Energy-efficient communication in wireless cable sensor networks

In this paper, we introduce a new type of sensor: cable sensor. Unlike traditional point sensors, this type of sensor has a rectangular sensing region with a processor installed on it to do processing and communication. The wireless network formed by this kind of sensor is called wireless cable sensor network (WCSN). We study energy-efficient communication algorithms in WCSNs. We address it in two ways: one is through reducing the total transmission power of processors while maintaining the connectivity of the network and the other is through scheduling cable sensors to let them take turns to go to sleep without affecting the coverage and connectivity of the network. In the first approach, we initially develop a distributed algorithm called DTRNG based on the relative neighbourhood graph. Later we enhance it to Algorithm determine the transmission power by removing the largest edge in CYCles (DTCYC). Mathematical proofs show that Algorithm DTCYC provides an optimal solution that can not only minimise the total processor transmission power but maintain the connectivity of the network as well. In the second approach, we propose a cable mode transition algorithm which determines the minimum number of active sensors to maintain K-coverage as well as K-connectivity required by the application. We discuss the relationship between coverage and connectivity and prove the theorems that lay the foundation for our algorithm. Simulation results demonstrate that our algorithm is efficient in saving energy.

[1]  David A. Patterson.  Rescuing our families, our neighbors, and ourselves , 2005, CACM.

[2]  Lui Sha,et al.  Design and analysis of an MST-based topology control algorithm , 2005, IEEE Trans. Wirel. Commun..

[3]  David Simplot-Ryl,et al.  Energy-efficient area monitoring for sensor networks , 2004, Computer.

[4]  Ding-Zhu Du,et al.  Improving Wireless Sensor Network Lifetime through Power Aware Organization , 2005, Wirel. Networks.

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

[6]  I.A. Essa,et al.  Ubiquitous sensing for smart and aware environments , 2000, IEEE Wirel. Commun..

[7]  Teresa H. Meng,et al.  Minimum energy mobile wireless networks , 1998, ICC '98. 1998 IEEE International Conference on Communications. Conference Record. Affiliated with SUPERCOMM'98 (Cat. No.98CH36220).

[8]  Jennifer C. Hou,et al.  Maintaining Sensing Coverage and Connectivity in Large Sensor Networks , 2005, Ad Hoc Sens. Wirel. Networks.

[9]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

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

[11]  Paolo Santi.  Topology control in wireless ad hoc and sensor networks , 2005 .

[12]  Lui Sha,et al.  Design and analysis of an MST-based topology control algorithm , 2003, IEEE Transactions on Wireless Communications.

[13]  Neil Johnson,et al.  A smart sensor to detect the falls of the elderly , 2004, IEEE Pervasive Computing.

[14]  Neil C. Rowe.  Efficient deployment of fiber-optic cable seismic sensors , 2010, Defense + Commercial Sensing.

[15]  Guoliang Xing,et al.  Integrated coverage and connectivity configuration in wireless sensor networks , 2003, SenSys '03.

[16]  Ivan Stojmenovic,et al.  Localized minimum-energy broadcasting in ad-hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[17]  Sergei N. Bespamyatnikh.  On Constructing Minimum Spanning Trees in Rk1 , 1997, Algorithmica.

[18]  Andrew Chi-Chih Yao,et al.  On Constructing Minimum Spanning Trees in k-Dimensional Spaces and Related Problems , 1977, SIAM J. Comput..

[19]  Jan M. Rabaey,et al.  PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking , 2000, Computer.