Most wireless sensor networks (WSNs) are deployed for continuous monitoring of event(s) over very long periods. In such scenarios, lots of redundant and spurious data readings are generated at sensing nodes (SNs). If data remains untreated, continuous sensing over very long duration injects numerous undesired data readings into the network causing unnecessary consumption of substantial portion of network energy. Further, if sensing frequency at SNs is fixed and event changes its behaviour randomly, then either lot of redundant data readings are generated or many important observations are missed at crucial points. Therefore, in this paper a data filtering scheme called as two-way sliding window (TWSW) data filtering is proposed that filters/blocks redundant and spurious data using some filtering function at few SNs. Using TWSW setup, we further propose a novel scheme to dynamically adjust sensing frequency at active SNs to adapt to changing behaviour of an event. Simulation results show significant energy savings when these schemes are applied to various data dissemination approaches as opposed to scenarios when no such approaches are used.
['Teek Parval Sharma', 'Ramesh C. Joshi', 'Manoj Misra']
Data filtering and dynamic sensing for continuous monitoring in wireless sensor networks