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Author

V. Manasa

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Conference Aug 2026

Novel Cybersecurity and the Internet of Things (IoT) for Safeguarding Smart Grids: A Robust Architecture to Prevent Attacks on Specific Infrastructure

Energy networks are evolving into smart grids due to the expansion of the Internet of Things. Control is automated, monitored, and adjusted by intelligent network systems. While this method does enhance sustainability, efficiency, and reliability, it does so at the expense of cybersecurity. Critical infrastructure concerns have grown as a result of smart grids’ reliance on the Internet of Things. Smart meters, sensors, communication gateways, and controllers all fall under this category. Supply of electricity, confidentiality of data, and safety of the nation are all under risk from assaults on grid parts. To protect smart grid infrastructure from targeted attacks, this project employs an Internet of Things architecture driven by cybersecurity. Regions of the grid will be protected by multilayer security, advanced threat detection, and communication protocols. The most important things are ensuring the data is secure, authenticating devices, responding adaptively, and detecting anomalies in real-time. The smart grid’s visibility, resilience, and risk reduction are enhanced by a combination of centralized intelligence and decentralized security measures. Cybersecurity for smart grids is challenging because to the large amount of data, the number of devices involved, and the requirements for real-time operations. These concerns of cybersecurity based on the Internet of Things are addressed in this article. Scalability is ensured while infrastructure is protected. Sustainable, dependable, and environmentally friendly power systems are a result of secure IoT design.

Muruganantham Angamuthu, R. Alazaidah, Arumai Shiney et al. · 0 citations
Conference Aug 2026

Quantum Reinforcement Learning Driven Adaptive Resource Allocation for Internet of Things Devices

The high rate of Internet of Things (IoT) networks development has posed a serious problem of effective resource allocation because devices are heterogeneous, the traffic conditions are dynamic, and the energy and latency requirements are severe. Traditional resource allocation methods and classical reinforcement learning methods are not always the best methods to perform in a highly dynamic environment because they lack the adaptability and reduce convergence. The proposed paper introduces a Quantum Reinforcement Learning (QRL)-motivated adaptive resource allocation model which uses quantum-inspired state representation, as well as quantum-classical policy optimization, to optimize resource allocation. The proposed model is a dynamic distribution of bandwidth, transmission power, and computational resources the real-time state of network. Experimental assessment shows better performance than the traditional and classical reinforcement learning techniques. The proposed QRL framework attains the average accuracy of resource allocation 97.84%, lessens the communication latency by 43%, escalates the throughput by 64%, and lessens the energy usage by 37%. The system also has better convergence and stability exception when applied to different network load. Combination of quantum feature encoding improves efficacy in decision and learning. The findings affirm that the suggested framework offers a powerful and scalable system of smart resource management in the next-generation IoT systems.

A.Mohan Kumar, M. Al-Shalout, M. Elakiya et al. · 0 citations