Modified Clustering Algorithm for Energy Efficiency Utilizing Fuzzy Logic in WSN (MCF)

Tuba Firdaus, Meenakshi Yadav


A number of research have taken place in the field of Wireless Sensor Networks (WSN) as there is continuous need of advancement in the field of wireless communication, digital technology and micro-electro-mechanical systems(MEMS) . So the need of growth of low cost, low power, multifunctional sensor nodes have been required. A Wireless Sensor Network is a collection of sensor nodes that have the capability of sensing any environmental phenomenon, processing that information and then sending that data to the base station. A single sensor node is not capable of capturing desired information from a particular region so a collection of nodes are arranged to get accurate and sufficient result. This collection of sensor nodes along with a base station will collaboratively form a network that is known as Wireless Sensor Network. As limited energy is one of the most important constraint of WSN so it must be assured that it is utilized in most efficient way. Clustering is best approach to remove redundant data transmission to base station. Each cluster has a cluster head that is responsible for transmitting data to base station for that cluster members. Cluster head (CH) collect the data from all members of its cluster and perform aggregation on these data to remove redundancy then send it to base station. So appropriate CH election is very important for improving efficiency. In this thesis we have presented a clustering approach that has taken a heterogeneous environment and uses fuzzy logic to elect CHs more efficiently. We have combined two parameters Distance and Residual Energy and apply fuzzy rules on that to find the priority of a node for being a CH. Simulation shows that using fuzzy logic in SEP (Stability Election Protocol) will improve the energy efficiency by providing better load distribution and utilizing the benefits of heterogeneity of network. We have shown our analysis on two parameters- Number of dead nodes and average energy of nodes


Sensor Nodes; Clustering; Energy Efficiency; Fuzzy Logic; Residual Energy Distance

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