Mobile data gathering in wireless sensor networks by soft computing based ch selection and clustering

Abstract

Wireless sensor networks generally consist of static sensor node which can be deployed to monitor the environment The network is built by the sensor nodes and the data from the source reaches base station by passing through a number of sensor nodes This causes loss of energy at the sensor nodes To reduce energy loss of the sensor nodes in WSN cluster based topology can be used Sensor nodes are grouped into clusters Sensor nodes communicate with cluster head and cluster heads in turn send the collected data to the base station. This causes fast depletion of cluster heads energy Clustering, one of the basic data mining tasks is an unsupervised learning process aimed at discovering hidden patterns in data To overcome this a novel mobile data gathering in WSN by softcomputing based CH selection and clustering head selection for energyefficiency in heterogeneous are proposed in this paper A heterogeneous network uses a mixture of macro cells and small cells such as microcells picocells and femtocells These small cells can potentially improve spatial reuse and coverage by allowing future cellular systems to achieve higher data rates while retaining seamless connectivity and mobility in cellular networks It is based on fuzzy inference system Although the deployment of small cell networks is seen to be apromising way of catering to the ever increasing traffic demands the dense and random deployment of small cells and their uncoordinated operation raise important questions about the implication of energy efficiency in such multitier networks However only a small number of algorithms focus on heterogeneous network clustering which is a new topic that has only recently garnered significant attention newline