Scheduling data transmissions in wireless sensor networks used for position tracking

Abstract

In wireless sensor networks developed for ambient assisted living (AAL) applications, power supply is one of the most challenging problems. To extend the duration of the operation of a wireless sensor node, effcient utilization of the available energy can be achieved with low duty-cycle operation, where the sensor node is in sleep mode most of the time. In the case when measurements have low cost, a method is proposed for decreasing the usage of the most energy consuming mode (communication) by handling the measured data locally. In AAL applications the position tracking of a person is an essential task, and it is a good demonstrative example for showing the solution principles. Position tracking with motion sensors requires a high number of messages and most of them are caused by local movements. Our suggestion is to eliminate these messages. The method is based on a Hidden Markov Model of the motions of an observed person. The model provides information based on the estimated global state of the system, which is the position of the person in the space of interest. This state can be forwarded to the nodes so they locally perform the filtering to save valuable energy by not transmitting messages which are not relevant.