POSE.3C: Prediction-Based Opportunistic Sensing Using Distributed Classification, Clustering, and Control in Heterogeneous Sensor Networks

Abstract

This paper presents a distributed algorithm, called prediction-based opportunistic sensing using distributed classification, clustering, and control (POSE.3C), for self adaptation of sensor networks for energy management. The underlying 3C network autonomy concept enables utilization of the target classification information to form dynamic clusters around the predicted target position via selection of sensor nodes with the highest energies and maximum geometric diversity. Furthermore, the nodes can probabilistically control their heterogeneous devices to track targets of interest and minimize energy consumption in a completely distributed manner. Theoretical properties of the POSE.3C network are established and derived in terms of the network lifetime and missed detection characteristics. The algorithm is validated through extensive simulations that demonstrate a significant increase in the network lifetime as compared to other network control approaches, while providing high tracking accuracy and low missed detection rates.