Optimizing the energy consumption and lifetime improvement in wireless sensor network using genetic algorithm

Abstract

In the 21st Century, Wireless Sensor Networks has gained the attention of the researchers due to its extensive application in the field of medicine, defence and agriculture. The sensor network is designed with battery-driven nodes. The energy of the nodes is limited. The improvement of the lifetime of the sensor is a subject of great importance in the field of research. This research work highlights the optimisation of energy and lifetime improvement in the field of medicine. WSN plays a significant role in designing a telemedicine scheme for gathering information from various types of patients in the disaster area. For communicating with various health centres in the network, the node should have a large volume of energy. The main goal is energy optimisation. Since the node is managed by power, it is difficult to transmit a large volume of data. Milestones have been reached effectively through the use of novel algorithms like Network Clustering using Non-border CH oriented genetic algorithms, fuzzy rules and Kernel FCM, High Gain MDC algorithm, Critical node handling using shifting and limiting of job. Technologies used for the elongation of the lifetime of the network and minimising the energy consumption are clustering of nodes, compression of medical images and critical state energy management. newline