An investigation on real-time insights: enhancing process control with IoT-enabled sensor networks

Pandey, Shivam and Chaudhary, Mahi and Tóth, Zsolt György (2025) An investigation on real-time insights: enhancing process control with IoT-enabled sensor networks. DISCOVER INTERNET OF THINGS, 5 (1). ISSN 2730-7239

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Abstract

The Internet of Things (IoT) and sensor networks have significantly advanced process monitoring and control in multiple sectors, including manufacturing, agriculture, healthcare, and smart cities. Given the substantial volume of IoT-generated data, design-oriented solutions are necessary to accelerate data processing and improve scalability and flexibility. This article provides a comprehensive examination of the principal applications, technology, challenges, and future trajectories of IoT and sensor networks in process control as the control and monitoring of processes across industries are being transformed by the accelerated development of the IoT and sensor networks. Nevertheless, its pervasive adoption is impeded by obstacles such as data overload, scalability, and energy efficiency. This study is an investigation that presents a comprehensive framework for the integration of IoT-enabled sensor networks into real-time process control systems, thereby addressing these issues. We illustrate methods to enhance the collection, processing, and decision-making processes by studying advanced communication protocols such as MQTT and CoAP and key enabling technology of sensor networks. By conducting case studies of smart cities, agriculture, healthcare, and production, this investigation has extensive implications, enabling various industries to achieve enhanced operational efficiencies, robust scalability, and improved safety. Despite significant breakthroughs, challenges such as security, energy efficiency, and scalability remain to be addressed. Finally, we have presented advanced technologies capable of addressing these challenges and shaping the trajectory of IoT-based process management systems, including block chain, 5G, and AI integration and discussed future implication of study.

Tudományterület / tudományág

engineering and technology > agricultural engineering
engineering and technology > material sciences and technologies

Faculty

Not relevant

Institution

Soproni Egyetem

Item Type:	Article
SWORD Depositor:	Teszt Sword
Depositing User:	Csaba Horváth
Identification Number:	MTMT:36034082
Date Deposited:	10 Apr 2025 10:31
Last Modified:	10 Apr 2025 10:31
URI:	http://publicatio.uni-sopron.hu/id/eprint/3583

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