Wireless Sensor Networks (WSNs) are the foundation for Internet of Things (IoT) applications, facilitating paramount applications in smart cities, industrial automation, and healthcare. WSNs in dynamic resource-constrained environments, nevertheless, continue to lag in energy efficiency, adaptability, and security. In this paper, we present a new architecture that combines Federated Reinforcement Learning (FRL) and Distributed Quantum Cryptography (DQC) to break through these constraints. FRL module endows sensor nodes with decentralized, privacy-preserving intelligence for adaptive energy consumption control, routing, and transmission power control. DQC module uses Quantum Key Distribution (QKD) for securing communications against classical and quantum eavesdroppers. The FRL-DQC solution is extensively verified by large-scale simulations under diverse network topologies, traffic models, and threat scenarios. Experimental results are accompanied by significant gains in energy efficiency, communication delay, and quantum-resistant security over traditional WSNs and centralized learning. As an open and dynamic solution, the FRL-DQC solution facilitates the deployment of secure, energy-efficient, and future-proof WSN infrastructures with the ability to provide resilient performance in dynamic and smart environments.
Wireless Sensor Networks (WSNs) are the foundation for Internet of Things (IoT) applications, facilitating paramount applications in smart cities, industrial automation, and healthcare. WSNs in dynamic resource-constrained environments, nevertheless, continue to lag in energy efficiency, adaptability, and security. In this paper, we present a new architecture that combines Federated Reinforcement Learning (FRL) and Distributed Quantum Cryptography (DQC) to break through these constraints. FRL module endows sensor nodes with decentralized, privacy-preserving intelligence for adaptive energy consumption control, routing, and transmission power control. DQC module uses Quantum Key Distribution (QKD) for securing communications against classical and quantum eavesdroppers. The FRL-DQC solution is extensively verified by large-scale simulations under diverse network topologies, traffic models, and threat scenarios. Experimental results are accompanied by significant gains in energy efficiency, communication delay, and quantum-resistant security over traditional WSNs and centralized learning. As an open and dynamic solution, the FRL-DQC solution facilitates the deployment of secure, energy-efficient, and future-proof WSN infrastructures with the ability to provide resilient performance in dynamic and smart environments.
The rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.
Greater security pledges, reduced latency, and increased energy efficiency are now more important than ever due to the Wireless Sensor Networks' (WSNs) explosive growth. Although they work well for many distributed systems, traditional cryptography techniques are slow for mission-critical sensor applications and are still susceptible to new quantum attackers. In order to improve WSN security and performance, this study proposes a unified framework that combines Quantum Key Distribution (QKD) with 5G communication capabilities. This expanded work builds on previous simulation studies before and after the third Doctoral Committee evaluation. It includes a refined 5G-enabled architecture, a lightweight QKD model, and extensive simulations that capture energy consumption, bandwidth variation, latency, Packet Delivery Ratio (PDR), and Quantum Bit Error Rate (QBER). The findings show that while QKD maintains robust security even under controlled levels of quantum noise, 5G-assisted WSNs greatly increase bandwidth usage and communication delay. The proposed 5G–QKD WSN consistently delivers decreased latency, stable PDR, and enhanced security without imposing severe energy penalties, according to comparative simulations versus classical LEACH and MULE-based baselines. These results lend credence to the viability of hybrid quantum-enhanced wireless networks for critical infrastructure monitoring, smart environments, and future extensive IoT deployments.
ABSTRACT The increasing complexity of smart grid IoT ecosystems demands security architectures capable of resisting quantum‐era threats, protecting data privacy, and scaling across large distributed infrastructures. This study introduces a novel hybrid security framework that integrates quantum cryptography, deep learning–based intrusion detection, and federated learning into a unified, high‐assurance design tailored for next‐generation smart grid environments. The architecture employs quantum key distribution (QKD) for secure key generation, an adaptive deep feature obfuscation layer to mitigate adversarial manipulation, and a privacy‐preserving federated learning pipeline that eliminates centralized data exposure. Simulation‐based evaluations demonstrate substantial performance gains, achieving a low quantum bit error rate (1.8%), high key‐generation throughput (4900 keys/s), low latency (18 ms), and intrusion detection accuracy of 98.7%, consistently outperforming conventional cryptographic and machine learning baselines. The framework further exhibits enhanced resilience against quantum‐based and adversarial attacks, with efficient performance maintained even under increasing network density. While real‐world deployment will require hardware‐in‐the‐loop validation and optimization for heterogeneous traffic conditions, the results indicate strong potential for securing future smart grid IoT infrastructures and supporting sustainable smart city applications.
Quantum Key Distribution (QKD) has the main advantage of providing unconditional security, which is a consequence of the fundamental principles of quantum mechanics. For decades, key quantum distribution system has been the technological heart of security systems. However, the existing real QKD approaches have performance limitations. The channel experiences interference, photon loss, and fluctuations in eavesdropping conditions that reduce the efficiency and reliability of key generation. To address these issues, a fuzzy reinforcement learning-enhanced quantum key distribution (FRL-QKD) system is proposed to obtain secure and adaptable quantum communications. The integrated network combines fuzzy logic with reinforcement learning (RL) to expedite the localization of the photon polarization basis selection, control of key rates, and identification of error correction thresholds for optimized key generation. Fuzzy inference mechanisms are utilized within the FRL-QKD model to interpret an ambiguous environment. This first-stage implementation of the symbiosis of quantum cryptography, fuzzy intelligence and adaptive learning is a potent resource for future quantum-secure communications.
The requirement of efficient security protocols to protect confidential information from ever evolving cyber threat has been highlighted by the fast implementation of wireless sensor network (WSN) in numerous important domains, including healthcare monitoring, environmental surveillance and military operations. Despite their feasibility, traditional cryptographic approaches are utilized behind the computing capability of recent attackers, including the possibility of future quantum computer attacks. In order to revolutionize WSN security, this study proposes utilizing quantum cryptography and other concepts from quantum computing into the existing framework. This research leads to develop a communication protocol that is extremely safe and against the cyber-attacks. This protocol utilizes the quantum key distribution (QKD) to protect data and granting its confidential. In addition to improving security, the new architectures begin with change that makes WSN more capable of dependable and secure operations in environment where data protection is essential. A complete framework for protecting WSNs from new cyber threats in the future while keeping data transmissions and efficient process with reliability is discussed in this study. This proposed method enhances the dependencies in WSN installation across various industries, which is important for applications that need strong security measures. Evaluation results shows that the proposed method outperformed all existing methods in terms of relevant metrics.
The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.