Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.