Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dyn...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-08, Vol.9 (16), p.15066-15076
Main Authors: Zhou, Longyu, Leng, Supeng, Liu, Qiang, Chai, Haoye, Zhou, Jihua
Format: Article
Language:English
Subjects:
Algorithms
Collaborative computing
deep reinforcement learning
dynamic resource allocation
Edge computing
edge intelligence (EI)
Energy consumption
Heuristic algorithms
Machine learning
Manganese
Mobile computing
Nodes
Real-time systems
Resource allocation
Resource scheduling
Sensors
Servers
Structural hierarchy
Target tracking
Task analysis
Tracking
Wireless networks
Wireless sensor networks
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Summary:Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep {Q} -learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3125530