s daily life and establishing itself as an essential part of future Internet. One of the challenges for using IoT is the security of data collected by trillions of IoT devices and used by millions of services. Distributed ledger technology (DLT) provides a distributed security method which can benefit IoT. Yet challenges are put forward when integrating DLT with IoT, such as scalability and heterogeneous capability of IoT devices. In this paper, we propose a mechanism for integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on various proximity context information. A cluster head is used to bridge the IoT devices with the blockchain network where smart contract is deployed. Through this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate our mechanism and discuss issues that should be considered and implemented when using the proposed mechanism.

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• A Systematic Review of Learning Trough Mobile Augmented Reality

  2018. Hillevi Hedberg (et al.). International Journal of Interactive Mobile Technologies (iJIM) 12 (3), 75-85

  Artikel

  In the beginning of 2000, researchers started to see the potential of using Augmented Reality (AR) in educational and foresaw that further research within the field. Since then, AR research have taken many different approaches. This is also true for AR in relation to pedagogical purposes. This study is to investigate what has been studied within the AR field related to mobile augmented reality. It attempts to make systematic review of how learning and pedagogical aspects have been approached in the articles. In recent years, mobile augmented reality has become increasingly interesting due to the mobile devices small form factors and their ability to let the students move around freely while learning. The aim of this study is to make a systematic review of pedagogical uses of mobile augmented reality. Based on a review of previous literature of mobile AR systems for pedagogical purposes, published between 2000-2017, make it possible to see in which direction mobile AR systems for education are heading and how future mobile AR systems should be designed to best fit the needs of future students so they can more effectively improve their learning.

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• A Time-Scaling Data Generation Method For Internet of Things Simulation

  2018. Zhuo Hu (et al.). Proceedings of 2018 Seventh International Conference on Advances in Computing, Communication and Information Technology - CCIT 2018, 34-40

  Konferens

  Simulation plays an important role on studing the Internet of Things (IoT) traffic, which has an increasing impact on network infrastructures.The existing simulation tools and mechanisms on IoT mainly focus on simulating large-scale deployment of IoT and heterogeneous IoT applications. This paper concentrates on how to simulate the lengthy, burthty and multi-level time-scale IoT applications quickly. A time-scale data generation (TSDG) method is proposed to reduce the used simulation time of different IoT scenarios while keeping the real world characteristics of the IoT applications. Implementations of TSDG in ns-3 and simulation experiments of smart home and smart shopping centre are described in the paper. The evaluations results show that the proposed TSDG method can effectively reduce the time used to simulation IoT applications in ns-3 while reflect the IoT application traffic in the real world.

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• Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)

  2018. Hasibur Rahman, Rahim Rahmani. Applied Computing and Informatics 14 (1), 73-87

  Artikel

  The unprecedented prevalence of ubiquitous sensing will revolutionise the Future Internet where state-of-the-art Internet-of-Things (IoT) is believed to play the pivotal role. In the fast forwarding IoT paradigm, hundreds of billions of things are estimated to be deployed which would give rise to an enormous amount of data. Cloud computing has been the prevailing choice for controlling the connected things and the data, and providing intelligence based on the data. But response time and network load are on the higher side for cloud based solutions. Recently, edge computing is gaining growing attention to overcome this by employing rule-based intelligence. However, requirements of rules do not scale well with the proliferation of things. At the same time, rules fail in uncertain events and only offer pre-assumed intelligence. To counter this, this paper proposes a novel idea of leveraging the belief-network with the edge computing to utilize as an IoT edge-controller the aim of which is to offer low-level intelligence for IoT applications. This low-level intelligence along with cloud-based intelligence form the distributed intelligence in the IoT realm. Furthermore, a learning approach similar to reinforcement learning has been proposed. The approach, i.e. enabling a Future IoT Controller (FITC) has been verified with a simulated SmartHome scenario which proves the feasibility of the low-level intelligence in terms of reducing rules domination, faster response time and prediction through learning experiences at the edge.

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• The Role of Mobile Edge Computing Towards Assisting IoT with Distributed Intelligence

  2018. Hasibur Rahman, Rahim Rahmani, Theo Kanter. Mobile Solutions and Their Usefulness in Everyday Life, 33-45

  Kapitel

  Internet-of-Things (IoT) promises to impact every aspect of our daily life by connecting and automating everyday objects which bring the notion of SmartLiving. While it is certain that the trend will grow at a rapid speed, at the same time, challenge to alleviate intelligence of things by reaping value from the data requires to be addressed. The intelligence further cannot depend only on the existing cloud-based solutions which edge computing is expected to mitigate by integrating distributed intelligence. An IoT application necessitates applying knowledge with low latency. However, to comply with the vision of autonomic IoT and real-time intelligence, extracting and applying knowledge are necessitated for which this chapter proposes to exploit mobile edge computing (MEC) to further assist distributed intelligence. Therefore, the problem that this chapter addresses is feasibility investigation of MEC to provide intelligence by reasoning contextualised data and, thereby, the role of MEC in distributed intelligence.

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• Towards Security on Internet of Things

  2018. Kazi Masum Sadique, Rahim Rahmani, Paul Johannesson. Procedia Computer Science 141, 199-206

  Artikel

  The Internet of Things (IoT) paradigm refers to the network of physical objects or "things" embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT data collected from different sensors, nodes and collectors are transferred to the cloud over the internet. IoT devices are used by consumers, healthcare, businesses as well as by the governments. It is being forecast that 31 billion IoT devices will be deployed all over the world by the year 2020. As the use of IoT devices is increasing every moment several IoT vulnerabilities are introduced. The results and analysis indicate that massive deployment of IoT with an integration of new technologies are introducing new security challenges in IoT paradigm. In this paper, IoT security challenges and open issues are discussed which provides a ground for future research.

