Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks

TL;DR

This paper introduces a hierarchical edge intelligence framework for mobile target tracking in wireless sensor networks, optimizing energy and accuracy through a dynamic resource allocation algorithm.

Contribution

It proposes a novel hierarchical structure integrating mobile nodes and edge servers, along with a long-term resource allocation algorithm for improved tracking performance.

Findings

Energy consumption reduced by over 14.5% with the proposed algorithm.

Enhanced tracking accuracy compared to non-cooperative schemes.

Effective real-time target tracking with optimized resource management.

Abstract

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in Mobile Target Tracking Wireless Sensor Networks (MTT-WSN). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is difficult to execute dynamic and critical tasks in the MTT-WSN network. Besides, the network cannot ensure consecutive tracking due to the limited energy. To address the problems, this paper proposes a new hierarchical target tracking structure based on Edge Intelligence (EI) technology. The structure integrates the computing resource of both mobile nodes and edge servers to provide efficient computation capability for real-time target tracking. Based on the proposed structure, we formulate an energy optimization model with the constrains of system execution latency and trajectory prediction accuracy. Moreover, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource allocation solution for the ac- curate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep Q-learning over 14.5% in terms of system energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the non-cooperative scheme.