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DEVELOPMENT OF A MODIFIED ENERGY-EFFICIENT CLUSTERING WITH SPLITTING AND MERGING FOR WIRELESS SENSOR NETWORKS USING CLUSTER-HEAD HANDOVER MECHANISM

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  • Abstract : Available
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  • Reference Style: APA
  • Recommended for : Student Researchers
  • NGN 5000

ABSTRACT

Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a Modified Energy Efficient Clustering with Splitting and Merging (๐‘š๐ธ๐ธ๐ถ๐‘†๐‘€) for WSNs using Cluster-Head Handover Mechanism was implemented in this research. This model used information of the residual energy of sensor nodes and a suitable Cluster Head (CH) handover threshold to minimize energy consumption in the network. A backup CH was incorporated into the model to take over the responsibilities of the CH once the CH handover threshold is reached. The energy consumed by nodeโ€™s amplifier was varied with its transmission distance. This work was carried out using MATLAB R2013a and the performance of the modified model was validated in terms of network lifetime and residual energy ratio with an existing energy reduction technique in a self-organized clustering of WSNs. Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicate a significant reduction in energy consumption of the network nodes. Also, scalability and robustness test were carried out with respect to network lifetime by randomly adding and removing a number of nodes from the network. Average improvements of 7.8%, and 10.3% were achieved for scalability and robustness test respectively. The results of this work showed that ๐‘š๐ธ๐ธ๐ถ๐‘†๐‘€ has a better residual energy ratio, scalability, robustness and a longer network lifetime when compared with an existing energy reduction technique in a self-organized clustering of WSNs.





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