Abstract

The Wireless IoT sensor network (IWSN) has significant potential in industrial settings, but to fully realize its benefits, a robust and scalable computer system is required to handle the continuous influx of data from various applications. In this research study, we propose an IoT sensor-cloud architecture that integrates WSN with cloud technology, providing a unique data analytics framework for highly secure analysis of sensor data. The proposed architecture emphasizes effective interoperability mechanisms in the cloud, and provides an IPv6 extensible enterprise WSN design and simulation technique. To demonstrate the effectiveness of our proposed architecture, we track the pH, resistivity, and dissolved oxygen levels of industrial effluents that are discharged into water sources. We use AT instructions in conjunction with the HTTP GET technique to gather and upload detector data to the ThingSpeak cloud through a GPRS internet connection, enabling real-time online monitoring and control using IoT functionality. The proposed architecture uses a distributed approach to handle high volumes of incoming data from the IoT sensors, storing the data in a scalable and accessible way for analysis. Real-time analysis is performed using a combination of batch and stream processing frameworks and machine learning algorithms, and the results are visualized using a web-based dashboard that provides real-time updates on key metrics and allows users to explore the data in different ways. Security is a top priority in our proposed architecture, and we use encryption technologies such as SSL/TLS and access control mechanisms such as OAuth2 to ensure the secure transmission and storage of sensitive industrial IoT data. The architecture is designed to be scalable and adaptable to handle a wide range of IoT use cases in industrial settings. The proposed IoT sensor-cloud architecture provides a robust and scalable solution for the collection, analysis, and exchange of significant amounts of IoT sensor information, enabling real-time monitoring and control of critical environmental parameters in industrial settings.

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