Sensor Network Coverage Restoration

Wireless sensor networks are emerging as a new computational platform consisting of small, low-power and inexpensive nodes used in a broad set of application areas including environmental monitoring, habitat monitoring and disaster recovery. Typically sensor nodes are deployed over a geographical area for the purpose of detecting, tracking and monitoring events of interest. Since sensor nodes are deployed in a large land region, the objective is to achieve complete coverage of the region, that is, every location in the region lies in the observation field of at least one sensor node. However the initial placement of sensors may not achieve this goal for various reasons: the number of original sensors may have been too low, the original placement may have been random (for example, sensors deployed from the air) leaving parts of the region uncovered, or, some of the sensors have malfunctioned, leaving coverage holes. In this paper we consider the coverage restoration problem in sensor networks. The goal is to find a minimal set of new sensors, and their locations, such that when they are added to an existing sensor field we can achieve complete coverage of the region under surveillance. The technique we propose is distributed, and minimizes the communication costs. The key idea of our technique is to use an efficient and yet very accurate representation of the uncovered area that uses techniques from discrepancy theory. By representing the uncovered area as a set of points, we can use efficient and simple algorithms for finding small sets of sensors to cover the uncovered areas. We partition the sensor network into cells, and run these algorithms locally. We also present an extensive experimental evaluation to validate our approach.