Fault-Resilient Wireless Platforms for Industrial IoT Applications
Hasan Muhammed AliiDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq; Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah iu.tech.eng.iu.comp.hassanaljawahry@gmail.com0009-0009-2714-1544
Dr.R. VelanganniAssistant Professor, Crescent School of Law, (Management Studies), BS Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu velangannijose78516@gmail.com0000-0003-4412-3689
M. MuthazhaguDepartment of Marine Engineering, AMET University, Kanathur, Tamil Nadu muthazhagumugilan@ametuniv.ac.in0009-0001-1792-8257
Dr. Arasuraja GanesanAssociate Professor, Department of Management Studies, St. Joseph’s Institute of Technology, OMR, Chennai, Tamil Nadu arasuraja.mba@gmail.com0000-0001-6137-1911
Dr.S.N.V.J. Devi KosuruAssistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh jyotsnakosuru@gmail.com0000-0003-1521-5701
The rapid development of IIoT applications has created unique challenges for wireless communication systems, particularly in contexts where dependability and redundancy are crucial. In these demanding industrial settings, the need for continuous system functions in data transfer, system health, and operations has given rise to fault-resilient wireless systems. This paper focuses on the design, issues, and performance of these systems in the context of IIoT. We address the need for fault tolerance in systems severely limited by electromagnetic interference, physical barriers, and power supply. Essential design criteria include protocol redundancy, adaptive routing, and automated fault identification, which are critical in averting the collapse of a given network. These concepts, which substantiate the increasing network challenges posed by the real-world applications of smart grids, manufacturing plants, and oil and gas domain operations, are documented in case studies. In addition, we provide estimates of system reliability, Latency, and energy efficiency to evaluate performance and contrast sponsored and unsupervised traditional wireless networks. Future work can build on these findings by incorporating AI, machine learning, and advanced cybersecurity tools to enhance fault resilience in wireless IoT systems. The paper concludes with a proposal for future work and the eradication of standardization barriers to facilitate broader cross-sector adaptation.