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

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.

[1]  Ravi Prakash,et al.  Max-min d-cluster formation in wireless ad hoc networks , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Rajeev Shorey,et al.  Mobile, Wireless and Sensor Networks: Technology, Applications and Future Directions , 2005 .

[3]  Dirk Timmermann,et al.  Low energy adaptive clustering hierarchy with deterministic cluster-head selection , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[4]  Nirupama Bulusu,et al.  Wireless Sensor Networks A Systems Perspective , 2005 .

[5]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[6]  Patrick J. Vincent,et al.  Energy conservation in wireless sensor networks , 2007 .

[7]  Ossama Younis,et al.  Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach , 2004, IEEE INFOCOM 2004.

[8]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[9]  Tzu-Jane Tsai,et al.  A access-based clustering protocol for multihop wireless ad hoc networks , 2001, IEEE J. Sel. Areas Commun..

[10]  Krishna M. Sivalingam,et al.  Data Gathering Algorithms in Sensor Networks Using Energy Metrics , 2002, IEEE Trans. Parallel Distributed Syst..

[11]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[12]  Mahdi Lotfinezhad,et al.  Effect of partially correlated data on clustering in wireless sensor networks , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..