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

TL;DR

This paper introduces a reinforcement learning-based, context-aware optimization method for IoT networks that jointly selects connectivity and processing units to balance energy, response-time, security, and cost.

Contribution

It presents a novel joint optimization approach using reinforcement learning that considers multiple device constraints simultaneously, outperforming deterministic solutions.

Findings

Achieves significant gains over deterministic solutions.

Balances multiple objectives such as energy, response-time, security, and cost.

Can meet holistic multi-objective criteria where benchmarks may fail.

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-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 multi-objective optimisation 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.