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• A Cost Efficient Design of a Multi-Sink Multi-Controller WSN in a Smart Factory

  2017. Hamid Reza Faragardi (et al.). 2017 IEEE 19th Intl Conference on High Performance Computing and Communications HPCC 2017, 594-602

  Konferens

  Internet of Things (IoT), one of the key elements of a smart factory, is dubbed as Industrial IoT (IIoT). Software defined networking is a technique that benefits network management in IIoT applications by providing network reconfigurability. In this way, controllers are integrated within the network to advertise routing rules dynamically based on network and link changes. We consider controllers within Wireless Sensor Networks (WSNs) for IIoT applications in such a way to provide reliability and timeliness. Network reliability is addressed for the case of node failure by considering multiple sinks and multiple controllers. Real-time requirements are implicitly applied by limiting the number of hops (maximum path-length) between sensors and sinks/controllers, and by confining the maximum workload on each sink/controller. Deployment planning of sinks should ensure that when a sink or controller fails, the network is still connected. In this paper, we target the challenge of placement of multiple sinks and controllers, while ensuring that each sensor node is covered by multiple sinks (k sinks) and multiple controllers (k′ controllers). We evaluate the proposed algorithm using the benchmark GRASP-MSP through extensive experiments, and show that our approach outperforms the benchmark by lowering the total deployment cost by up to 24%. The reduction of the total deployment cost is fulfilled not only as the result of decreasing the number of required sinks and controllers but also selecting cost-effective sinks/controllers among all candidate sinks/controllers.

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• A Mechanism for Mitigating DoS attack in Information-Centric Networks

  2017. Haoyue Xue (et al.). Proceedings of the 1st International Conference on Internet of Things and Machine Learning

  Konferens

  Information-Centric Networking (ICN) ¹ is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

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• A deep relation learning method for IoT interoperability enhancement within semantic formalization framework

  2017. Bin Xiao, Rahim Rahmani. Proceedings of the Second International Conference on Internet of things and Cloud Computing

  Konferens

  Internet of Things (IoT) is facing with the interoperability issue due to the massive amount of heterogeneous entities (both physical and virtual entities) constantly generating heterogeneous data objects; semantic formalization has been widely recognized as a basis for the IoT interoperability, by which IoT can acquire the ability to comprehend data and further recognize the logic relations among heterogeneous IoT entities and heterogeneous data objects, thus to establish mutual understanding between each other to support with interoperability. Even semantic-driven track has emphasizes a lot on the logic relations in connection to the service rules and policies for interoperability, it is important that the quantity-driven relations should be also explored with adhering to the framework of semantic formalization. This paper explores a Deep Recursive Auto-encoders formed data relation learner in line with the semantic framework, which supports the data interoperability enhancement in a quantity-driven way based on the logic-driven framework. The learner starts with representing the virtual IoT entities via feature extraction; based on that, learner is trained in a manner of considering the surrounding relations of the targeted entity. As a baseline, a contrast learner with "regular" structure has been proposed which cannot functionally support semantic framework, even though the semantic formalization is indispensable; regardless the limitations in lab environment, the conducted experiments show that the proposed learner performs a bit better than the contrast learner under the same conditions. So that, the proposed method can synergistically enhances the interoperability within a semantic formalization framework.

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• Autonomous Cooperative Decision-Making in Massively Distributed IoT via Heterogenous Networks

  2017. Rahim Rahmani, Theo Kanter. Proceedings of the 1st International Conference on Internet of Things and Machine Learning

  Konferens

  This paper presents a disruptive approach "Immersive Networking" enabling massively distributed IoT nodes to participate in autonomous and cooperative decision-making. The approach is mandated by perceived limitations in 5G networking architecture maintaining control in the edge gateway. In our approach, control may be delegated to clusters of IoT nodes beyond the edge gateway. The communication is event-based involving publish-subscribe between related nodes. Clusters are identified in an autonomic fashion based on multi-criteria proximity. Local decisions can combine global and local context information to establish network slices in a decentralized fashion based on application demands. Moreover, such decisions may be part of a collaborative effort (map-reduce) based on either local or global context. Application demands expressed as such are modeled compatible with Open Data initiatives. We demonstrated feasibility of the approach and evaluate its advantages over the 5G architecture involving an edge gateway.

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• Edge-based interoperable service-driven information distribution for intelligent pervasive services

  2017. Bin Xiao (et al.). Pervasive and Mobile Computing 40, 359-381

  Artikel

  Internet of Things (IoT)-based Intelligent Pervasive Service (IPS) systems face increasing pressure from the massive amounts of heterogeneous data generated; the heterogeneity hinders interoperability between data resources and IPSs, making data sharing inefficient and making it difficult to satisfy the needs and fulfill requirements of the IPSs. In response, this article proposes a method of interoperable service-driven information distribution on the edge side to enhance the service-level interoperability with feature-level interoperability by self-adapting data sharing according to the service needs, which will also help to release incremental data pressure and provide better data privacy by conducting service-driven and relevance-based data sharing.

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• Immersive IoT in Smart Cities

  2017. Theo Kanter, Rahim Rahmani. ICIST 2017, 44-49

  Konferens

  A rapidly increasing and vast number of “things” will be connected to the Internet, in all sectors of society (people, places, sensors, homes, industry, government services, etc.). The urgency of finding sustainable solutions requires “things” and services of the overall system to display autonomic intelligent behavior. The ability of cloud infrastructure to orchestrate the fine-grained and agile control of “things” is limited. This mandates an alternative approach intelligently moving control to the “things”. Thereby we minimize the reliance on cloud infrastructure, and are able to build more agile, intelligent and effective solutions in various application areas, such as Health, Transport and through Automation. We provide examples of such novel solutions tested in “Urban ICT Arena”, a largescale smart city testbed in Stockholm.

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• Mobile Blue Gateway - an Application for Multicasting Data to Populations of Bluetooth Low Energy Sensors for Smart Community Sensing

  2017. Rahim Rahmani (et al.). Proceedings of the Second International Conference on Internet of things and Cloud Computing

  Konferens

  Information distribution and management of large Bluetooth Low Energy Wireless Sensor Networks today, is a time-consuming and tedious task for the user. With the current tools available for public use, sensors have to be updated in a one-by-one basis. Remotely sensors updating through for example remote controls or gateways is not possible. The remote sensors updating concept is becoming more and more realizable due to recent advancements in Internet-enabled technologies. However, this necessitates a blue gateway system that provides services to respond to the remotely sensors updating to enable such Mobile Blue Gateway automation at the edge of today’s networks for smart community sensing. To this end, this paper designs and develops a Mobile Blue Gateway which provides a novel way to communicate and distribute information updating through the use of the workload between multiple gateways. It enables the user to remotely update sensors of multiple gateways simultaneously.

