Data fusion utilization for optimizing large-scale Wireless Sensor Networks

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 nodes and high data redundancy. 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 Kalman Filters (KF) based data fusion as a technique to reduce the number of active sensor nodes in a very large WSN to conserve network resources while preserving the required data reliability and accuracy. Our results show that there is great potential for improving WSN operations utilizing our proposed approach.