IOT-HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

Industrial 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. To overcome above research gaps, we shift to industrial 4.0, which combine IoT and mechanism learning for industrial monitor and manage. Here, we propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT-HML). The main goal of the research is to overcome the issues of information security and control systems by developing a hybrid machine learning technique. Compared to the existing AODV protocol, the proposed C-IWO based routing protocol outperformed efficiently in terms of 19.2% average delay, 12.7% average energy consumption, 10.26% average throughput, 3.8% average delivery ratio, and 16.33% average loss ratio, respectively. In addition, the accuracy 98.5%, sensitivity 97.3%, specificity 98.2%, precision 98.35%, recall 98.32%, and F-measure 97.49% of proposed CP-LNN technique is very high compare to obtainable state-of-art classifiers.