# Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.09671 · HTML: https://mlsanity.com/paper/2002.09671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [Almost Sure Convergence of Networked Policy Gradient over Time-Varying Networks in Markov Potential Games](https://mlsanity.com/paper/2410.20075/) (2024)

## Abstract (arXiv)

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.