Energy optimization is one of the most important issues in the research of wireless sensor networks (WSNs).In the applications of monitoring,we scatter a large number of sensors uniformly to cover a few Points of Interest(PoI) distributed randomly in the monitored area.Since the energy of battery-powered sensor is limited in WSNs,sensors are scheduled to wake up in a large scale sensor network application.In this paper,we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large scale application of monitoring.To extend the lifetime of sensor network,we need to balance the energy consumption of sensors so that there won't be too much redundant energy in some sensors while the lifetime of WSN terminates.The detection probability and false alarm probability are taken into consideration to achieve a better performance and depict the real sensing process which is characterized in the probabilistic sensing model.Data fusion is also introduced to utilize information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaborativelv,which will decrease the number of sensors that cover the monitored region.Based on the probabilistic model and data fusion,Minimum Weight Probabilistic Coverage Problem(MWPCP) is formulated in this paper.We also propose a greedy method to solve MWPCP and conduct simulation experiments to prove our superiority over existing work.
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