Socially Oriented Edge Computing for Energy Awareness in IoT Architectures

The Internet of Things services provision, by using machine-to-machine (M2M) communication, plays a critical role in today's ubiquitous systems. This service provision offers reliability and consistency to the end user. In many cases, there are secondary devices that are interconnected (glasses, set-top-boxes, home furniture, etc.), playing an active role in the level of QoS/QoE provided to end users. This is valid for any service demands, requested by end users on the move. The most important aspect of this kind of communication is to allow users to exploit continuous on-demand service provision, regardless of how demanding their running applications are. This is feasible to achieve by using schemes to support the edge devices. Considering the latter, this work presents and identifies the different ways to implement the edge computing paradigm by using M2M communications in dense networked systems via social connectivity from two different perspectives: the offered reliability for delay-tolerant (delay-sensitive) services and the energy conservation over reliability provision. Both perspectives introduce significant applications execution optimization when using delay-sensitive data. In such a case, the offloading social-based processing of selected applications to the edge devices, in terms of both time and energy, offers significant lifetime extensibility for each device as indicative results show.