Coverage-adaptive random sensor scheduling for application-aware data gathering in wireless sensor networks

Due to the application-specific nature of wireless sensor networks, application-aware algorithm and protocol design paradigms are highly required in order to optimize the overall network performance depending on the type of applications. In this paper, we propose a novel coverage-adaptive random sensor scheduling for application-aware data-gathering in wireless sensor networks, with a goal to maximize the network lifetime. The underlying idea is to decide in each round (approximately) k data reporters (sensors) which can meet the desired sensing coverage specified by the users/applications. The selection of these k data reporters is based on a geometric probability theory and a randomization technique with constant computational complexity without exchanging control (location) information with local neighbors. The selected k data reporters for a round form a data gathering tree to get rid of wait-and -forward delay which may result from the random sensor scheduling and are scheduled to remain active (with transceiver on) during that round only, thus saving energy. All sensors have an equal opportunity to report sensed data periodically so the entire monitored area is covered within a fixed delay. Simulation results show that our proposed random sensor scheduling leads to a significant conservation of energy with a small trade-off between coverage and data reporting latency while meeting the coverage requirement given by the users/applications. (c) 2006 Elsevier B.V. All rights reserved.