Reliability and Battery Lifetime Improvement for IoT Networks: Challenges and AI-powered solutions

TL;DR

This paper explores AI-powered distributed learning schemes to enhance the reliability and battery lifetime of IoT networks, addressing energy efficiency and resource management challenges in future wireless systems.

Contribution

It introduces a lightweight, resource-efficient learning scheme for IoT devices and compares its performance with centralized control methods.

Findings

Distributed learning improves IoT communication reliability

Energy efficiency is significantly enhanced by the proposed scheme

Performance surpasses centralized control in resource-constrained scenarios

Abstract

Towards realizing an intelligent networked society, enabling low-cost low-energy connectivity for things, also known as Internet of Things (IoT), is of crucial importance. While the existing wireless access networks require centralized signaling for managing network resources, this approach is of less interest for future generations of wireless networks due to the energy consumption in such signaling and the expected increase in the number of IoT devices. Then, in this work we investigate leveraging machine learning for distributed control of IoT communications. Towards this end, first we investigate low-complex learning schemes which are applicable to resource-constrained IoT communications. Then, we propose a lightweight learning scheme which enables the IoT devices to adapt their communication parameters to the environment. Further, we investigate analytical expressions presenting performance of a centralized control scheme for adapting communication parameters of IoT devices, and compare the results with the results from the proposed distributed learning approach. The simulation results confirm that the reliability and energy efficiency of IoT communications could be significantly improved by leveraging the proposed learning approach.