Cooperative sensing in mobile sensor networks based on distributed consensus

Abstract

Mobile sensor networks (MSNs) have wide applications such as military target detection and tracking, detection of toxic chemicals in contaminated environments, and search and rescues after disasters, etc. In many applications, a core problem is to conduct cooperative scalar field mapping (or searching) over a large area of interest. Centralized solutions to the scalar field mapping may not fit for large mobile sensor network due to the single-point-of-failure problem and the limited scalability. In this paper, autonomous mobile sensor networks are deployed to map a scalar field in a cooperative and distributed fashion. We develop a cooperative sensor fusion algorithm based on distributed consensus filters. In this algorithm each agent receives measurements from its neighboring agents within its communication range, and iteratively updates the estimate of the unknown scalar field and an associated confidence map. A motion planning algorithm is used to obtain a path for complete coverage of the field of interest. A distributed flocking control algorithm is adopted to drive the center of the mobile sensor network to track the desired paths. Computer simulations are conducted to validate the proposed algorithms. We evaluate the mapping performance by comparing it with a centralized mapping algorithm. Such a cooperative sensing approach can be used in many military surveillance applications where targets may be small and elusive.