Cluster-based location report for person tracking in wireless sensor networks

In traditional tracking systems, the mobiles report their locations periodically. With the number of the mobiles increasing, these methods result in high loss rate of packets and rapid depletion of the network energy. In practice, we observe that some mobiles are so close to each other that we are informed the same location from the localization algorithm. Thus it is necessary and possible to reduce message complexity through merging the location messages. By exploiting the Received Signal Strength (RSS), this paper proposes a human-Behavior based Mobile Clustering Mechanism (BMC) for Rf-based Person Tracking Systems. It constructs clusters according to the distance between mobiles and maintains clusters efficiently. In BMC, only the cluster-heads report locations periodically instead of each node, thereby decreasing the message complexity greatly. Signal interference will wobble the clusters in practical environment, so it degrades the performance of BMC dramatically. To address the issue, Signal Smoothness is developed. The simulation shows that BMC based location report outperforms traditional methods by 64% of message reduction on average in impartial testing scenes.