Traffic Load-Aware Resource Management Strategy for Underwater Wireless Sensor Networks

2025-08-11

AI Summary

To address communication inefficiency and unreliability in energy-constrained underwater wireless sensor networks (UWSNs)—caused by time-varying channels, multipath fading, and biased local observations—this paper proposes a distributed resource management framework based on deep multi-agent reinforcement learning (MARL). The resource scheduling problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). To mitigate local observation distortion, we introduce a neighbor-listening-driven traffic load awareness mechanism; additionally, a solution-space pruning strategy is adopted to significantly reduce action-space complexity. Simulation results demonstrate that the proposed method maintains robust adaptability under dynamic traffic loads and high packet-collision probabilities. Compared to baseline approaches, it improves throughput by 18.7% and reduces packet loss rate by 32.4%, while jointly optimizing energy efficiency, reliability, and scalability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Distributed Problem Solving
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Underwater Wireless Sensor Networks (UWSNs) represent a promising technology that enables diverse underwater applications through acoustic communication. However, it encounters significant challenges including harsh communication environments, limited energy supply, and restricted signal transmission. This paper aims to provide efficient and reliable communication in underwater networks with limited energy and communication resources by optimizing the scheduling of communication links and adjusting transmission parameters (e.g., transmit power and transmission rate). The efficient and reliable communication multi-objective optimization problem (ERCMOP) is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). A Traffic Load-Aware Resource Management (TARM) strategy based on deep multi-agent reinforcement learning (MARL) is presented to address this problem. Specifically, a traffic load-aware mechanism that leverages the overhear information from neighboring nodes is designed to mitigate the disparity between partial observations and global states. Moreover, by incorporating a solution space optimization algorithm, the number of candidate solutions for the deep MARL-based decision-making model can be effectively reduced, thereby optimizing the computational complexity. Simulation results demonstrate the adaptability of TARM in various scenarios with different transmission demands and collision probabilities, while also validating the effectiveness of the proposed approach in supporting efficient and reliable communication in underwater networks with limited resources.

Problem

• Optimizing communication links in UWSNs with limited energy
• Mitigating partial observation disparities using traffic load-awareness
• Reducing computational complexity in deep MARL decision-making

Innovation

• Deep multi-agent reinforcement learning for resource management
• Traffic load-aware mechanism using neighbor overhear information
• Solution space optimization to reduce computational complexity