Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning
This work addresses energy efficiency in vehicle tracking for applications like rescue and surveillance, but it appears incremental as it builds on existing methods with DRL enhancements.
The paper tackles the trade-off between tracking accuracy and energy consumption in vehicle tracking within wireless sensor networks by proposing a decentralized strategy that adjusts activation areas, resulting in improved performance as shown in simulations.
Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL aided design.