Volume 16, Issue 3, August 2025

Industrial IoT and Smart Manufacturing: Trends and Security Challenges

Nadia Tahseen, Saba Saleem, Junaid Arshad

Computer Science Department, University of Engineering & Technology, Lahore, Pakistan

Abstract- IIoT and smart manufacturing approach have provided industrial operation with real time data collection, predictive maintenance and automation. The work on IIoT (connected sensor, cloud computing, AI) improves the operational efficiency, reduces downtime and optimizes the effectively made decisions. Along with these comes the rise in the productivity and operational flexibility of manufacturing, which is basically some of the key trends of smart manufacturing — including digital twins, 5G connectivity, etc. On the other hand, there are some remaining problems like data security issues, integration complexity, high implementation cost and absence of workforce. All of these problems have various good reasons to have robust cybersecurity strategies, frameworks and to invest in upskilling of the workforce. IIoT is the basis of the paper which explores recent trends and challenges of smart manufacturing, the technology development and may be within the solution for a more resilient and more efficient industrial ecosystem.

Keywords- Industrial Internet of Things (IIoT), Smart Manufacturing, Digital Twins, Edge Computing, 5G Connectivity, Cybersecurity, Predictive Maintenance, Automation, and Operational Efficiency

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Towards Secure and Reliable Software in IoT Healthcare Systems: A Risk Management Perspective

Chinara Adebayo, Babatunde Damilola

Department of IT, University of Science and Technology, Nigeria

Abstract- The Internet of Things (IoT) is an emerging technology that facilitates data collection and communication between nodes using smart devices connected through various mediums. Numerous techniques have been implemented in IoT-based healthcare environments for collecting and utilizing patient data. However, managing security remains a major challenge in these systems. This research analyzes the existing security challenges and barriers in IoT healthcare environments and proposes solutions to address them. Since security is a critical concern in the entire system, the paper also presents future recommendations to enhance the security of IoT-based healthcare systems.

Keywords- Internet of Things (IoT), Security Threats, Challenges, Healthcare System and Solutions

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A Comprehensive Review on IoT Security: Attack Detection Mechanisms, Protocol Vulnerabilities, and Emerging Defensive Strategies

Ahsan Ikram

Institute of Data Science, University of Engineering and Technology (UET), Lahore, Pakistan

Abstract- In this article, I quickly analyze security vulnerabilities found in IoT systems, discussing protocol loopholes, attack surfaces, and countermeasures. The increasing reliance on Machine Learning and Deep Learning for the detection and mitigation of attacks such as DDoS, botnets, and MITM will also be traced throughout. Besides, the paper also discusses integrating Blockchain for secured communication and smart contracts, especially concerning resource-constrained devices. All these IoT domains, healthcare and smart grids, require customized, scalable, and adaptive security solutions. Hybrid approaches using lightweight encryption with anomaly detection will be necessary to secure IoT environments, as the paper suggests.

Keywords- IoT Security, vulnerabilities, Smart Grid and Secure Communication

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A Comprehensive Review of Distributed Systems in 5G and IoT: Trends, Challenges and Opportunities

Iqra Batool

Data Science Department, University of Engineering and Technology, Lahore, Pakistan

Abstract- With IoT and 5G being introduced, the goals and design of today’s computers have been altered significantly. Cloud architectures that use one central point for processing data are no longer capable of handling autonomous vehicles, smart manufacturing and healthcare conducted remotely. For this reason, edge computing, fog computing and hybrids of cloud and edge architecture have received more attention. They eliminate the need to send and receive data over long distances because processing is done nearer to the data, therefore speeding up connections. This paper looks at the research which focuses on how distributed computing systems for IoT and 5G have evolved, what they are capable of and where they have limitations. The study looks into important architectures, security structures, methods of organizing tasks and how AI helps manage workloads in a distributed system. Besides the positive aspects, it points out main issues, including the lack of energy, inconsistent standards, security risks and a demand for resources that can expand. To conclude, the paper shares key ideas for future work that will help design more flexible, protected and efficient systems for the upcoming interconnected world.

Keywords- Distributed Computing, Edge Computing, Fog Computing, 5G Networks, Internet of Things (IoT), Hybrid Architecture, Real-Time Systems, Cyber-Physical Systems, Orchestration and Scalability

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AI-Accelerated Architectures Overview: Emerging Trends, Challenges, and Revolutionary Hardware

Rimal Choudhry, M. Junaid

University of Engineering and Technology, Lahore

Abstract- The continuous changes in AI and ML have made it necessary to design specific hardware to deal with the higher computing needs of modern AI solutions. General-purpose processors are found to be inefficient for working on the difficult and large-scale operations in deep learning, neural networks and AI inference. For this reason, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs) and custom Application-Specific Integrated Circuits (ASICs) have become essential parts of modern AI. They are designed with architectures that support both parallel computation, ample memory and effective connectivity to give much better results in both speed and power efficiency. This paper gives an overview of AI-accelerated architectures and highlights what permits them to support AI workloads effectively. We work on solving issues in AI hardware, especially those related to scaling up, power use and designing systems at the hardware-software level. Furthermore, the role of programming models, software frameworks and performance benchmarking in better using AI accelerators across various sectors, including vehicles, robotics, medicine and edge systems is studied.

Keywords- Neural Networks, AI Inference, GPUs, ASICs, Programming Models and Software Frameworks

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