AbstractThe cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.

AbstractThe smart grid network (SGN) is expected to leverage advances in the Internet of Things (IoT) to enable effective delivery and monitoring of energy. By integrating communication, computing, and information tools like smart sensors and meters to facilitate the process of monitoring, predictions, and management of power usage, the SGN can improve competence of power‐grid architecture. However, the effective deployment of IoT‐powered SGNs hinges on the deployment of strong security protocols. With the advent of quantum computers, classic cryptographic algorithms based on integer factorization and the Diffie‐Hellman assumptions may not be suitable to secure the sensitive data of SGNs. Therefore, in this paper, a secure quantum‐safe mutual authentication and key‐exchange (MAKe) mechanism is proposed for SGNs, that make use of the hard assumptions of small integer solution and inhomogeneous small integer solution problems of lattice. The proposed protocol is intended to offer confidentiality, anonymity, and hashed‐based mutual authentication with a key‐exchange agreement. Similarly, this scheme allows creation and validation of the mutual trust among the smart‐meters (SMs) and neighbourhood‐area network gateway over an insecure wireless channel. A random oracle model is then used to perform the formal security analysis of the proposed approach. A thorough formal analysis demonstrates proposed algorithm's ability to withstand various known attacks. The performance analysis shows that the proposed approach outperforms other comparative schemes with respect to at least 22.07% of minimal energy utilization, 51.48% effective storage and communications costs, as well as 76.28% computational costs, and thus suitable for resource‐constrained SGNs.

AbstractCooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

AbstractThe Internet of Medical Things (IoMT) has revolutionized the healthcare industry by allowing remote monitoring of patients suffering from chronic diseases. However, security concerns arise due to the potential life‐threatening damage that can be caused by attacks on IoMT devices. To enhance the security of IoMT devices, researchers propose the use of novel artificial intelligence‐based intrusion detection techniques. This article presents a hybrid alex net model and an orthogonal opposition‐based learning Yin‐Yang‐pair optimization (OOYO) optimized attention‐based Peephole long short term memory (PLSTM) model to distinguish between malicious and normal network traffic in the IoMT environment. To improve the scalability of the model in handling the random and dynamic behavior of malicious attacks, the hyper parameters of the PLSTM framework are optimized using the OOYO algorithm. The proposed model is evaluated on different IoT benchmark datasets such as N‐BaIoT and IoT healthcare security. Experimental results demonstrate that the proposed model provides a classification accuracy of 99% and 98% on the healthcare security and N‐BaIoT datasets, respectively. Moreover, the proposed model exhibits high generalization ability for multi‐class classifications and is effective in reducing the false discovery rate. Overall, the proposed model achieves high accuracy, scalability, and generalization ability in identifying malicious traffic, which can help improve the security solution of IoMT devices.

AbstractMany different sectors are beginning to deploy cyber‐physical systems (CPS) and industrial Internet of Things (IIoT) devices, including wearables, sensors, and critical infrastructure. In order to ensure that the device's data remains confidential, it must be encrypted prior to transmission. However, obtaining authenticity and integrity in IIoT data is also crucial, and signcryption has been proposed as a solution to this challenge. An additional obstacle is how to search for signcrypted cloud data using plaintext keywords. In this paper, we suggest a plaintext‐checkable identity‐based signcryption for CPS towards IIoT (IBSC‐PCE), which enables plaintext searching of signcrypted cloud‐based data. We provide both confidentiality and integrity and also demonstrate the security of our approach under the q‐Diffie‐Hellman inversion problem (q‐DHIP). This problem has been shown to be secure in the random oracle model (ROM), which further reinforces the robustness of our approach. Compared to current state‐of‐the‐art methods, our proposed approach outperforms them.

AbstractIn this paper, multi‐users (MU) communications are considered based on the cognitive ratio (CR) technique. Multiple‐input multiple‐output (MIMO) and non‐orthogonal multiple access (NOMA) are proposed to be merged with CR for the sake of improving the achievable throughput and satisfying user fairness. Moreover, utilizing MIMO‐NOMA enables the CR users to be served in the coverage network without the need for spectral sensing. Three scenarios are assumed in this paper to investigate the performance of each CR secondary user (SU) in the presence of single or multiple primary users (PUs). Pairing algorithms are proposed and applied to obtain optimum throughputs and outage probabilities, in which the algorithms are employed to select a PU with an SU to be coupled in a pair, depending on allocating the required power for each user, taking into account that any PU has the priority to reach its required throughput. Moreover, mathematical expressions have been derived for each case to evaluate the required power allocation factor and the achievable throughput for each user. Furthermore, the proposed algorithms show successful and satisfactory achievable rates and outage probabilities.

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

AbstractIn this paper, making full use of the spatial domain of transmit antennas (TAs) and keeping the characteristic of the squared minimum Euclidean distance (MED) between the transmitted spatial vectors (TSVs), generalized enhanced spatial modulation with signal points allocation (GESM‐SPA) is proposed to expand the size of signal spaces for enhancing the spectral efficiency and the reliability of communications. In the GESM‐SPA, according to the number of active TAs, signal constellation points (CPs) from the QAM or secondary QAM constellations are allocated and then modulated on the corresponding active TAs with the selected antenna index (AI) vector. Through this design, which further exploits the spatial domain with the variability of active TAs, the squared MED between the TSVs is increased in comparison with the existing traditional systems. More specifically, in view of the disadvantage of the classic ESM system, the constellation groups (CGs) with priority given to the QAM CPs are constructed to further maximize the squared MED. Then, the AI vector subsets corresponding to the obtained CGs are designed to be candidate for the specified AI vector set with the AI information. The squared MED and the average bit error probability (BEP) are analyzed. In simulation results using Monte Carlo, the GESM‐SPA outperforms the existing classic systems in terms of the bit error rate performance.

AbstractNature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.