Search Results (63)

The convergence of the Internet of Everything (IoE) and healthcare requires ultra-reliable, low-latency, and intelligent communication systems. Sixth-generation (6G) wireless networks, coupled with digital twin (DT) models and large AI models (LAMs), are envisioned to promise substantial and practically meaningful improvements in smart healthcare by enabling real-time monitoring, diagnosis, and personalized treatment. In this article, we propose an LAM-enhanced DT-driven network slicing framework for healthcare applications. The framework leverages large models to provide predictive insights and adaptive orchestration by creating virtual replicas of patients and medical devices that guide dynamic slice allocation. Reinforcement learning (RL) techniques are employed to optimize slice orchestration under uncertain traffic conditions, with LAMs augmenting decision-making through cognitive-level reasoning. Numerical results show that the proposed LAM–DT–RL framework reduces service-level agreement (SLA) violations by approximately 42–43% compared to a reinforcement-learning-only slicing strategy, while improving spectral efficiency and fairness among heterogeneous healthcare services. Finally, we outline open challenges and future research opportunities in integrating LAMs, DTs, and 6G for resilient healthcare IoE systems. Full article

The increasing number of intelligent connected vehicles (ICVs) is leading to a growing scarcity of spectrum resources for the Internet of Vehicles (IoV), which has created an urgent need for the use of full-duplex non-orthogonal multiple access (FD-NOMA) techniques in vehicle-to-everything (V2X) communications. Meanwhile, for the flexibility of autonomous aerial vehicles (AAVs), V2X communications assisted by AAVs are regarded as a potential solution to achieve reliable communication between ICVs. However, if the integration of FD-NOMA and AAVs can satisfy the requirements of V2X communications, then quickly and accurately analyzing the total achievable rate becomes a challenge. Motivated by the above, an accurate analytical expression for the total achievable rate over Rician fading channels is proposed to evaluate the transmission performance of NOMA-enhanced AAV-assisted IoV with imperfect channel state information (CSI). Then, we derive an approximate expression with the truncated error, based on which the closed-form expression for the approximate error is theoretically provided. Finally, the simulation results demonstrate the accuracy of the obtained approximate results, where the maximum approximate error does not exceed 0.5%. Moreover, the use of the FD-NOMA technique in AAV-assisted IoV can significantly improve the total achievable rate compared to existing work. Furthermore, the influence of key network parameters (e.g., the speed and Rician factor) on achievable rate is thoroughly discussed. Full article

Ontology technology addresses data heterogeneity challenges in Internet of Everything (IoE) systems enabled by Cyber Twin and 6G, yet the subjective nature of ontology engineering often leads to differing definitions of the same concept across ontologies, resulting in ontology heterogeneity. To solve this problem, this study introduces a hybrid ontology matching method that integrates a Recurrent Neural Network (RNN) with syntax-based analysis. The method first extracts representative entities by leveraging in-degree and out-degree information from ontological tree structures, which reduces training noise and improves model generalization. Next, a matching framework combining RNN and N-gram is designed: the RNN captures medium-distance dependencies and complex sequential patterns, supporting the dynamic optimization of embedding parameters and semantic feature extraction; the N-gram module further captures local information and relationships between adjacent characters, improving the coverage of matched entities. The experiments were conducted on the OAEI benchmark dataset, where the proposed method was compared with representative baseline methods from OAEI as well as a Transformer-based method. The results demonstrate that the proposed method achieved an 18.18% improvement in F-measure over the best-performing baseline. This improvement was statistically significant, as validated by the Friedman and Holm tests. Moreover, the proposed method achieves the shortest runtime among all the compared methods. Compared to other RNN-based hybrid frameworks that adopt classical structure-based and semantics-based similarity measures, the proposed method further improved the F-measure by 18.46%. Furthermore, a comparison of time and space complexity with the standalone RNN model and its variants demonstrated that the proposed method achieved high performance while maintaining favorable computational efficiency. These findings confirm the effectiveness and efficiency of the method in addressing ontology heterogeneity in complex IoE environments. Full article

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article

Pompeii is widely known for its tragic past. In 79 A.D., a massive eruption of Mount Vesuvius buried the city and its inhabitants under volcanic ash. Lost for centuries, it was rediscovered in 1748 when the Bourbon monarchs initiated excavations, marking the beginning of systematic digs. Since then, Pompeii has gained worldwide recognition for its archeological wonders. Despite centuries of looting and damage, it remains a breathtaking site. With millions of visitors annually, the Pompeii Archeological Park is the one most visited site in Italy. Managing such a vast and complex heritage site requires significant effort to ensure both visitor safety and the preservation of its fragile structures. Accessibility is also crucial, particularly for individuals with disabilities and staff responsible for site management. To address these challenges, integrated systems and advanced technologies like the Internet of Things/Everything (IoT/IoE) can provide innovative solutions. These technologies connect people, smart devices (such as mobile terminals, sensors, and wearables), and data to optimize security, safety, and site management. This paper presents a security/safety IoT/IoE-based system for security, safety, management, and visitor services at the Pompeii Archeological Park. Full article

