Energy Efficient Routing using Machine Learning based Link Quality Estimation for WMSNs
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Abstract
In Wireless Multimedia Sensor Networks (WMSNs), Energy constraint battery-operated sensors collect surplus data from the target area, process them and transmit to the end server using the adopted routing approach. During this transmission, the quality of the multimedia data such as audio, scan images, recordings, live video streaming are get diminished by unbalanced, insecure low quality wireless links due to various causes. The design and implementation of energy efficient routing with suitable link quality estimation (LQE) model is an essential and challenging job in resource constrained WMSNs for upholding the energy of the network and affords Quality of Service.Many sophisticated LQE approaches for WMSNs have been proposed in recent days. In this paper an Energy Efficient Routing using Machine Learnig (EERML-LQE) technique for link quality prediction is proposed to hit the impact of Link Quality Analysis (LQA) using Gaussian Naive Bayes classification for predicting the link accuracy. Experimental results of the proposed routing protocol with machine learning based link quality estimation reveals that the EnRoML outperforms current routing approaches in terms of various network performance parameters like energy utilization, packet drop ratio, life time of the network and average packet latency
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