MNMU-RA: Most Nearest Most Used Routing Algorithm for Greening the Wireless Sensor Networks

Hafiz Bilal Khalil, Syed Jawad Hussain Zaidi

School of Electrical Engineering & Computer Sciences, National University of Sciences and Technology, Islamabad, Pakistan.

DOI: 10.4236/wsn.2012.46023                

Abstract

Wireless sensors are widely deployed in military and other organizations that significantly depend upon the sensed information in any emergency situation. One of the main designs issues of the wireless sensor network (WSN) is the conservation of energy which is directly proportional to the life of the networks. We propose most nearest most used routing algorithm (MNMU-RA) for ad-hoc WSNs which vitally plays an important role in energy conservation. We find the best location of MNMU node for energy harvesting by apply our algorithm. Our method involves the least number of nodes in transmission of data and set large number of nodes to sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and enhance the life of the network.

Keywords

Energy Efficiency; Wireless Sensor Networks; Routing

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	I. F. Akyildiz, T. Melodia and K. Chowdhury, “A Survey on Wireless Multimedia Sensor Networks,” Computer Networks, Vol. 51, No. 4, 2007, pp. 921-960. doi:10.1016/j.comnet.2006.10.002
[2]	J. M. Kahn, R. H. Katz and K. S. J. Pister, “Emerging Challenges: Mobile Networking for Smart Dust,” International Journal of Communication Networks, Vol. 2, No. 3, 2000, pp. 188-196.
[3]	M. Cardei and D. Z. Du, “Improving Wireless Sensor Network Lifetime through Power Aware Organization,” Wireless Networks, Vol. 11, No. 3, 2005, pp. 333-340. doi:10.1007/s11276-005-6615-6
[4]	Q. Hu and Z. Z. Tang, “An Adaptive Transmit Power Scheme for Wireless Sensor Networks,” 3rd IEEE International Conference on Ubi-Media Computing, Jinhua, 5-7 July 2010, pp. 12-16.
[5]	W. Ye, J. Heidemann and D. Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” Proceedings of the IEEE INFOCOM, New York, 23-27 June 2002, pp. 1567-1576.
[6]	W. Ye, J. Heidemann and D. Estrin, “Medium Access Control with Coordinated Adaptive Sleeping for Wireless Sensor Networks,” IEEE/ACM Transactions on Networking, Vol. 12, No. 3, 2004, pp. 493-506. doi:10.1109/TNET.2004.828953
[7]	Q. Hu and Z. Tang, “ATPM: An Energy Efficient MAC Protocol with Adaptive Transmit Power Scheme for Wireless Sensor Networks,” Journal of Multimedia, Vol. 6, No. 2, 2011, pp. 122-128. doi:10.4304/jmm.6.2.122-128
[8]	A. P. Abidoye and N. A. Azeez, “ANCAEE: A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks,” Journal of Wireless Sensor Networks, Vol. 3, No. 9, 2011, pp. 307-312. doi:10.4236/wsn.2011.39032
[9]	S. R. Gandham, M. Dawande, R. Prakash and S. Venkatesan, “Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations,” Global Telecommunications Conference, San Francisco, 1-5 December 2003, pp. 377-381.
[10]	M. A. M. Vieira, C. N. Coelho, D. C. Silva and J. M. Mata, “Survey on Wireless Sensor Network Devices,” Proceedings of IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’03), Lisbon, 16-19 September 2003, pp. 537-544.
[11]	J. Paradiso and T. Starner, “Energy Scavenging for Mobile and Wireless Electronics,” Pervasive Computing, Vol. 4, No. 1, 2005, pp. 18-27. doi:10.1109/MPRV.2005.9
[12]	V. Gungor and G. Hancke, “Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches,” IEEE Transactions on Industrial Electronics, Vol. 56, No. 10, 2009, pp. 4258-4265. doi:10.1109/TIE.2009.2015754
[13]	CrossBow. Mica2 Data Sheet. http://www.xbow.com/Products/Product_pdf_files/MICA%20data%20sheet.pdf