The remarkable advances of microsensing microelectromechanical systems (MEMS) and wireless communication technologies have promoted the development of wireless sensor networks. A WSN consists of many sensor nodes densely deployed in a field, each able to collect environmental information and together able to support multihop ad hoc routing. WSNs provide an inexpensive and convenient way to monitor physical environments. With their environment-sensing capability, WSNs can enrich human life in applications such as healthcare, building monitoring, and home security.
Traditional surveillance systems typically collect a large volume of videos from wallboard cameras, which require huge computation or manpower to analyze. Integrating WSNs’ sensing capability into these systems can reduce such overhead while providing more advanced, context-rich services. For example, in a security application, when the system detects an intruder, it can conduct in-depth analyses to identify the possible source. The “Related Work in Wireless Surveillance” sidebar provides additional information about other work in this area.
Our integrated mobile surveillance and wireless sensor system (iMouse) consists of numerous static wireless sensors and several more powerful mobile sensors. The benefits of iMouse include the following:
- It provides online real-time monitoring. For example, when the system is capturing events, the static sensors can immediately inform users where the events are occurring, and the mobile sensors can later provide detailed images of these events.
- It’s event-driven, in the sense that only when an event occurs is a mobile sensor dispatched to capture images of that event. Thus, iMouse can avoid recording unnecessary images when nothing happens.
- The more expensive mobile sensors are dispatched to the event locations. They don’t need to cover the whole sensing field, so only a small number of them are required.
- It’s both modular and scalable. Adding more sophisticated devices to the mobile sensors can strengthen their sensing capability without substituting existing static sensors.
Because mobile sensors run on batteries, extending their lifetime is an important issue. We thus propose a dispatch problem that addresses how to schedule mobile sensors to visit emergency sites to conserve their energy as much as possible. We show that if the number of emergency sites is no larger than the number of mobile sensors, we can transform the problem to a maximum matching problem in a bipartite graph; otherwise, we group emergency sites into clusters so that one mobile sensor can efficiently visit each cluster.