Exploring group moving pattern for an energy-constrained object tracking sensor network

Abstract

In this paper, we investigate and utilize the characteristic of the group movement of objects to achieve energy conservation in the inherently resource-constrained wireless object tracking sensor network (OTSN). We propose a novel mining algorithm that consists of a global mining and a local mining to leverage the group moving pattern. We use the VMM model together with Probabilistic Suffix Tree (PST) in learning the moving patterns, as well as Highly Connected Component (HCS) that is a clustering algorithm based on graph connectivity for moving pattern clustering in our mining algorithm. Based on the mined out group relationship and the group moving patterns, a hierarchically predict ion-based query algorithm and a group data aggregation algorithm are proposed. Our experiment results show that the energy consumption in terms of the communication cost for our system is better than that of the conventional query/update based OTSN, especially in the case that on-tracking objects have the group moving characteristics.