Optimizing IoT Energy Efficiency on Edge (EEE): a Cross-layer Design in a Cognitive Mesh Network

TL;DR

This paper presents a cross-layer optimization approach to enhance energy efficiency in battery-powered IoT devices by leveraging a grid-powered cognitive radio mesh network, with extensive simulations validating its effectiveness.

Contribution

It introduces a novel cross-layer design that shifts energy consumption to a grid-powered network, optimizing IoT device energy efficiency beyond infrastructure-focused solutions.

Findings

The proposed algorithms outperform baseline schemes in simulations.

The optimization effectively prolongs IoT device lifetime.

Network performance improves under various settings.

Abstract

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices' energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.