A Cognitive Multi-hop Clustering Approach for Wireless Sensor Networks

AbstractWireless sensor networks (WSNs) exert a pull on the modern research community towards many design challenges, especially, constraints on their lifetimes. Solutions proposed to save energy in WSNs posses their own merits and limitations. The trends evolved from the perspective of improving performance and scalability of conventional clustering approaches. They emerge by adopting cognitive techniques to handle uncertainty and instability present in the application atmosphere. This paper proposes a clustering approach for WSNs, namely, energy aware fuzzy clustering algorithm (EAFCA) which achieves lifetime enhancement in CH election, data aggregation and inter-cluster traffic phases of a multi-hop WSN environment. This algorithm contributes the process of cluster head (CH) election in a cluster in an energy-efficient manner by considering the residual energy, mean distance to 1-hop neighbors and 2-hop coverage of the competing nodes. The elected CH aggregates the data from all the sensor nodes of its cluster and forwards the same to the base station. Performance evaluation of the proposed EAFCA is done with popular clustering algorithms and the experimental results show improvement in terms of lifetime of WSNs under first node dies and half of the nodes alive scenarios.

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