IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

Wireless sensor networks is composed of thousands of nodes that are deployed in large geographical area and these sensor nodes are small, wireless and battery powered. Wireless Sensor Network have wide range of applications but they are conquered with many challenging issues and complications that need to be addressed. The energy consumption of the nodes and the extension of the Network lifetime are the main challenges and the most significant features of the routing protocol in order to make it suitable, effective and energy efficient. As the sensor nodes are battery powered in this paper we propose an energy efficient routing power management technique which uses the concept of Time to Live TTL, 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 backbone 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 Quality of services QoS parameters like Energy Consumption, throughput, delay.