Joint link scheduling and power allocation in imperfect and energy-constrained underwater wireless sensor networks

TL;DR

This paper introduces ICRL-JSA, a deep reinforcement learning-based method for joint link scheduling and power allocation in energy-constrained, imperfect underwater wireless sensor networks, improving communication fairness and reliability.

Contribution

It proposes a novel deep MARL algorithm tailored for underwater environments, addressing energy constraints and node malfunctions without human intervention.

Findings

ICRL-JSA outperforms benchmark algorithms in simulations.

The method effectively handles complex acoustic channels.

It enhances fairness, efficiency, and reliability in UWSNs.

Abstract

Underwater wireless sensor networks (UWSNs) stand as promising technologies facilitating diverse underwater applications. However, the major design issues of the considered system are the severely limited energy supply and unexpected node malfunctions. This paper aims to provide fair, efficient, and reliable (FER) communication to the imperfect and energy-constrained UWSNs (IC-UWSNs). Therefore, we formulate a FER-communication optimization problem (FERCOP) and propose ICRL-JSA to solve the formulated problem. ICRL-JSA is a deep multi-agent reinforcement learning (MARL)-based optimizer for IC-UWSNs through joint link scheduling and power allocation, which automatically learns scheduling algorithms without human intervention. However, conventional RL methods are unable to address the challenges posed by underwater environments and IC-UWSNs. To construct ICRL-JSA, we integrate deep Q-network into IC-UWSNs and propose an advanced training mechanism to deal with complex acoustic channels, limited energy supplies, and unexpected node malfunctions. Simulation results demonstrate the superiority of the proposed ICRL-JSA scheme with an advanced training mechanism compared to various benchmark algorithms.