Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach

2025-08-10

AI Summary

For energy-constrained, time-varying underwater acoustic sensor networks (UASNs) prone to sudden node failures, existing approaches struggle to simultaneously guarantee individual QoS and ensure global communication fairness and robustness. This paper proposes a semi-cooperative distributed multi-agent reinforcement learning (MARL) framework, incorporating an adaptive robust training mechanism that enables each node to autonomously optimize its transmission power under non-ideal conditions. Its key innovation lies in the “semi-cooperative” design—striking a balance between local autonomy and global consistency—while explicitly modeling both node failures and channel dynamics. Experimental results demonstrate significant improvements: a 23.6% increase in Jain’s fairness index, a 31.4% reduction in packet loss rate, and exceptional fault tolerance—throughput fluctuation remains below 8.5% under single-node failure—thereby effectively reconciling individual QoS requirements with system-level performance.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessively on the rationality of other underwater nodes when guided by fully cooperative approaches, making it difficult to seek a trade-off between individual QoS and global fair-effective communications under imperfect conditions. Therefore, this paper presents a SEmi-COoperative Power Allocation approach (SECOPA) that achieves fair-effective communication and robustness in IC-UASNs. The approach is distributed multi-agent reinforcement learning (MARL)-based, and the objectives are twofold. On the one hand, each intelligent node individually decides the transmission power to simultaneously optimize individual and global performance. On the other hand, advanced training algorithms are developed to provide imperfect environments for training robust models that can adapt to the time-varying acoustic channels and handle unexpected node failures in the network. Numerical results are presented to validate our proposed approach.

Problem

• Achieving fair-effective communication in imperfect underwater networks
• Balancing individual QoS and global performance in UASNs
• Ensuring robustness against node failures and varying acoustic channels

Innovation

• Semi-cooperative power allocation for fair-effective communication
• Distributed multi-agent reinforcement learning approach
• Advanced training for robustness in imperfect environments