Maximizing the Lifetime of Wireless Sensor Networks Using ERPMT
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : Monica Nagdive , Prof. Avinash Agrawal
Monica Nagdive , Prof. Avinash Agrawal . "Maximizing the Lifetime of Wireless Sensor Networks Using ERPMT", International Journal of Engineering Trends and Technology (IJETT), V11(10),498-501 May 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
A sensor mote is a small node with the capabilities such as sensing computation, wireless communication. By networking large numbers of sensor nodes, it is possible to obtain data about physical and environmental phenomena. These networks work for very long duration from several months to years. In these applications, sensor nodes use batteries as the sole power source. Therefore, energy efficiency becomes critical issue. In this paper, I present a novel sleep-scheduling technique called Virtual Backbone Scheduling (VBS) in combination with Remote differential Compression (RDC) Algorithm and ERPMT (Efficient Routing Power Management Technique) method. VBS forms a multiple overlapped backbones which work alternatively to prolong the network lifetime. The rotation of multiple backbones makes sure that the energy consumption of all sensor nodes is equal and balanced. The RDC algorithm is used to compress the transmitting data along the backbone. ERPMT method is used to divide the node energy into two ratios one is self-generated data by node and other for the data obtained from other working sensor nodes which fully utilizes the energy and achieves a longer network lifetime compared to the existing techniques. The scheduling problem of VBS is formulated as the Approximation algorithm based on the Schedule Transition Graph (STG) is used to estimate the problem of maximum Lifetime Backbone Scheduling. Basically the comparison is made in between the power consumption of network with and without using the energy efficient routing power management method. The result can be shown by using NS2 simulator The performance is evaluated by considering the QoS parameters like data rate, packet loss ratio, throughput, delay, energy.
 Jim Kurose and Keith Ross. „?Computer Networking: A Top Down Approach Featuring the Internet’’, 3rd edition. Addison-Wesley, July 2004.
 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci.?? A survey on sensor networks’’, IEEE Communications Magazine, vol. 40, no. 8, pp. 102-114, Aug. 2002.
 S. Lindsey and C. S. Raghavendra.” Pegasis: Power-efficient gathering in sensor information system”s, in Proceedings of IEEE Aerospace Conference, Big Sky, MT, Mar.2002.
 R. Cohen and B. Kapchits, “An optimal wake-up scheduling algorithm for minimizing energy consumption while limiting maximum delay in a mesh sensor network,” IEEE/ACM Trans. on Netw. , vol. 17, no. 2, pp. 570–581, 2009.
 M. J. Miller and N. H. Vaidya, “Power save mechanisms for multi-hop wireless networks,” in Proc. of BROADNETS’ 04 , pp. 104–114, 2004.
 C. Misra and R. Mandal, “Rotation of CDS via Connected Domatic Partition in Ad Hoc Sensor Networks,” IEEE Trans. Mobile Computing, vol. 8, no. 4, pp. 488-499, Apr2009.
 S.Sudha Praba, “WSN Life Time Maximization using Virtual Backbone and ERPMT Techniques”, International Journal of Engineering Sciences & Research Technology, June 2013
Virtual Backbone Scheduling (VBS), Schedule Transition Graph (STG), Efficient power management technic (ERPMT), Connection Dominating Set(CDS), Remote Differential Compression (RDC) Algorithm.