A Sensing Data Driven Clustering Algorithm for Adaptive Sampling in Wireless Sensor Networks

Abstract:

The objective of environmental observation with wireless sensor networks is to extract the synoptic structures of the phenomena of region of interest (ROI) in order to make effective predictive and analytical characterizations. Adaptive sampling strategy is regarded as a much promising method for improving energy efficiency in recent years. However, due to distributed characteristics of wireless sensor networks, adaptive sampling schemes should be operated in a distributed manner with clustering algorithm. In this paper, we dedicate to investigating appropriate sensing-aware clustering algorithm for adaptive sampling. The principle of SAC algorithm follows such metric: sensor nodes that are similar to each other in terms of their observed sensory data should be clustered into one group. Besides, sensor nodes will join in its nearest cluster for the sake of spatial correlation model with Euclidean physical distance. By emphasizing on the sensing-aware clustering, it helps to derive better spatial correlation to guarantee adaptive sampling. The simulation results verify SAC algorithm at the aspect of correlation factor.

• Periodical: Applied Mechanics and Materials (Volumes 182-183)
• Pages: 748-752
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.182-183.748
• Online since: June 2012
• Authors: Mei Yan Zhang, Wen Yu Cai, Li Ping Zhou
• Keywords: Adaptive Sampling, Energy Effective, Sensing-Aware Clustering, Wireless Sensor Network (WSN)

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