Scalable Multi-Clustering Aggregation Scheme in WSN Using Machine Learning

Abstract

Due to Limited resource constraints in WSN data packets collide while being routed to sink, redundant data can be eliminated by data aggregation, which minimizes overall amount of data transferred and increases network's lifespan. Minimizing energy consumption and boost data aggregation rate is most crucial factor in WSN. A Scalable Multi-Clustering Aggregation utilizing Machine Learning (SMCA-ML) focuses on data aggregation approach of heterogeneous wireless sensor networks, using neurons as wireless sensor network nodes in a machine learning method. Machine Learning method accumulates the captured data collected by senor nodes and integrates the accumulated data with multi-clustering route. Threshold value of hidden layer and weight of input layer are randomly generated by the proposed method prior to training. This results in an unstable output that affects the efficiency of data aggregation and causes a long delay. More crucially, distinct threshold settings were made in accordance with the features of uneven energy consumption in wireless sensor networks (WSNs) allow data packets more swiftly by setting smaller threshold in far sink with enough energy. To maximize data aggregation, close sink area with tight energy employs a greater threshold. This way, the combination can result in high data fusion, efficient energy consumption, and little delay. The results of simulation suggested that SMCA-ML based data aggregation algorithm can significantly extend lifespan of network, substantially decrease energy consumption, enhance network energy, expand network performance and improve data aggregation efficiency when compared to conventional Stable Election Protocol (SEP), Back Propagation algorithm, and Extreme Learning Machine.

Keywords

• : Data Aggregation
• Machine Learning
• Energy Efficient
• Network life span
• Clustering
• reliability

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