Fractal Clustering and similarity measure: Two new approaches for reducing energy consumption in Wireless Sensor Networks

Sensor clustering is an efficient strategy to reduce the number of messages flowing in a Wireless Sensor Network (WSN), decreasing, this way, the energy consumption in the network. This paper presents two new approaches for sensors clustering in WSNs, namely Fractal Clustering in Wireless Sensor Networks (FCWSN) and Similarity Measure in Wireless Sensor Networks (SMWSN). Both approaches are based on a new principle, known as behavioral clustering, which is able to cluster sensors with similar sensed data patterns of recent historical data collected. By exploring the new clustering method, the approaches are able to reduce message transmission by using cluster-heads for concentrating communication between sensors and sink. In order to validate and compare the proposed approaches, simulations have been conducted over real data, using SinalGo simulator. Results show that FCWSN and SMWSN can both significantly reduce the number of messages injected into the network whereas SMWSN presented a number of messages slightly smaller than FCWSN. In relation to Root Mean Square Error (RMSE), FCWSN remains about 10% lower than SMWSN approach, while both have a low RMSE.