Optimisation of spatiotemporal context-constrained full-view area coverage deployment in camera sensor networks via quantum annealing

Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP's complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.