Human motion tracking in a wireless network of mobile and static sensors

This paper presents the development of an integrated mobile and static wireless sensor network (WSN) for the purpose of tracking a human in a home environment. Majority of tracking solutions rely on expensive sensors with high power requirements, which are not scalable nor suitable for systems requiring a long network lifetime. Our approach is to use inexpensive off-the-shelf stationary ranging sensors, placed at known locations in the environment. However, issues encountered when tracking a human with a limited number of sensors may include a poor tracking estimate and poor coverage regions where the static sensors are unable to detect the target of interest. An alternative to increasing the number of static sensors deployed, is to integrate single mobile sensor into the network. A collaborative sensor selection scheme is applied to static sensors which returns the measurement that will yield the highest information gain and maximise the network lifetime. Results show that with a robot following the human, the regions where the human is detected is increased and an improved tracking estimate is obtained from the fusion of static and mobile sensor readings.