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• Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT

  2017. Hasibur Rahman, Rahim Rahmani, Theo Kanter. Procedia Computer Science 109, 335-342

  Artikel

  Future Internet is expected to be driven by prevalence of the Internet of Things (IoT). This prevalence of IoT promises to impact every aspect of human life in the foreseeable future where computing paradigm would witness huge influx of IoT data. Context is gaining growing attention to make sense of the data and it is envisaged that context-aware computing would act as an indispensable enabler for IoT. Contextualizing the collected IoT data enables to reap value from the data and to harvest the knowledge. Reasoning the contextualized data, that is, context information is imperative to the vision of harvesting knowledge. Edge computing is also expected to play a vital role in IoT to reduce dependency on cloud based solution, to achieve faster response, and to provide intelligence closer to the IoT things. The combination of context-awareness and edge solution would be inseparable in the future IoT. Furthermore, IoT vision comprises of different IoT applications controlled by a capable controller at the edge, an edge controller necessitates to counter the challenge of providing knowledge for each of the IoT applications. Therefore, such a controller requires to offer different context-aware reasoning to alleviate the intelligence-of-things. In view of this, this paper proposes a multi-modal context-aware reasoner the aim of which is to provide knowledge at the edge for each IoT application. The context-aware reasoning has been verified with rules-based and Bayesian reasoning for three IoT applications and initial results suggest that it is promising to realize such multimodal reasoning at the edge with low latency.

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• A SDN-based architecture for horizontal Internet of Things services

  2016. Yuhong Li (et al.). IEEE International Conference on Communications (ICC 2016)

  Konferens

  The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

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• Comparing properties of massively multiplayer online worlds and the Internet of Things

  2016. Kim Nevelsteen, Theo Kanter, Rahim Rahmani. 2016 IEEE Symposium on Computers and Communication (ISCC), 1-5

  Konferens

  A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, it is discussed how research from one domain can possibly be exapted to the other domain and vice versa. A case study of a MMOW interfacing with IoT is presented in closing.

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• Entity Configuration and Context-Aware reasoNer (CAN) towards Enabling an Internet of Things Controller

  2016. Hasibur Rahman, Rahim Rahmani, Theo Kanter. Intelligent Systems and Applications, 237-258

  Kapitel

  The Internet of Things (IoT) paradigm has so far been investigating into designing and developing protocols and architectures to provide connectivity anytime and anywhere for anything. IoT is currently fast forwarding towards embracing a paradigm shift namely Internet of Everything (IoE) where making intelligent decisions and providing services remains a challenge. Context plays an integral role in reasoning the collected data and to provide context-aware services and is gaining growing attention in the IoT paradigm. To this end, a Context-Aware reasoNer (CAN) has been proposed and designed in this chapter. The proposed CAN is a generic enabler and is designed to provide services based on context reasoning. Discovering and filtering entities, i.e. entity configuration, become pivotal in analysing context reasoning to provide right services to right context entities in right time. This chapter leverages the concept of entity configuration and CAN towards enabling an IoT controller. The chapter further demonstrates use cases and future research directions towards generic CAN development and facilitating context-aware services to IoE.

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• Gap-based Caching for ICN-based Vehicular Networks

  2016. Yuhong Li (et al.).

  Konferens

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• Immersive Networking — A Framework for Virtual Environments with Augmented Reality in Human Decision-Making

  2016. Theo Kanter, Uno Fors, Rahim Rahmani. International Journal of Multimedia and Ubiquitous Engineering 11 (6), 43-60

  Artikel

  In this publication we present Immersive Networking as a novel framework for connecting people and places and things in virtual environments with augmented reality to be used in eg. Virtual training environments. The research is mandated by technology advances in internetworking underpinned by 5G networks and Internet-of-Things. These advances present new possibilities and challenges to integrate people, places and things in virtual environments. Existing frameworks such as MPEG-V possess representational capabilities but have insufficient support for integrating entities from the real world via heterogeneous infrastructure. MPEG-V for instance makes no statements about distributed control. Seamless experiences in virtual environment require self-organization of connectivity between people, places and things via heterogeneous 5G and Internet-of-Things infrastructures. A second important aspect of the quality of our experience is the immediacy of responses. Both aspects of seamless and self-organizing connections between entities require that we push control to the end-devices co-located with the entities themselves. These end-devices may incorporate sensing gateways and interaction devices, which include both local and non-local information from the virtual environment in the interaction. Thus delegation of control to end-devices requires means for the organizing or relations and clustering by relevance. This capability is particularly important as the projected number of devices and sensors to be connected via the Internet-of-Things is projected to be in the order of 50 billion by 2020. Immersive Networking supported by MediaSense constitutes a scalable self-organizing means for connecting people and places and things in virtual environments with augmented reality. MediaSense moves control to the edge enabling immediacy in experiences based on seamless self-organization through clustering of entities in relations organized by relevance. We conclude by validating our approach in several scenarios evaluating the relevance and application in human decision-making..

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• Improving Distributed Forensics and Incident Response in Loosely Controlled Networked Environments

  2016. Irvin Homem, Theo Kanter, Rahim Rahmani. International Journal of Security and Its Applications 10 (1), 385-414

  Artikel

  Mobile devices and virtualized appliances in the Internet of Things can be end nodes on varying networks owned by different parties over time, while still seamlessly participating in licit or illicit activities. Digital Forensics and Incident Response (DFIR) tools today struggle to perform digital investigations in such loosely controlled networked environments as they face several challenges including: scarcity of resources, availability, trust, privacy, data volumes, velocity and variety. In this paper we analyze the state of research in DFIR in networked environments, identifying the challenges facing DFIR tools particularly in loosely controlled network environments. We present the requirements for a system to address these challenges at the various steps of the typical digital investigation methodology. From this we identify the need for support from Peer to Peer (P2P) overlays and discuss their relative merits and drawbacks in order to identify those that would best support DFIR in loosely controlled networked environments. Finally we incorporate both structured and unstructured P2P overlays in various capacities in our architecture in order to organize devices in loosely controlled networks, using context information, thus enabling efficient capture, analysis and reporting of artifacts of use in digital investigations.

