4.7 Article

Barrier Coverage Quality Improvement for AI-Based Passive Bistatic Radar Networks

Journal

IEEE SENSORS JOURNAL
Volume 21, Issue 22, Pages 25379-25390

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3071770

Keywords

AI-based smart IoTs; barrier coverage; AI-based passive bistatic radar sensor network; gap localization; gap mending

Funding

  1. National Natural Science Foundation of China [61761029, 61461030, 61871209]

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This paper investigates the issue of automatically localizing barrier gaps and computing the locations of new receivers to improve coverage quality in an AI-based smart node PBRSN. The proposed algorithms are validated through extensive simulations, with results showing accurate gap localization and efficient barrier gap mending with fewer receivers.
Artificial Intelligent based smart Internet of Things (AI-based IoTs) refers to a network that has sensing data analysis function and network automatic management functions such as self-diagnosis and self-healing function. Among them, self-healing function is a critical problem in an AI-based Passive Bistatic Radar Sensor Network (PBRSN). Much different from active bistatic radar, transmitters in PBRSN are not deployed by network designer and may be out of work due to some irresistible reasons, then the coverage quality of a protected region cannot be satisfied. Therefore, how to localize barrier gap accurately and mend barrier gap quickly are the most important issues in a PBRSN. In this paper, we study the problem of how to automatically localize barrier gap and compute the locations of new receivers for coverage quality improvement in a PBRSN with an AI-based smart node, where the network was providing K barriers coverage for a protected region by adopting receivers optimal deployment strategy. First, we design an algorithm to localize all barrier gaps, and then an algorithm called Deployment Line between Sub-barriers Algorithm (DLSA) is designed to compute the locations of new receivers inside gap region with the objective of minimizing the total number of new receivers. Finally, extensive simulations are conducted to validate the correctness and effectiveness of our proposed algorithms. The simulation results show that our gap finding algorithm can localize all barrier gaps accurately. While the DLSA can mend a barrier gap with fewer receivers if there is at least one working transmitter in this barrier.

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