Motion sensor and camera placement design for in-home wireless video monitoring systems

Motion sensors and cameras can be used together to build in-home video monitoring systems, where the cameras are only activated when the motion sensors detect motion. This saves both bandwidth (reduced video storage and/or transmission) as well as energy (if the cameras are battery-operated). However, motion sensors and cameras have different fields of view (FoV), and thus it is not clear that attaching motion sensors to cameras provides the most efficient system. Therefore, it is necessary to evaluate the performance of systems where the motion sensors are attached to the cameras (attached scenario) as well as where the motion sensors are detached from the cameras (detached scenario). We consider the motion sensor and camera placement problem under these two scenarios, with an additional hybrid scenario, and we formulate optimization problems to achieve the minimum energy consumption, longest network lifetime, or lowest cost. Simulation results show the tradeoffs between different objectives for all three scenarios.Peer ReviewedPostprint (published version

Motion sensor and camera placement design for in-home wireless video monitoring systems

Motion sensors and cameras can be used together to build in-home video monitoring systems, where the cameras are only activated when the motion sensors detect motion. This saves both bandwidth (reduced video storage and/or transmission) as well as energy (if the cameras are battery-operated). However, motion sensors and cameras have different fields of view (FoV), and thus it is not clear that attaching motion sensors to cameras provides the most efficient system. Therefore, it is necessary to evaluate the performance of systems where the motion sensors are attached to the cameras (attached scenario) as well as where the motion sensors are detached from the cameras (detached scenario). We consider the motion sensor and camera placement problem under these two scenarios, with an additional hybrid scenario, and we formulate optimization problems to achieve the minimum energy consumption, longest network lifetime, or lowest cost. Simulation results show the tradeoffs between different objectives for all three scenarios.Peer Reviewe