CME: A Contour Mapping Engine in Wireless Sensor Networks

Contour maps, showing topological distribution of extracted features, are crucial for many applications. Building a dynamic contour map in a wireless sensor network is a challenging task due to the constrained network resources. In this paper, we present the design of a contour mapping engine (CME) in wireless sensor networks. Our design incorporates in-network processing based on binary classification to reduce the total number of active nodes. The underlying network architecture is analyzed to derive an optimal configuration. We show, by extensive simulations, the superiority of CME over the state-of-the-art contour mapping techniques.

[1]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[2]  Deborah Estrin,et al.  Next Century Challenges: Mobile Networking for Smart Dust , 1999, MobiCom 1999.

[3]  Yunhao Liu,et al.  Contour map matching for event detection in sensor networks , 2006, SIGMOD Conference.

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

[5]  Dimitrios Gunopulos,et al.  Incremental support vector machine construction , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[6]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[7]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[8]  Wei Hong,et al.  Beyond Average: Toward Sophisticated Sensing with Queries , 2003, IPSN.

[9]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[10]  Jun Yang,et al.  Constraint chaining: on energy-efficient continuous monitoring in sensor networks , 2006, SIGMOD Conference.

[11]  Randy H. Katz,et al.  Next century challenges: mobile networking for “Smart Dust” , 1999, MobiCom.

[12]  Ambuj K. Singh,et al.  Distributed Spatial Clustering in Sensor Networks , 2006, EDBT.

[13]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

[14]  Katia Obraczka,et al.  Isolines: energy-efficient mapping in sensor networks , 2005, 10th IEEE Symposium on Computers and Communications (ISCC'05).

[15]  Alfred O. Hero,et al.  Relative location estimation in wireless sensor networks , 2003, IEEE Trans. Signal Process..

[16]  Andreas Savvides,et al.  TASC: topology adaptive spatial clustering for sensor networks , 2005, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005..

[17]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[18]  Tomasz Imielinski,et al.  DataSpace: querying and monitoring deeply networked collections in physical space , 2000, IEEE Wirel. Commun..

[19]  Li Li,et al.  Contour maps: Monitoring and diagnosis in sensor networks , 2006, Comput. Networks.

[20]  Gregory J. Pottie,et al.  Instrumenting the world with wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[21]  Katia Obraczka,et al.  Efficient continuous mapping in sensor networks using isolines , 2005, The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services.

[22]  Christos Faloutsos,et al.  An environmental sensor network to determine drinking water quality and security , 2003, SGMD.

[23]  Mohammad Ilyas,et al.  Smart Dust , 2006 .