Investigation of In-Network Data Mining Approach for Energy Efficient Data Centric Wireless Sensor Networks

Abstract

The continuous improvement in hardware design and advances in wireless communication have enabled the deployment of various wireless applications. Wireless sensor network applications become essential tools for monitoring the activity and evolution of our surrounding environment. However, the wireless sensor nodes are highly resource constrained in terms of limited processing speed, run time memory, persistent storage, communication bandwidth and finite energy. Therefore, for energy efficient in-network data retention and query processing, data mining approach is highly required that reduces the storage space, energy and communication cost consumption. This paper investigates the data mining approach for clustering sensor networks. Results show 99.88% less storage space, 37.6% reduced energy and 80% increased query throughput is achieved using data mining approach for wireless sensor networks.