Energy-Efficient Boundary Monitoring for Large-Scale Continuous Objects

Abstract

For monitoring of a large-scale continuous object, a large number of sensor nodes might be participated with object detection and tracking. In order to reduce huge quantities of data from the sensor nodes, previous studies focus on representative selection for data reporting to a sink. However, they simply choose representatives among a large number of candidates without consideration of node deployment environments and detection accuracy. Hence, this letter proposes a novel object tracking scheme that first makes a small set of candidates and then chooses a small number of representatives in the set. Also, since the scheme also considers object alteration for representative selection, it can provide high energy-efficiency despite reducing data reporting.