Over the past decade, the Internet of Things (IoT) has undergone a paradigm shift away from centralized cloud computing to edge computing. Hundreds of billions of things are estimated to be deployed in the rapidly advancing IoT paradigm, resulting in an enormous amount of data.

Sending all the data to the cloud has recently proven to be a performance bottleneck as it causes many network issues including latency, power consumption, security, and privacy. However, the existing paradigms do not use edge devices for decision making. Distributed intelligence could strengthen the IoT in several ways by distributing decision-making tasks among edge devices within the network, instead of sending all data to a central server.

All computational tasks and data are shared among edge devices. Edge computing offers many advantages including distributed processing, low latency, fault tolerance, better scalability, better security, and data protection. These advantages are helpful for critical applications that require higher reliability, real-time processing, mobility support, and context awareness.

In this study, the application of different types of intelligence (for example rule-based and machine learning) and network challenges for implementing distributed intelligence at the edge of the network are investigated.

This is Ramin Firouzi’s PhD thesis project.
Rahim Rahmani is the supervisor, and Thashmee Karunaratne is the co-supervisor.