Machine Learning for Relaying Topology: Optimization of IoT Network with Energy Harvesting

TL;DR

This paper proposes a machine learning-based relaying topology algorithm for energy-harvesting IoT networks, optimizing network longevity and throughput in complex, unbalanced energy distribution scenarios.

Contribution

It introduces a novel ML algorithm with a backward-pass rate assessment and iterative balancing for relay selection in energy-harvesting IoT networks.

Findings

The proposed scheme outperforms conventional methods in simulations.

The algorithm maintains stability under various network conditions.

It effectively balances energy and throughput among nodes.

Abstract

In this paper, we examine the internet of things system which is dedicated for smart cities, smart factory, and connected cars, etc. To support such systems in wide area with low power consumption, energy harvesting technology without wired charging infrastructure is one of the important issues for longevity of networks. In consideration of the fact that the position and amount of energy charged for each device might be unbalanced according to the distribution of nodes and energy sources, the problem of maximizing the minimum throughput among all nodes becomes a NP-hard challenging issue. To overcome this complexity, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize the node with sufficient energy as the relay. To validate the proposed scheme, we conducted simulations on the system model we established, thus confirm that the proposed scheme is stable and superior to conventional schemes.