In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.
In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.
Sixth-generation (6G) networks offer unprecedented ultra-reliability, capacity, and low-latency communication. Nevertheless, the same developments also pose security threats to complex communication infrastructures that may be resistant to traditional cryptographic tools, especially in the context of quantum computing. Quantum Key Distribution (QKD) is an innovative technique for achieving unconditional security that leverages quantum-mechanical principles to share cryptographic keys securely. This paper discusses how QKD can be integrated into 6G and beyond networks and used to ensure the confidentiality and integrity of data across applications such as autonomous systems, the Internet of Things (IoT), and smart city infrastructure. This paper explores the architecture of QKD-based 6G communication, key-generation systems, and quantum-resistant network layers. It also examines scalability, synchronization, and hardware implementation, and suggests possible hybrid approaches for integrating classical and quantum security protocols. The results imply that the idea of QKD may become the foundation of safeguarded communication systems in the future, ensuring resilience against attacks in the quantum era and enabling individuals to trust the next generation of wireless.
Quantum Key Distribution (QKD) enables information-theoretic secure key exchange by exploiting fundamental quantum phenomena such as superposition and no-cloning. Classical QKD protocols (e.g., BB84) guarantee that any eavesdropping attempt introduces detectable disturbances, but they typically assume stable, high-quality channels and unconstrained devices. In contrast, Internet of Things (IoT) networks present a radically different environment: devices have limited processing power, minimal onboard memory, strict energy budgets, and communicate over highly variable wireless links subject to interference and rapid fading. These constraints lead to elevated quantum bit error rates (QBER), frequent key reconciliation failures, and prohibitive energy costs, all of which jeopardize practical QKD deployment. To address these challenges, we propose an AI-powered QKD protocol specifically optimized for IoT scenarios. Our approach integrates three AI modules: (1) a gradient-boosted regression channel estimator that predicts instantaneous link transmittance using lightweight sensor data and historical photon counts; (2) a reinforcement learning (RL) agent that adaptively tunes photon intensity, basis-selection probability, and reconciliation block size to balance key rate and error rate; and (3) a convolutional neural network (CNN) classifier that selects the optimal Low-Density Parity-Check (LDPC) code rate based on real-time noise characteristics. We simulate a star-topology IoT network of 50 battery-powered devices communicating over 5 km polarization-encoded links with realistic urban loss (0.1–0.3 dB/km) and environmental noise. Compared against a static BB84 baseline, our AI-enhanced protocol reduces average QBER from 4.5% to 2.9%, increases secure key rate from 12.5 kbps to 16.0 kbps, lowers latency by 22%, and reduces energy consumption per key bit by 18%. These improvements persist across channel conditions and device heterogeneity, demonstrating robustness.
The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
As we move toward the rollout of 6G networks, we're stepping into an era of groundbreaking technological advancements. These next-generation networks promise ultra-low latency and seamless integration with artificial intelligence (AI) and the Internet of Things (IoT), unlocking a new level of connectivity and performance. However, with these powerful capabilities come significant security challenges. Traditional encryption methods may not be enough to protect the complex and highly sensitive data traveling through 6G networks. One of the most promising solutions lies in Quantum Communication, particularly Quantum Key Distribution (QKD)-a method that leverages the laws of quantum mechanics to enable highly secure encryption. This paper explores the emerging security risks in both terrestrial and satellite communication channels and reviews current research on integrating quantum communication into 6G infrastructure. Special attention is given to its potential within the metaverse, a rapidly evolving digital environment that demands robust and future-proof security frameworks. Key areas of focus include-Addressing security vulnerabilities in quantum communication, Enhancing infrastructure to support quantum technologies, Reducing error rates in quantum protocols. the paper aims to propose a flexible, forward-looking framework designed to ensure fast, secure communication for critical sectors such as finance, defense, and healthcare.
Purpose The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD. Design/methodology/approach The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN). Findings Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection. Originality/value The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
The Internet of Things (IoT) connects billions of machines that can interact with each other. IoT is one of the fastest-growing areas in the history of computing, and will continue in this direction in the 6G era. New security problems have been raised, however, since implementing protection mechanisms for IoT devices, such as encryption, authentication, and so on, is inefficient, due to their inherent flaws. Therefore, a new method of protecting IoT devices needs to be sought. Quantum security depends on the natural physical phenomenon (quantum mechanics) and offers an appropriate and powerful security technique. This paper suggests a new approach for simulating the quantum key distribution between IoT devices and a server to encrypt the data sent to the server. It also demonstrates the simplicity of this new method, and its efficiency in producing a quantum key distribution (QKD) simulation. In addition, it describes the use of the final length key for symmetrical security for IoT devices. Moreover, the simulation of the attacker between the server and IoT devices is performed.