Tuning Data Reporting and Sensing for Continuous Monitoring in Wireless Sensor Networks

In most of Wireless Sensor Networks (WSNs) deployments, application requirements at sink and nature of event decide sensing frequency. Very high sensing frequency at sensor node (SNs) may result in generation of useless redundant data and too low sensing frequency may miss useful data about the event. Also, spurious data may be injected into network due to some temporary abnormal conditions near some active SNs. Therefore, in this paper we propose a data reporting scheme where prior to dissemination SN checks accumulated data against a filtering criteria. Further, sensing a slowly changing event with very high frequency results in lot of energy waste as it may generate redundant data. Whereas, sensing rapidly changing event with low frequency may cause loss of useful information. Since, the rate of change in the behavior of an event may be unpredictable, therefore along with data filtering we also propose an approach in which a cluster head node (CHN) dynamically sets sensing frequency in its cluster SNs by analyzing the flow of data reports received from these SNs. Simulation results show significant energy savings when this scheme is applied to various data dissemination approaches as opposed to scenarios when no data filtering is used and sensing frequency is fixed.