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

Abstract

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-theart 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 multi-objective 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.

IoT and machine learning for enabling sustainable development goals

“…However, given the range of SDG-driven HIW applications with differing requirements and challenges, there is no one-solution-fits-all for data management. Instead, a context-aware approach is beneficial in which a joint decision for i) the best connectivity, ii) processing unit (e.g., device, fog, and cloud), iii) the percentage of data to be offloaded is optimised for a sustainable operation that prioritises Frontiers in Communications and Networks frontiersin.org reducing energy consumption, reducing response time, improving, security, and reducing monetary cost depending on the context (Ozturk et al, 2022). • Machine Learning: SDG-driven HIW systems that require automation rely heavily on machine learning (ML) and AI technologies to infer actionable insights from the gathered data.…”

“…Authors in (Liu et al, 2022), for instance, use reinforcement learning to maximise energy efficiency in the user-beamform selection mechanism in non-orthogonal multiple access systems. Similarly, other works propose a reinforcement learning approach for enabling context-aware connectivity with multi-objectives, including energy efficiency (Ozturk et al, 2022). AI, which includes reinforcement learning and deep learning, has witnessed great success in multiple areas, not the least in enabling Intelligent IoT.…”