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.

Spatiotemporal correlation–based adaptive sampling algorithm for clustered wireless sensor networks

“…The detailed sensing-aware clustering algorithm is illustrated in our previous work. 25 Figure 7 demonstrates the clustering results of different CH percent ( p = 0 . 05 and p = 0 . 15 ). In Figure 7, different clustering nodes are labeled with different colors and shapes, and sensor nodes with sign number for each cluster represent CHs.…”