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• Intelligent Data-Intensive IoT: A Survey

  2016. Bin Xiao (et al.). 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2362-2368

  Konferens

  The IoT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or IoT, the data-intensive feature of IoT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence，intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle IoT data and different intelligence enablers for IoT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive IoT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive IoT to tackle the challenges.

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• Logical interactions for heterogeneous IoT entities via virtual world mirrors in support of Ambient Assisted Living

  2016. Bin Xiao, Theo Kanter, Rahim Rahmani. Journal of Ambient Intelligence and Smart Environments 8 (5), 565-580

  Artikel

  In Ambient Assisted Living (AAL), as an important applied area of Internet of Things (IoT) technology, logical entity interaction establishes an intelligent virtual world in which heterogeneous entities are emulated by object-oriented mirrors through tightly cooperating sensors and actuators from different spaces in the real world; this enables the system to generate intelligent services to support human behaviour. However, until now, few researchers have focused on enabling interactions between heterogeneous IoT entities for AAL services. Further, in relevant studies regarding semantic entity interactions, researchers have mainly focused on enabling semantic interactions between sensors (rather than integral entities) for the purpose of coordinating and managing data resources, where tight collaborations between sensors and actuators are not emphasised. Hence, the system capability of initialising intelligent service to enhance human experience has not been properly emphasised, to date. This paper discusses enabling logical object-oriented IoT entity interactions to enrich AAL services for humans through the creation of mirrors in the virtual world emulating the attributes and behaviours of heterogeneous entities via cooperating sensors and actuators in the real world. This paper introduces the Entity Device Collocating (EDC) Platform to globally locate and retrieve sensors and actuators respectively adhering to the attributes and behaviours of integral mirrors in the virtual world; this is intended to enable the interoperability between virtual and real worlds. Interactions are created upon entity mirrors mapping entities from the physical world to the virtual world, during which interactions continue to evolve based on the service logics. The proposed technique is applied to a typical AAL case of smart pill-ingestion, where a demo is implemented for verification. Comparing with relevant research, the proposed technique contributes by introducing a flexible and scalable IoT entity interaction method targeting AAL to address human needs.

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• Reasoning Service enabling SmartHome Automation at the Edge of Context Networks

  2016. Hasibur Rahman (et al.). New Advances in Information Systems and Technologies, 777-786

  Konferens

  The popular concept of SmartHome means that the appliances such as lighting, heating and door locks are controllable remotely through for example remote controls or mobile phones. The concept is becoming more and more realizable due to recent advancements in Internet-enabled technologies. SmartHomes can become even more intelligent and automated by exploiting such intelligent and affordable Internet-enabled technologies. However, this necessitates a context-aware system that provides services to respond to the context changes to enable such SmartHome automation at the edge of today’s context-centric networks. To this end, this paper designs and develops a context-aware reasoning service for home automation which provides a novel way to connect SmartHomes through the use of a distributed context exchange network overlay. It enables mobility service application to communicate with and control SmartHomes remotely.

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• A Relational Context Proximity Query Language

  2015. Jamie Walters, Theo Kanter, Rahim Rahmani. Mobile Networks and Management, 277-289

  Konferens

  The creation of applications and services realising massive immersive participation require the provisioning of current, relevant and accurate context information. These applications benefit from access to this highly dynamic information in real time. Existing approaches to provisioning context information are limited by their interpretation of context relationships as address book solutions thus limiting the discovering of related entities. We introduce the context proximity query language (CPQL) for querying context related entities on distributed across collections of remote endpoints. As a declarative query language (CPQL) is similar in structure to SQL and describes the relationships between entities as distance functions between their associated context information. We simulate CPQL and show that it offers improvements over existing approaches while scaling well.

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• An Ontology-based Problem-logic Driven Approach towards the Activity Awareness for Elderly Care

  2015. Bin Xiao, Theo Kanter, Rahim Rahmani. International Journal of Multimedia and Ubiquitous Engineering 10 (4), 31-42

  Artikel

  The same activity under different user situations in the elderly care system may lead to different intelligent system response, since user needs vary with the changing situations. Activity awareness (AA) emphasizes that systems intelligently respond to user needs by aware the user-performed activities which is denoted and comprehended according to various user situations. But most current AA technics use similar patterns retrieved from the growing historical data, neglecting the situation change, called dataset driven. Thus, an aptly approach is needed in support of AA systems to handle various activity denotations in different user situations. Responding to the problem, this paper proposes an Ontology-based Problem-logic driven approach (OPL) to enhance the AA system by denoting user activities with the problem logic according to user-oriented denoted problem domains, where the AA system can seamlessly integrate with inferring for intelligent system response. Specifically, denoted problem domain and logic are proposed to support the OPL concepts, while activity graph is formed to support the intelligent system response based on the annotated problem logic. With the OPL, systems can directly target at the changing situations with rule-based inferring. A case study is performed upon the scenario retrieved from a European elderly care project, where a proof-of-concept prototype is established to confirm the validity of the OPL approach.

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• Constructing Context-centric Data Objects to Enhance Logical Associations for IoT Entities

  2015. Bin Xiao, Theo Kanter, Rahim Rahmani. Procedia Computer Science 52, 1095-1100

  Artikel

  Entities in Internet of Things (IoT) need intelligent associations to allow a flexible and dynamic system reaction towards varying user situations. Purely data-oriented association methods (e.g., data-mining, machine-learning, etc.) are limited in deduction from existing associations. Also such data-oriented methods are limited in accuracy for working with small-scale datasets (e.g., working with patterns retrieved from historical data), outputting associations based on statistics rather than logic. Moreover, existing semantic technologies (ontological-oriented or rule-oriented) are facing with either flexibility or dynamicity challenges to discover and maintain the associations. This paper proposes an alternative technique of semantically constructing context-centric data objects based on service logics for logical associations, which enables an event net based on association nets adapting for the changing situations (called context-centric). A proof-of-concept implementation is carried out based on a vehicle planning scenario to validate the data construction technique. Comparing to previous work, this technique possesses advantages of flexibility and dynamicity for entity associations based on service logics.

