Preferential Coverage Based Efficient Sensor Placement in Distributed Sensor Networks

摘要

Effective sensor placement methodologies are desired for distributed sensor networks frequently encountered in military, environmental, and nano-biotechnology applications. The goal is to provide a (sub-)optimal framework for sensor resource management, while placing those sensors such that they provide accurate coverage within the required location and range probability. The problem is not trivial as the sensors might not be of equal capacity, the terrain upon which the sensors are deployed might have many obstacles and some sensors might fail. In some applications, areas over the sensor field are marked preferential, with high desired probability of detection and coverage. In this paper, we propose a unique sensor placement computing framework for preferential coverage in the sensor-field, while trying to deploy minimum number of sensors. The proposed approach treats the sensor field as an image, which provides an advantage of attaining pixel-level accuracy in sensor placement. A unique algorithm is presented that initially concentrates on the preferential regions and then proceeds towards the calibration of other uncovered regions of the sensor field. Our approach has shown significant improvement in time-performance in contrast to the greedy-approach, and has a strong potential for applications in several mission-critical applications.