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The modern wireless sensor network is focused on integrating the emerging technology of IoT with the real world for enhanced data processing to improve automation and communication more smartly and safely. In the present industrial upgradation with IoT application greatly proved its positive influences on the operation and management phases along with progressive adoption of cloud computing and big data analytics. In Industry, smart monitoring and distributed control of entire architecture are hold up with the renovated technology of the Internet of Things (IoT). It lies on the top layer of the wireless communication network to afford enhanced connectivity among smart wireless or wired sensor with the embedded modern controller to the cloud server. In IoT application, real-time data acquisition is done securely and transmitted to the data analytics services to identify any catastrophic situation existence. It is reliable and moved industrial automation to the next step evolution of formulating to make proactive decisions to decide and react to the industrial variation constraints. Though modernization has shifted to analog to the digital generation, it is interoperable with available protocols and data standardizations in industrial sectors. Hence this paper enumerates the detailed configurations and features of IoT application involved in the modern communication platform towards the development of smart cities by incorporating data mining using AI (Artificial Intelligence). Further, the trending overview of Industrial IoT application in risk-based environments with complete knowledge-based architecture with incorporated wireless technology and protocols are outlined. [ABSTRACT FROM AUTHOR]

"Pipeline integrity" implies the concepts of failure prevention, inspection and repair that help inspection engineer to maximize their safety. With the entire circumstance built on the reliable pipeline, its integrity is receiving more attention than ever of its high-tech tubular structure with modeling and monitoring. In the modern pipeline infrastructure, regulating the pressure and flow rate throughout transportation plays a crucial role with tedious data frameworks. To afford a suitable data fusion platform for IoT-based monitoring and control, Model Predictive Control is formulated. Since the different models can extract a variety of field sensors data may result in inconsistent results in the decision process. In the proposed work, data fusion is integrated with model predictive control to improve the robustness by tackling different scenario risk rates to formulate the final model for real-time implementation. The accurate models are selected based on the scheduled task identification and risk probability characteristics in the oil pipelines. The final control signal will be actuated based on the multi-criteria decision-making method using cloud server data with field PID control signal rates. The proposed prediction model is capable of predicting the oil pipeline leaks and blocks several steps ahead which are used in the optimization part of the controller. Finally, the final output illustrates the effectiveness of the proposed controller with a simulation example, employing real fluid pipeline pressure and flow rate data under a variety of hard constraints. [ABSTRACT FROM AUTHOR]

• IoT and Machine learning incorporated online monitoring and control of fluid pipelines. • Narrow Band IoT module to acquire selected sensor data for concise fluid tpipeline performance evaluaition. • Performance of SCADA with IoT is compared with SCADA without IoT. • Real-time results confirm LQR-PID controller assures perfect control on pressure and flowrate. • Based on K-means clustering computing result online server initiates control action during leaks and cracks in the fluid pipelines. It is important to concentrate on monitoring and control of the pipeline transportation system before the failure resulting in fatal accidents. To enhance the supervision performances, the SCADA (Supervisory Control and Data Acquisition) platform is incorporated with IoT by utilizing the NB-IOT module holding a high-level engineering interface. In the proposed methodology, SCADA with the LQR-PID controller serves as Local Intelligence. When the local intelligence fails to react proactively during risk occurrences, immediately its performance is deactivated by the webserver through the NB (Narrow Band)-IoT module. For experimental real-time validation of the proposed work, a lab-scale DCS (Distributed Control System) based fluid transportation system is undertaken where flow and pressure prevail to be the most influencing parameters during risk occurrences in the pipelines. Also, the performance analyses are validated experimentally using unsupervised K-means clustering to identify abnormality caused by blockage and crack in the pipeline on the cloud-stored data. Image, graphical abstract [ABSTRACT FROM AUTHOR]