Adaptive Vision-Based Coverage Optimization in Mobile Wireless Sensor Networks: A Multi-Agent Deep Reinforcement Learning Approach

TL;DR

This paper introduces a vision-based, multi-agent deep reinforcement learning method for autonomous, energy-efficient coverage optimization in mobile wireless sensor networks, improving coverage and extending network lifetime.

Contribution

It presents a novel, scalable MARL-DRL framework with a vision-based coverage evaluation for autonomous sensor deployment and reconfiguration.

Findings

Achieves 26.5% better coverage than traditional methods

Reduces energy consumption by 32%

Extends network lifetime by 45%

Abstract

Traditional Wireless Sensor Networks (WSNs) typically rely on pre-analysis of the target area, network size, and sensor coverage to determine initial deployment. This often results in significant overlap to ensure continued network operation despite sensor energy depletion. With the emergence of Mobile Wireless Sensor Networks (MWSNs), issues such as sensor failure and static coverage limitations can be more effectively addressed through mobility. This paper proposes a novel deployment strategy in which mobile sensors autonomously position themselves to maximize area coverage, eliminating the need for predefined policies. A live camera system, combined with deep reinforcement learning (DRL), monitors the network by detecting sensor LED indicators and evaluating real-time coverage. Rewards based on coverage efficiency and sensor movement are computed at each learning step and shared across the network through a Multi-Agent Reinforcement Learning (MARL) framework, enabling decentralized, cooperative sensor control. Key contributions include a vision-based, low-cost coverage evaluation method; a scalable MARL-DRL framework for autonomous deployment; and a self-reconfigurable system that adjusts sensor positioning in response to energy depletion. Compared to traditional distance-based localization, the proposed method achieves a 26.5% improvement in coverage, a 32% reduction in energy consumption, and a 22% decrease in redundancy, extending network lifetime by 45%. This approach significantly enhances adaptability, energy efficiency, and robustness in MWSNs, offering a practical deployment solution within the IoT framework.