Utilization of convex optimization for data fusion-driven sensor management in WSNs

In large-scale Wireless Sensor Networks (WSNs), one of the most important challenges is manageability of the network. With the increase in sensor nodes, data forwarding, route selection, network reliability and data accuracy are vital characteristics of WSNs that suffer from the growth in scale. In this paper, we propose a data fusion based approach to drastically improve network lifetime, reduce excessive network load, and improve overall WSN performance. Our proposed approach utilizes employment of data fusion to intelligently select a subset of nodes with information needed for the data fusion, while removing all redundant nodes without impacting the fused data quality. We also introduce two methods for reducing the number of sensor nodes in a generic estimation problem using data fusion for reliability improvement of the sensed data in the presence of noise. The first method is based on observation similarity, while the second method leverages convex optimization. Our results show that our proposed methods can greatly improve large-scale WSN operation efficiency.

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