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• Differentiated Context Maintenance and Exchange oriented to Internet of Things

  2015. Yuhong Li, Theo Kanter, Rahim Rahmani. International Journal of Computer Science Issues 12

  Artikel

  Context information can add more meaning and value to the sensor data in Internet of Things. However, due to the large amount of sources of contexts and sensor data, the exchange of contexts through the networks may cause more and more data traffic. This paper proposes a novel method for maintaining and exchanging contexts through the Internet more efficiently. Contexts are classified into different levels. Each level of contexts is maintained and exchanged using different methods. A new protocol for exchanging the contexts is designed. Through the independent transmitting of contexts and sensor data, the context-awareness in Internet of Things can be realized, and the network bandwidth usage can be greatly reduced and thus the energy of node can also be saved.

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• Enabling Distributed Context Entity Discovery for an Internet-of-Things Platform

  2015. Hasibur Rahman, Theo Kanter, Rahim Rahmani. Proceedings of 2015 SAI Intelligent Systems Conference (IntelliSys)

  Konferens

  Use of context has been prevailing in distributed computing with the emergence of mobile computing. An entity is said to be context-aware if it responds to context changes. These distributed context-aware entities share context information in order to make intelligent decisions or carry out important tasks. MediaSense, an Internet-of-Things (IoT) platform, offers such intelligent delivery of context information to any host- anywhere. However, such an IoT platform is facing challenge of discovering context entities. In view of this, this paper particularly addresses the challenge of discovering distributed context entities by extending the distributed protocol, Distributed Context eXchange Protocol (DCXP), for MediaSense platform. In particular, a publish/subscribe approach has been employed to overcome the challenge which enables fast context entity discovery.

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• Layering the Internet-of-Things with Multicasting in Flow-sensors for Internet-of-services

  2015. Rahim Rahmani, Theo Kanter. International Journal of Multimedia and Ubiquitous Engineering 10 (12), 37-52

  Artikel

  Development of Internet-of-Services will be hampered by heterogeneous Internet-of-Things infrastructures, such as inconsistency in communicating with participating objects, connectivity between them, topology definition & data transfer, access via cloud computing for data storage etc. Our proposed solutions are applicable to a random topology scenario that allow establishing of multi-operational sensor networks out of single networks and/or single service networks with the participation of multiple networks; thus allowing virtual links to be created and resources to be shared. The designed layers are context-aware, application-oriented, and capable of representing physical objects to a management system, along with discovery of services. The reliability issue is addressed by deploying IETF supported IEEE 802.15.4 network model for low-rate wireless personal networks. Flow- sensor succeeded better results in comparison to the typical - sensor from reachability, throughput, energy consumption and diversity gain viewpoint and through allowing the multicast groups into maximum number, performances can be improved.

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• Speech emotion recognition in emotional feedbackfor Human-Robot Interaction

  2015. Javier G. Rázuri (et al.). International Journal of Advanced Research in Artificial Intelligence (IJARAI) 4 (2), 20-27

  Artikel

  For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such properties of sound to recognize emotions. This work evaluated six different kinds of classifiers to predict six basic universal emotions from non-verbal features of human speech. The classification techniques used information from six audio files extracted from the eNTERFACE05 audio-visual emotion database. The information gain from a decision tree was also used in order to choose the most significant speech features, from a set of acoustic features commonly extracted in emotion analysis. The classifiers were evaluated with the proposed features and the features selected by the decision tree. With this feature selection could be observed that each one of compared classifiers increased the global accuracy and the recall. The best performance was obtained with Support Vector Machine and bayesNet.

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• Supporting Self-Organization with Logical-clustering Towards Autonomic Management of Internet-of-Things

  2015. Hasibur Rahman, Theo Kanter, Rahim Rahmani. International Journal of Advanced Computer Sciences and Applications 6 (2), 24-33

  Artikel

  One of the challenges for Autonomic Management in Future Internet is to bring about self-organization in a rapidly changing environment and enable participating nodes to be aware and respond to changes. The massive number of participating nodes in Internet-of-Things calls for a new approach in regard of Autonomic Management with dynamic self-organization and enabling awareness to context information changes in the nodes themselves. To this end, we present new algorithms to enable self-organization with logical-clustering, the goal of which is to ensure that logical-clustering evolves correctly in the dynamic environment. The focus of these algorithms is to structure logical-clustering topology in an organized way with minimal intervention from outside sources. The correctness of the proposed algorithm is demonstrated on a scalable IoT platform, MediaSense. Our algorithms sanction 10 nodes to organize themselves per second and high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.

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• Adaptive Information Provisioning in Distributed Context Centric Architectures

  2014. Jamie Walters, Theo Kanter, Rahim Rahmani. International Journal of Computer Science Issues 11 (4), 10-21

  Artikel

  The provisioning of user context information between service endpoints is central to realizing massive immersive participation on an Internet of Things. This information must in turn be provisioned to endpoints with minimal overhead costs. Where this is achieved through centralized repositories of context information there arises issues of scalability and availability. Where distributed approaches have been proposed, information dissemination has been optimized relative to the underlying network properties. In this paper we extend the Distributed Context Protocol (DCXP) to support subscriptions relative to an entity-application-entity triple, minimizing the number of subscriptions required and through application specific optimization minimize the overall cost of delivering user context information to service endpoints.

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• An Information-Centric Approach for Data Dissemination in Vehicular Networks

  2014. Yuhong Li, Theo Kanter, Rahim Rahmani. 2014 International Conference on Connected Vehicles & Expo (ICCVE 2014), 888-893

  Konferens

  The features of information dissemination in vehicular networks make it necessary to introduce Information-Centric Networking (ICN) technique to vehicular networks. However, some design principles of ICN must be implemented and extended in order to realize efficient data dissemination in vehicular networks. This paper proposes an approach for realizing ICN-based data dissemination in vehicular networks. An architecture for organizing and disseminating information in a vehicle is suggested. The concept of cluster of common interest is proposed, with which groups of vehicles can be established dynamically according to the information they are interested in. Within each cluster, information are disseminated to the vehicles in a way decided by the features of the information. To show the feasibility of our approach, the approach has been implemented and tested in the simulation environment.

