The Internet of Drones (IoD) has latterly started to gear up its applications in diverse sectors of the society as a result of high adaptability and adjustability to new circumstances. Security, privacy, and storage issues still remain as major barriers for next-generation IoD systems to meet their general applicability requirements, even though many one-key-for-all static authentication and append-only blockchain-assisted systems have been proposed by the IoD community. First, bearing channel bandwidth and drones’ resource constraints in mind, authentication protocols with less computation and communication overhead are preferable. Second, the IoD drones might collect different types of data simultaneously, a unique secret session key for each type of data is needed to prevent data leakage from unauthorized parties. Third, the permanent storage of each drone’s cryptographic and task information on the append-only blockchain raises significantly alert after a long period of operation and/or an exponential growth of drones. Motivated by the research challenges presented above, we propose a redactable blockchain-assisted application-aware authentication system, also referred to as ReBAS, for next-generation IoD applications, where the drones shuttle back and forth between different flying zones to collect diverse types of data. The Chebyshev polynomial, redactable consortium blockchain, and chameleon hash function are adopted to significantly minimize the computational, communication, and storage overheads of cryptography-related operations. According to the security verification, and formal and informal security analysis, the ReBAS not only guarantees secure and dynamic authenticated key establishment but also is in compliance with the security requirements of Canetti-Krawczyk adversarial framework. We also develop a rigorous simulation framework and conduct an extensive comparative study. The experimental results demonstrate that the ReBAS can minimize the overheads in computation, communication, and storage while enhancing scalability.

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

This paper introduces a state-of-the-art Deep Reinforcement Learning (DRL) model designed for IoT devices in Multi-Access Edge Computing (MEC), with a focus on urban accident prediction. The Edge server serves as a central hub, collecting reinforcement learning data from distributed IoT devices and distributing custom deep learning models for reinforcement learning back to these devices, all within the network. The study also addresses privacy concerns by combining Differential Privacy (DP) with Federated Learning (FL) in DRL within MEC environments. DRL combines deep learning and reinforcement learning principles, enabling wireless IoT devices to optimize actions for maximum rewards in their environments. The surge in IoT devices in networks not only generates massive data but also raises privacy concerns. Differential Privacy, rooted in Google's FL technology, safeguards data privacy. IoT devices utilize DRL and FL to improve learning efficiency and uphold privacy policies. The paper outlines how wireless IoT devices accumulate traceable data tables through reinforcement learning within a Grid-world framework. Within the MEC architecture, Edge servers efficiently execute deep neural networks tailored for reinforcement learning data and distribute results to other IoT devices upon request. This paper presents a tailored DRL model for MEC and a federated DRL framework for maintaining differential privacy while handling shared data generated through deep learning in Edge servers.

This paper presents a quantitative model to assess the performance of a NFT (Non-Fungible Token)-centered chain as referred to as a NFT Chain in this paper. The model was introduced in [17] and more extensive simulations are conducted and the results are presented in this work. NFT chain in general stores its data distributed across on chain (e.g., NFT registration ledger data and an address pointing at the data located off chain such as meta data table and ultimate digital asset's data) due to the high cost to store the potentially high volume of data for digital assets. Therefore, it is expected that the overall performance of NFT chain is primarily to be dominated and bound by the off-chain performance. The proposed performance model employing an embedded Markovian queueing process model, tracks a bivariate state of the NFT chain such that \(\left(\varvec{i},\varvec{j}\right)\) where \(\varvec{i}\) stochastically tracks the number of slots of the transactions executed on chain and \(\varvec{j}\) stochastically tracks the number of transactions off chain as well, and the states transition as determined by \({\varvec{\lambda }}_{\varvec{o}\varvec{n}}\), \({\varvec{\lambda }}_{\varvec{o}\varvec{f}\varvec{f}}\), \(\varvec{\mu }\), and the number of slots in the current block. Extensive numerical simulations are performed to validate the efficacy of the model. The primary set of variables used in the simulations consists of \({\varvec{\lambda }}_{\varvec{o}\varvec{n}}\), \({\varvec{\lambda }}_{\varvec{o}\varvec{f}\varvec{f}}\), \(\varvec{\mu }\) and the average number of slots of the transactions during a block posting, \(\varvec{L}\), is simulated based on both \({\varvec{L}}_{\varvec{o}\varvec{n}}\)and \({\varvec{L}}_{\varvec{o}\varvec{f}\varvec{f}}\); and the average waiting time \(\varvec{W}\) based on both \({\varvec{W}}_{\varvec{o}\varvec{n}}\)and \({\varvec{W}}_{\varvec{o}\varvec{f}\varvec{f}}\), in an intermingled manner in order to take into account of the nature of NFT transactions executed across on- and off-chain without loss of generality. The simulation results in [17] has demonstrated a good agreement with the expected and intuitive trends. The results of more extensive simulations are presented in this paper to further demonstrate the efficacy and versatility of the proposed model. Ultimately, the proposed NFT chain model will serve as a sound theoretical foundation for the design of NFT chains from the performance's perspective.