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

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.