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• Design of High Accuracy Tracking Systems with H Infinite Preview Control

  2014. Antonio Moran Cardenas (et al.). POLIBITS Research Journal on Computer Science and Computer Engineering With Applications 50, 21-28

  Artikel

  Positioning and tracking control systems are an important component of autonomous robot applications. This paper presents the design method of tracking control systems based on H infinite preview control where the present and future desired positions of the robot are used to determine the control actions to be applied so that the robot describes the desired trajectory as close as possible. The performance improvements achieved with H infinite preview control have been examined in the frequency and time domains for different types of reference signals when applied to a one-dimensional positioning system. It was found that preview control improves the tracking performance by improving the phase response of the tracking system.

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• Distributed Immersive Participation as Crowd-Sensing in Culture Events

  2014. Theo Kanter (et al.). Journal of Virtual Worlds Research 7 (2)

  Artikel

  This article investigates new forms for creating and enabling massive and scalable participatory immersive experiences in live cultural events, characterized by processes, involving pervasive objects, places and people. The multi-disciplinary research outlines a new paradigm for collaborative creation and participation towards technological and social innovation, tapping into crowd-sensing. The approach promotes user-driven content-creation and offsets economic models thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet-of-Things infrastructure to connect artifacts, performers, participants and places. Equally importantly, we enable the in-situ creation of collaborative experiences building on relevant existing and stored content, based on decisions leveraging multi-criteria clustering and proximity of pervasive information, objects, people and places. Finally, we investigate some new ways for immersive experiences via distributed computing but pointing forward to the necessity to do more with regard to collaborative creation.

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• Effect of emotional feedback in a decision-making system for an autonomous agent

  2014. Javier Guerrero Rázuri (et al.). Advances in Artificial Intelligence - IBERAMIA 2014, 613-624

  Konferens

  The point of view of Isaac Asimov is unlikely in a close future, but machines that develop tasks in a sensible manner are already a fact. In light of this remark, recent research tries to understand the requirements and design options that imply providing an autonomous agent with means for detecting emotions. If we think about of exporting this model to machines, it is possible that they become capable to evolve emotionally according to such models and would take part in the society more or less cooperatively, according to the perceived emotional state. The main purpose of this research is the implementation of a decision model affected by emotional feedback in a cognitive robotic assistant that can capture information about the world around it. The robot will use multi-modal communication to assist the societal participation of persons deprived of conventional modes of communication. The aim is a machine that can predict what the user will do next and be ready to give the best possible assistance, taking in account the emotional factor. The results indicate the benefits and importance of emotional feedback in the closed loop human-robot interaction framework. Cognitive agents are shown to be capable of adapting to emotional information from humans.

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• Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense

  2014. Hasibur Rahman, Rahim Rahmani, Theo Kanter. Proceedings of 2014 Science and Information Conference, 64-71

  Konferens

  Crowdsourcing was initially devised as a method for solving problems through soliciting contributions from a large online community. Crowdsourcing is facing new challenges to handle the increase of information in real-time from a vast number of sources in Internet-of-Things (IoT) scenarios. Thus we seek to leverage the power of social web, smart-devices, sensors, etc., fusing these heterogeneous sources into distributed context information in order to enable novel crowdsourcing scenarios. This mandates research in efficient management of heterogeneous and distributed context information through logical-clustering. Logical-clustering can efficiently filter out similar context information obtained from distributed sources based on context similarity. However, the efficiency of logical-clustering is challenged by the distribution of context information in crowdsourcing scenarios. Publish/Subscribe mechanism can counter this challenge. To this end, we propose a scalable publish/subscribe model, MediaSense, which is based on p2p technologies. This paper presents our approach to a scalable logical-clustering concept. The evaluation of our approach applied to MediaSense can achieve a rate of approximately 3530 messages/sec for publish/subscribe events. Moreover, this approach further achieves 99% increase for subscription matching and 163% improvement in memory requirements in comparison with other approaches.

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• Generic Distributed Sensing in Support of Context Awareness in Ambient Assisted Living

  2014. Bin Xiao, Theo Kanter, Rahim Rahmani. Multimedia and Ubiquitous Engineering, 99-107

  Konferens

  Researches in ambient assisted living have so far faced three important challenges: (1) Lack of a comprehensive approach to capture user needs that are generic; i.e., not limited to specific events, but as generic related to the user. (2) Lack of a highly flexible and scalable platform for the distributed sharing and processing of context between nodes in IoT networks. (3) Increased amount of communication and devices with sensors participating in the acquisition, processing and sharing of context further challenges both computation capability and storage capacity of the system. In this paper, we address these limitations and present novel support, applied in a system for remote assistance of elderly. The support comprehensively retrieves user needs from generic context, via a scalable overlay providing increment of processing capability and storage. Further, the support self-organizes entities into generic context from distributed sensing, using the Dependent Context Pattern (DCP) based on the Context Virtualizing Platform (CVP).

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• Integration of Artificial Neural Networks and Linear Systems for the Output Feedback Control of Nonlinear Vibration Systems

  2014. Javier Francisco Guerrero Rázuri (et al.). CCC 2014, the 33rd Chinese Control Conference, 1850-1855

  Konferens

  This paper analyzes the integration of neural networks and linear systems for the identification, state estimation and output feedback control of weakly nonlinear systems. Considering previous knowledge about the system given by approximated linear state-space models, linear observers and linear controllers, training algorithms for the neuro-identification, state neuro-estimation and output feedback neuro-control were derived considering the dynamics of the nonlinear system. It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model. It was also found that the state estimation and vibration isolation performance of the system with integrated linear-neuro output feedback control is better than the system with linear control or neuro-control.

