• Rahul K Ghotekar


A wireless sensor network is network form of sense compute, and communication elements which helps to monitor, events in a specified environment. Sensor nodes in wireless sensor network are depends on battery power they have limited transmission range that’s why energy efficiency plays a vital role to minimize the overhead through which the Network Lifetime can be achieved. The lifetime of network, depends on number of nodes, strength, coverage area and connectivity of nodes in the network. In our paper, we introduced an policy of  balancing  the energy consumption of sensor  nodes .Through this load balancing approach of sensor node we can easily divide the loads on particular sensor node and can increase the network life by increasing the power of battery, the proposed methodology of dynamic balancing the sensor node energy is been used for single path with least energy utilization is selected using probabilistic theory and comparing it with assumed threshold value ,hence concluded with the result.Index Terms— wireless sensor network, dynamic load balancing, sensor node, threshold, probabilistic


[1] Rahim Kacimi, Riadh Dhaou , André-Luc Beylot “Load balancing techniques for lifetime maximizing in wireless”, Ad Hoc Networks 11 (2013) 2172–2186.
[2] D.Sharmila, R.Sujitha & G.Rajkumar “On Improving the Lifetime of Wireless Sensor Networks Using Virtual Scheduling Backbone Replacement”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013).
[3] Yaxiong Zhao, Student Member, IEEE, Jie Wu, Fellow, IEEE, Feng Li, Member, IEEE, and Sanglu Lu, Member, IEEE “On Maximizing the Lifetime of Wireless Sensor Networks Using Virtual Backbone Scheduling”ieee transactions on parallel and distributed systems, vol. 23, no. 8, august 2012.
[4] Dipak Wajgi ,Dr.Nilesh singh V. Thakur “Load Balancing Algorithms in Wireless Sensor Network : A Survey” IRACST – International Journal of Computer Networks and Wireless Communications (IJCNWC), ISSN: 2250-3501Vol.2, No4, August 2012.
[5] Navneet Kaur, “Review on Load Balancing in Wireless Sensor Network” Volume 3, Issue 5, May 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.
[6] J´anos Levendovszky, K´alm´anTornai, GergelyTrepl´an, Andr´asOl´ah, “Novel load balancing algorithms ensuring uniform packet loss probabilities for WSN”, 978-1-4244-8331-0/11/$26.00 ©2011 IEEE.
[7] EndreL´aszl´o, K´alm´anTornai, GergelyTrepl´an , J´anos Levendovszky , “Novel Load Balancing Scheduling Algorithms for Wireless Sensor Networks” , CTRQ 2011 : The Fourth International Conference on Communication Theory, Reliability, and Quality of Service.
[8] F. Zhao, L.J. Guibas, Wireless Sensor Networks: An Information Processing Approach, Morgan Kaufmann Publishers, 2004.
[9] V. Raghunathan, C. Schurgers, S. Park, M.B. Srivastava, Energy-aware wireless microsensor networks, IEEE Signal Processing Magazine 19 (2) (2002) 40–50, Signal Processor (MC68175/D), Motorola, 1996.G. R. Faulhaber, “Design of service systems with priority reservation,” in Conf. Rec. 1995 IEEE Int. Conf. Communications, pp. 3–8.
[10] Y. Chen, Q. Zhao, on the lifetime of wireless sensor networks, IEEE Communications Letters 9 (11) (2005) 976–978, 10.1109/LCOMM.2005.11010.A. Karnik, “Performance of TCP congestion control with rate feedback:TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, Indian Institute ofScience, Bangalore, India, Jan. 1999.
[11] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless micro sensor networks, Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS’00), vol. 2, IEEE Computer Society, Washington, DC, USA, 2000, pp. 3005–3014.
How to Cite
GHOTEKAR, Rahul K. IMPROVE THE LIFETIME OF VARIABLE NODE POWER THROUGH LOAD BALANCING BY REDUCING THE OVERHEAD IN SENSOR NETWORK.. International Journal Of Emerging Technology and Computer Science, [S.l.], v. 1, n. 4, apr. 2017. ISSN 2455-9954. Available at: <>. Date accessed: 31 may 2020.


wireless sensor network; dynamic load balancing;p sensor node; threshold; probabilistic