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Network Lifetime Enhancement Using Energy Aware Sink and Node Relocation Min Hop Method for Wireless Sensor Network


Deepak V. Biradar and Dr.K.R. Nataraj
Abstract

The advances in the manufacturing technologies have led to development of smaller size nodes along with low cost and low power features. Wireless Sensor Network (WSN) contains spatially separated autonomous sensor nodes which can help to detect environment factors such as temperature, sound, pressure and transfer the detected information to the main location through multiple hops in the network. WSNs are used for many applications like terrestrial digital broadcasting systems, environment monitoring, RADAR Systems and many more. For WSN to operate for longer period of time energy is the most important constraint which is responsible for extending the Network Lifetime (NL). During data delivery the nodes which are closer to sink will have more energy usage and the energy levels will come down heavily for such nodes in comparison to other nodes and this process leads to reduction of NL. The nodes during the routing process can also become dead and create a hotspot zone. Using Mobile Sensor one can move out of hotspot zone. In order to reduce energy consumption for specific set of nodes sink relocation is used. In this paper we propose Energy Aware Sink Relocation and Node Relocation with Min Hop (EASNR-MH) for data transmission in WSNs in order to improve NL and Throughput. The proposed method will perform classification of neighbor nodes into high energy (Healthy) and low energy (Non-Healthy) nodes, regularly measures energy levels, adjusts transmission range, measures Time to Live (TTL) and performs sink movement. During the data transmission Multiple Input Multiple Output (MIMO) technology is used. The data is split into multiple streams at the sensor and then recombined at the receiver, with multiple data streams data packets which are lost/dropped can be reduced, overall throughput can be increased. The EASNR-MH is simulated using MATLAB and then compared with EASR, One Step and Stationary methods.

Volume 11 | 04-Special Issue

Pages: 1382-1399