• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

CCAJS: A Novel Connect Coverage Algorithm Based on Joint Sensing Model for Wireless Sensor Networks


Abstract

This paper discusses how to effectively guarantee the coverage and connectivity quality of wireless sensor networks when joint perception model is used for the nodes whose communication ranges are multi-level adjustable in the absence of position information. A Connect Coverage Algorithm Based on Joint Sensing model (CCAJS) is proposed, with which least working nodes are chosen based on probability model ensuring the coverage quality of the network. The algorithm can balance the position distribution of selected working nodes as far as possible, as well as reduce the overall energy consumption of the whole network. The simulation results show that, less working nodes are needed to ensure the coverage quality of networks using joint perception model than using the binary perception model. CCAJS can not only satisfy expected coverage quality and connectivity, but also decrease the energy consumption, thereby prolonging the network lifetime.


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Cite this article

[IEEE Style]
Z. Sun, Y. Yun, H. Song, H. Wang, "CCAJS: A Novel Connect Coverage Algorithm Based on Joint Sensing Model for Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 10, no. 10, pp. 5014-5034, 2016. DOI: 10.3837/tiis.2016.10.020.

[ACM Style]
Zeyu Sun, Yali Yun, Houbing Song, and Huihui Wang. 2016. CCAJS: A Novel Connect Coverage Algorithm Based on Joint Sensing Model for Wireless Sensor Networks. KSII Transactions on Internet and Information Systems, 10, 10, (2016), 5014-5034. DOI: 10.3837/tiis.2016.10.020.

[BibTeX Style]
@article{tiis:21254, title="CCAJS: A Novel Connect Coverage Algorithm Based on Joint Sensing Model for Wireless Sensor Networks", author="Zeyu Sun and Yali Yun and Houbing Song and Huihui Wang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2016.10.020}, volume={10}, number={10}, year="2016", month={October}, pages={5014-5034}}