T-D3QN: Deep Reinforcement Learning Based Digitalizing Bit Allocation for Target Tracking in Underwater Wireless Sensor Networks

Reducing energy consumption while maintaining tracking accuracy is a significant challenge in the context of underwater target tracking using wireless sensor networks. The allocation of digitalizing bits for each sensor node is pivotal in influencing both tracking accuracy and energy efficiency. However, traditional optimization methods for bit allocation in sensing quantization are constrained by environmental dependencies, inflexibility, and prolonged computation times. To address these issues, we propose an innovative bit allocation algorithm based on deep reinforcement learning (DRL) for target tracking in underwater sensor networks. This algorithm focuses on adaptively quantizing and transmitting sensor data to optimize energy usage. Initially, we establish a quantitative relationship between bit allocation, tracking accuracy, and energy consumption within these sensor nodes. Building on this foundation, we develop a bit allocation model to minimize energy consumption while adhering to accuracy constraints. We present a novel transformer-based DRL network, the transformer-double dueling deep Q-network, to optimize bit allocation strategies. This network facilitates the offline-trained reinforcement learning agent to derive optimal sensor quantization bit allocations in real time. Our experimental results demonstrate that the proposed algorithm effectively achieves a balance between target tracking precision and digital bit consumption, significantly enhancing real-time computational efficiency.