Energy Efficient Clustering and Data Aggregation Protocol using Machine Learning in Wireless Sensor Networks

Abstract

As the Wireless Sensor Networks (WSNs) are resource-constrained, energy-efficient data transmission required by considering various applications. The novel routing protocol designed to achieve the scalability, energy efficiency, QoS optimization with minimum overhead in this research called Strong Clustering Algorithm & Data Aggregation using Machine Learning (SCADA-ML). The SCADA-ML design is mainly based on the use of machine learning techniques for CH selection and robust data aggregation to minimize the energy consumption while maintaining the other performances for different size of WSNs. In the first contribution, we focused on optimal CH selection and cluster formation using the supervised ML technique called Artificial Neural Network (ANN). The problem of optimal CH selection for each cluster is formulated according to the architecture of ANN (input layer hidden layer, and output layer) in which the every sensor node properties such as residual energy, distance from the BS, and bandwidth allocated are processed as input to ANN. At CH node, there may be the possibility of redundant information, therefore in the second contribution the efficient data aggregation performed by CH node of each cluster to minimize the energy consumption using the Independent Component Analysis (ICA) ML technique. The clusters with similar data need to perform the data aggregation. As compared to other data aggregation methods, ICA is computation efficient and reduces the redundant data based on differential entropy. The experimental results show that SCADA-ML outperforms the existing ML-based clustering and data aggregation algorithms.