Edge Computing for Offload-Aware Energy Conservation using M2M Recommendation Mechanisms

In this work the problem of energy conservation in wireless Internet of Things (IoT) devices is being addressed for machine-to-machine (M2M) communication. IoT connected devices (i.e. glasses, set-top-boxes, home appliances etc.) can affect the energy levels of the IoT ecosystem and can play an active role in the level of QoS/QoE provided to the end-users, for any service demands, on-the-move. To this end, this work proposes a novel offloading methodology that hosts a "resource-aware" model, which allows the efficient energy draining apps monitoring that run in an IoT ecosystem. More specifically, this work utilises an Edge-based Computing offload mechanism in M2M communication for resource-aware recommendation. The work assesses the proposed framework in the context of (i) the offered reliability for IoT services by assistive recommendation scheme and (ii) the energy conservation for a number of devices, forming the IoT ecosystem during the offloading process.