Coverage-aware self-scheduling in sensor networks

One important measurement of a sensor network's performance is how well the sensor network can monitor a covered area, namely network coverage. In this paper, we propose a scheme to improve the network coverage of a sensor network. We assume that each sensor can probabilistically detect a nearby event, and build a probabilistic model of network coverage by considering the data correlation among neighboring sensors. We also define Sensing Denomination (SD) of a sensor using the network coverage model. A sensor's SD is regarded as the measurement of its contribution to the network coverage. The more the contribution of a sensor to the network coverage, the higher the sensor's SD is. Based on the location information of neighboring sensors, each sensor can calculate its SD value in a distributed manner. We then propose coverage-aware self-scheduling, in which sensors periodically calculate their SD values and probabilistically schedule their active and hibernating states according to their SD values so that sensors with higher SD value have more chance to stay active. With the same amount of energy consumed, better network coverage can be achieved by applying the scheme. We then demonstrate through numerical study that the scheme improves sensor network coverage considerably.