Adaptive Coverage Optimization for Energy-Constrained Multicamera Surveillance Networks

Efficient surveillance is crucial for ensuring security across diverse everyday environments. Traditionally, coverage optimization algorithms focus primarily on maximizing coverage over the surveillance region with little or no regard for energy efficiency. In the event of an energy-constrained scenario such as a power shortage, an ideal surveillance network should exhibit graceful degradation by operating with a reduced number of cameras that maintain coverage at least across the high-priority regions. Traditional surveillance networks lack an adaptive mechanism to dynamically turn cameras on or off in response to variations in energy availability. In this article, we propose the camera coverage ranking (CCR) algorithm, a novel approach for ranking all the cameras in a surveillance network based on their area of coverage, priority of the covered region, and extent of overlap with neighboring cameras. This rank order serves as the sequence for activating or deactivating cameras under energy-constrained scenarios as per various end-user requirements. We validate our approach on map images across diverse indoor and outdoor surveillance scenarios with existing camera networks. The experimental results show that our algorithm ensures optimal coverage in the face of varying energy availability. Our approach is both cost-effective and computationally efficient, and thus minimizes the effort involved in achieving adaptive coverage optimization for energy-efficient multicamera surveillance networks.