Leveraging Data Fusion in Dynamic WSN Network Size Reduction

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Abstract:

Wireless Sensor Networks (WSN) continue their tremendous growth acceleration. WSNs have found their way into a wide range of domains, from military and transportation applications to medical and environmental monitoring. Some of these applications can include a very large number of nodes, which poses significant challenges to network lifetime, data transmission, and overall reliability. Recently, data fusion approaches are gaining traction in WSNs for improving reported data accuracy and help predict future events. They are used to improve the reliability of delivered information. While this addresses data accuracy, it does not address the inefficiencies caused by very large networks and high redundancy within the data. Data aggregation is a simple way of streamlining data flow, but does not fully address the issue. The large WSN size causes congestion and increases the traffic load in the network; plus, decreasing the performance of the WSN and potentially disrupting its operation altogether. In this paper, we therefore explore data fusion based on Kalman Filters (KF) as a technique to reduce the number of active sensor nodes to conserve network resources while also achieving high data accuracy. Our results show the great potential of this approach for improving WSN operations.