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• Mobile Backhaul Network Convergence

  2014. Syed Fashahid, Rahim Rahmani. Proceedings of the 2014 International Conference on Computer Network and Information Science, 66-71

  Konferens

  This research investigates a number of selected mobile backhaul network (MBHN) deployments in a simulated environment and evaluates the feasibility of each deployment in terms of network failure, convergence and resilience. Four different scenarios from target network have been considered which includes single path route scenario, multi path route scenario, access bus network scenario and MPLS scenario, where three different use cases are experimented in each scenario. Research concludes that if backhaul network failure detection time is reduced, it help to reduce the overall network convergence time. Result shows that bidirectional forwarding detection (BFD) protocol works well to detect the failure in a network. The outcome and measurements from the simulated networks can be used as a guideline for configuration of real networks.

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• Comparing Properties of Massively Multiplayer Online Worlds and the Internet of Things

  Kim J. L. Nevelsteen, Theo Kanter, Rahim Rahmani.

  Artikel

  With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, the problem of scaling IoT is explicated, as well as the problem of incorporating an MMOW with IoT into a pervasive platform. Three case studies of a MMOW interfacing with IoT are presented in closing.

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• Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing

  2014. Hasibur Rahman, Rahim Rahmani, Theo Kanter. International Journal of Advanced Research in Artificial Intelligence 3 (11)

  Artikel

  The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.

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• Vehicular Network Enabling Large-Scale and Real-Time Immersive Participation

  2014. Theo Kanter (et al.). Internet of Vehicles – Technologies and Services, 66-75

  Konferens

  This paper presents a system and mechanisms enabling real-time awareness and interaction among vehicles connected via heterogeneous mobile networks. Information obtained by vehicles is considered as the centre in our system. Vehicles are organized dynamically in overlaid clusters. In each cluster, vehicle-related information is pushed in time. As a network node, each vehicle has the function of content abstraction and distribution. Through processing and abstracting the sensed data, various vehicle-related information are organized and denoted in hierarchical names at each node. The data are transmitted and forwarded using protocols accordant with the characteristics of the content. In this way, large-scale and real-time information exchanges among vehicles are realized. Part of our system has been implemented and tested. An open source platform providing standard sensor and actuator API can be provided.

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• Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network

  2013. Javier G. Rázuri (et al.). 2013 12th Mexican International Conference on Artificial Intelligence (MICAI), 85-96

  Konferens

  This paper focuses on a system of recognizing human’s emotion from a detected human’s face. The analyzed information is conveyed by the regions of the eye and the mouth into a merged new image in various facial expressions pertaining to six universal basic facial emotions. The output information obtained could be fed as an input to a machine capable to interact with social skills, in the context of building socially intelligent systems. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human’s expression and that are particularly relevant for the decoding of emotional expressions. Finally we use the merged image as an input to a feed-forward neural network trained by back-propagation. Such analysis of merged images makes it possible, obtain relevant information through the combination of proper data in the same image and reduce the training set time while preserved classification rate. It is shown by experimental results that the proposed algorithm can detect emotion with good accuracy.

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• Autonomous Motion of Mobile Robot Using Fuzzy-Neural Networks

  2013. Antonio Moran Cardenas (et al.). 2013 12th Mexican International Conference on Artificial Intelligence, 80-84

  Konferens

  This paper analyzes the performance and practical implementation of fuzzy-neural networks for the autonomous motion of mobile robots. The designed fuzzy-neural controller is a refined version of a conventional fuzzy controller, and was trained to optimize a given cost function minimizing positioning error. It was found that the mobile robot with fuzzy-neural controller presents good positioning and tracking performance for different types of desired trajectories. It was verified by computer simulation as well as experimentally using a laboratory-scale car-like robot model.

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• Conceptual Framework for Internet of Things’ Virtualization via OpenFlow in Context-aware Networks

  2013. Theo Kanter, Rahim Rahmani, Arif Mahmud. International Journal of Computer Science Issues 10 (6), 16-27

  Artikel

  A novel conceptual framework is presented in this paper with an aim to standardize and virtualize Internet of Things’ (IoT) infrastructure through deploying OpenFlow technology. The framework can receive e-services based on context information leaving the current infrastructure unchanged. This framework allows the active collaboration of heterogeneous devices and protocols. Moreover it is capable to model placement of physical objects, manage the system and to collect information for services deployed on an IoT infrastructure. Our proposed IoT virtualization is applicable to a random topology scenario which makes it possible to 1) share flow-sensors’ resources, 2) establish multi-operational sensor networks, and 3) extend reachability within the framework without establishing any further physical networks. Flow-sensors achieve better results comparable to the typi-cal-sensors with respect to packet generation, reacha-bility, simulation time, throughput, energy consump-tion point of view. Even better results are possible through utilizing multicast groups in large scale net-works.

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• Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs

  2013. Rahim Rahmani, S. M. Hasibur Rahman, Theo Kanter. RNIS: Research Notes in Information and Service Sciences 14, 721-728

  Artikel

  In the state-of-the-art sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. A novel idea of clustering sensors based on context similarity is presented in this paper. The sensors are physically distributed but logically clustered based on similar context. This will enable resources (data, services) to be shared. The network is a two-tier hierarchical distributed hash tables (DHTs) system based on the HyperFlow platform. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.

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• Emulating Trust Zone in Android Emulator with Secure Channeling

  2013. Arun Muthu, Rahim Rahmani, Dinakaran Rajaram. International Journal of Computer Science Issues 10 (5), 40-51

  Artikel

  There is a raise in penetration of smart phone while using enterprise application, as most of them are downloaded from the public market, resulting in challenge for security framework, causing a threat to lose sensitive user data. To prevent this ARM introduces the virtualization technique in hardware level, which prevents processing of trusted application that is completely isolated from general processing. To improvise this, we need to understand ARM Architecture; however it is still black box for users and developers. In this article, we take a deep look at the hardware architecture of the ARM trust zone to study and analyze its implementation and also to create its replica in emulator. Moreover we describe feasibility of various designs, implementation of trust zone feature in android emulator; with sample trusted application called secure channeling and concludes with annotation of suitable design on future enhancement. The security domain for secure processing and utility in emulator is to benefit the user and developer community.

