Abstract
Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. They are deployed remotely in sever environment which doesn’t have an infrastructure. Energy is a limited resource that needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for longtime. The major objective of this research is to efficiently consume energy based on the Neuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The significance of this study is to examine the challenges of energy efficient algorithms and the network lifetime on WSN so that they can assist several applications. Clustering is one of the hierarchical based routing protocols, which manage the communication between sensor nodes and sink via Cluster Head (CH), CH is responsible to send and receive information from multiple sensor nodes and multiple base stations (BS). There are various algorithms that can efficiently select appropriate CH and localize the membership of cluster with fuzzy logic classification parameters to minimize periodic clustering which consumes more energy and we have applied neural network learning algorithm to learn various patterns based on the fuzzy rules and measured how much energy has saved from random clustering. Finally, we have compared to our Neuro-Fuzzy logic and consequently demonstrated that our Neuro-Fuzzy model outperforms than random model.
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