Improve WSN Lifetime Based on K-Means, Genetic Clusters, and Data Compression

Wireless Sensor Networks (WSNs) are used in many areas such as environmental monitoring and industrial process automation. One major challenge in Wireless Sensor Networks is the finite energy supply of the sensor nodes. As a result, many energy saving mechanisms need to be implemented. In this paper, we present a detailed methodology to improve the lifetime and energy saving of a WSN. Our method applies K-means clustering for the even distribution of sensor nodes for a balanced load and energy usage in the clusters. For optimal Cluster Head (CH) selection, a generic algorithm is used based on specific imperative criteria such as residual energy, the energy needed to communicate to

and from the Base Station (BS), and the distance to the BS. The system also incorporates a multi-hop data transfer method, where compressed data packets are sent to the BS. The nodes in multi-hop communication also serve as intermediary nodes to pass data and thereby significantly reduce the energy spent by each sensor device. The techniques used for data compression play an important role for maintaining the desired level of data and integrity while also minimizing the packet size and thereby help in improving the energy efficiency of the network for prolonged operation. The proposed method is scientifically proven to perform better with respect to energy consumption, network lifetime, and

delivery ratio when compared to existing WSN protocols.

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