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• Establishing Multi-Criteria Context Relations Supporting Ubiquitous Immersive Participation

  2013. Jamie Walters, Theo Kanter, Rahim Rahmani. International Journal of Ad Hoc, Sensor & Ubiquitous Computing 4 (2), 59-78

  Artikel

  Immersive Participationentails massive participatory activities in the Internetengaging people, places and objects. This ispremised on the existence of an Internet of Things infrastructure supporting applications and services with the same richness of experience as the World Wide Web. This in turn presupposes the existence of models for establishing and maintaining context relations. Where these models do exist, they impose a limited interpretation of context relations in the presence of the inherent heterogeneous and dynamic characteristics of the supporting information. In this paper we introduce an approach towards establishing context relations through the use of an improved context relational model permitting a wider, more complete range of application specific scenarios. Additionally, wederive a measure of context proximity that considers the situation, attributes, relations, accuracy and heterogeneity of both the underlying information and the vast array of requirements for metrics supporting application problem domains

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• Network traffic efficiency analysis for logical clustering of flow-sensors

  2013. Rahim Rahmani, Hasibur Rahman, Theo Kanter. International Journal of Advancements in Computing Technology (IJACT) 5 (13), 1-13

  Artikel

  In the current era, access to information has become ubiquitous for anything and anyone, and the trend will continue in the foreseeable future as well. Sensor networks have been an integral part of pervasive computing and are expected to play a pivotal role in the future Networked Society. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. The novel approach of logical clustering is beneficial for heterogeneous interoperability of physical objects, thereby, heterogeneous contexts. The idea enables resources (data, services) to be shared among physically distributed objects. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach. Computational efficiency plays a significant role so that network traffic does not encounter abrupt and frequent fluctuations. In this paper, further computational efficiency analysis in terms of network traffic for logical clustering is highlighted.

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• On Performance of Logical-Clustering of Flow-Sensors

  2013. Rahim Rahmani, Hasibur Rahman, Theo Kanter. International Journal of Computer Science Issues 10 (5), 1-13

  Artikel

  In state-of-the-art Pervasive Computing, it is envisioned that unlimited access to information will be facilitated for anyone and anything. Wireless sensor networks will play a pivotal role in the stated vision. This reflects the phenomena where any situation can be sensed and analyzed anywhere. It makes heterogeneous context ubiquitous. Clustering context is one of the techniques to manage ubiquitous context information efficiently to maximize its potential. Logical-clustering is useful to share real-time context where sensors are physically distributed but logically clustered. This paper investigates the network performance of logical-clustering based on ns-3 simulations. In particular reliability, scalability, and reachability in terms of delay, jitter, and packet loss for the logically clustered network have been investigated. The performance study shows that jitter demonstrates 40 % and 44 % fluctuation for 200 % increase in the node per cluster and 100 % increase in the cluster size respectively. Packet loss exhibits only 18 % increase for 83 % increase in the packet flow-rate.

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• Supporting Context-Centric Relationships in Heterogeneous Environments

  2013. Jamie Walters, Theo Kanter, Rahim Rahmani. International Journal of Computer Science Issues 10 (5), 25-34

  Artikel

  Massive Immersive Participation is enriched through the use of context information describing the dynamic states and relations among people places and things. This in turn mandates the creation of methods and models for establishing and supporting these relationships. Previous approaches are undermined by their limited interpretation of context centric relations and subsequently do not offer support for multi-criteria relationships. In this paper, we extend on our previous work on establishing multi-criteria context relationships, to adding the support required for maintaining these relationships over heterogeneous and dynamic context information. We introduce a query language that supports an extended publish-subscribe approach and define solutions for maintaining, evaluating and adjusting these relationships while minimizing overall costs.

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• Deployment of flow-sensors in Internet of Things’ virtualization via OpenFlow

  2012. A Mahmud, Rahim Rahmani, Theo Kanter. 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, 195-200

  Konferens

  A novel idea presented in this paper is to deploy the Open Flow technology in wireless sensor networks that can lead to a significant achievement in Internet of things and cloud computing arena through network virtualization. Two new abstract layers namely Common platform layer and virtualization layer can be added at the top and bottom of a preset Infrastructure as a Service architecture. Our proposed IOT virtualization can be applicable in a random topology scenario which makes possible of the flow-sensors' resources to be shared, establishment of multi operational sensor networks and escalation of the reach ability under the same platform without establishing any further physical networks. Flow-sensor achieved 39% more reach ability than the typical sensors in an ideal scenario and even better results are possible from the amount of packets generation and simulation time viewpoint for larger scale networks.

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• Flow-sensor Mobility and Multicast Support in Internet of Things’ Virtualization

  2012. Arif Mahmud, Theo Kanter, Rahim Rahmani. 2012 International Conference on ICT Convergence, 16-22

  Konferens

  This paper studied communication between sensor groups-access points and a networking & communication model and packet transmission algorithm such that networks are divided into several multicast domains of stationary flow-sensors and received several packet-flows from mobile flow-sensors. The proposed approach to Internet of Things' virtualization is suitable for invoking and multi-tasking sensor networks within random network scenarios ranging from large single operational networks to distinct service networks within the involvement of multiple operational networks. The result implies larger multicast groups perform better than smaller ones whereas large flows of packets decrease the performances from the whole network viewpoint.

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• Design of active queue management for robust control on access router for heterogeneous networks

  2011. Rahim Rahmani, Christer Åhlund, Theo Kanter. EURASIP Journal on Wireless Communications and Networking

  Artikel

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• A self configuring fuzzy Active Queue Management controller in heterogeneous networks

  2010. Rahim Rahmani, Theo Kanter, C. Åhlund. ICT 2010, 634-641

  Konferens

  To achieve self configuring of Active Queue Management based on Fuzzy Logic Controller (FLC), we propose a Fuzzy Adaptive Active Queue Management Controller (FAAQMC). FAAQMC eliminates buffer overflow by adapting the buffer size to the required queue length with a control cycle time shorter than the mean inter-arrival time of a burst. This makes FAAQMC suitable to manage invariant burstiness or self-similar network traffic.

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Visa alla publikationer av Rahim Rahmani vid Stockholms universitet