Target Tracking Using Sensor-Cloud: Sensor-Target Mapping in Presence of Overlapping Coverage

Abstract

This work considers tracking of multiple targets using the sensor-cloud infrastructure. As targets enter the coverage zone of multiple sensors, it becomes crucial to schedule sensors and generate distinct clusters of sensors for each target. It becomes challenging to correctly map sensors to targets, in presence of overlapping coverage, to maintain their privacy and correctness of sensed information about the targets. We propose the Dynamic Mapping Algorithm (S-DMA) based on the Theory of Social Choice for ensuring a `fair' and unbiased mapping of sensors to targets. The distribution of summation of the preference values of sensors allocated to targets exhibit a standard deviation of 0.71 within 99% confidence interval. This implies that S-DMA maintains uniformity while scheduling sensors for every target.