With the advancement of network communication technology and Internet of Everything (IoE) technology, which connects all edge devices to the internet, the network traffic generated in various platform environments is rapidly increasing. The increase in network traffic makes it more difficult for the detection system to analyze and detect malicious network traffic generated by malware or intruders. Additionally, processing high-dimensional network traffic data requires substantial computational resources, limiting real-time detection capabilities in practical deployments. Artificial intelligence (AI) algorithms have been widely used to detect malicious traffic, but most previous work focused on improving accuracy with various AI algorithms. Many existing methods, in pursuit of high accuracy, directly utilize the extensive raw features inherent in network traffic. This often leads to increased computational overhead and heightened complexity in detection models, potentially degrading overall system performance and efficiency. Furthermore, high-dimensional data often suffers from the curse of dimensionality, where the sparsity of data in high-dimensional space leads to overfitting, poor generalization, and increased computational complexity. This paper focused on feature engineering instead of AI algorithm selections, presenting an approach that uniquely balances detection accuracy with computational efficiency through strategic dimensionality reduction. For feature engineering, two jobs were performed: feature representations and feature analysis and selection. With effective feature engineering, we can reduce system resource consumption in the training period while maintaining high detection accuracy. We implemented a malicious network traffic detection framework based on Convolutional Neural Network (CNN) with our feature engineering techniques. Unlike previous approaches that use one-hot encoding, which increases dimensionality, our method employs label encoding and information gain to preserve critical information while reducing feature dimensions. The performance of the implemented framework was evaluated using the NSL-KDD dataset, which is the most widely used for intrusion detection system (IDS) performance evaluation. As a result of the evaluation, our framework maintained high classification accuracy while improving model training speed by approximately 17.47% and testing speed by approximately 19.44%. This demonstrates our approach’s ability to achieve a balanced performance, enhancing computational efficiency without sacrificing detection accuracy—a critical challenge in intrusion detection systems. With the reduced features, we achieved classification results of a precision of 0.9875, a recall of 0.9930, an F1-score of 0.9902, and an accuracy of 99.06%, with a false positive rate of 0.65%. Full article

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems. Full article

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology. Full article

With the continuous progress and development of technology, the Internet of Everything (IoE) is gradually becoming a research hotspot. More companies and research institutes are focusing on the connectivity and transmission between multiple devices in asymmetric networks, such as V2X, Industrial Internet of Things (IIoT), environmental monitoring, disaster management, agriculture, and so on. The number of devices and business volume of these applications have rapidly increased in recent years, which will lead to a large load of terminals and affect the transmission efficiency of IoE data transmission. To deal with this issue, it has been proposed to perform data transmission via multipath cooperative transmission with multihop transmission. This approach aims to improve transmission latency, energy consumption, reliability, and throughput. This paper designs a channel-sensing-based cooperative transmission mechanism (CSCTM) with hybrid automatic repeat request (HARQ) for user equipment (UE) aggregation mechanism in future asymmetric IoE scenarios, which ensures that IoE devices data can be transmitted quickly and reliably, and supports real-time data processing and analysis. The main contents of this proposed method include strategies of cooperative transmission and redundancy version (RV) determination, a joint combination of decoding process at the receiving side, and a design of transmission priority through ascending offset sort (AOS) algorithm based on channel sensing. In addition, multihop technology is designed for the multipath cooperative transmission strategy, which enables cooperative nodes (CN) to help UE to transmit data. As a result, it can be obtained that CSCTM provides significant advancements in latency and energy consumption for the whole system. It demonstrates improvements in enhanced coverage, improved reliability, and minimized latency. Full article

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network. Full article

Polymer dielectric materials have recently attracted attention for their versatile applications in emerging electronic devices such as memory, field-effect transistors (FETs), and triboelectric nanogenerators (TENGs). This review highlights the advances in polymer dielectric materials and their integration into these devices, emphasizing their unique electrical, mechanical, and thermal properties that enable high performance and flexibility. By exploring their roles in self-sustaining technologies (e.g., artificial intelligence (AI) and Internet of Everything (IoE)), this review emphasizes the importance of polymer dielectric materials in enabling low-power, flexible, and sustainable electronic devices. The discussion covers design strategies to improve the dielectric constant, charge trapping, and overall device stability. Specific challenges, such as optimizing electrical properties, ensuring process scalability, and enhancing environmental stability, are also addressed. In addition, the review explores the synergistic integration of memory devices, FETs, and TENGs, focusing on their potential in flexible and wearable electronics, self-powered systems, and sustainable technologies. This review provides a comprehensive overview of the current state and prospects of polymer dielectric-based devices in advanced electronic applications by examining recent research breakthroughs and identifying future opportunities. Full article

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. Full article

With the fast growth of the Industrial Internet of Everything (IIoE), computing and telecommunication industries all over the world are moving rapidly towards the IPv6 address architecture, which supports virtualization architectures such as Network Function Virtualization (NFV). NFV provides networking services like routing, security, storage, etc., through software-based virtual machines. As a result, NFV reduces equipment costs. Due to the increase in applications on Industrial Internet of Things (IoT)-based networks, security threats have also increased. The communication links between people and people or from one machine to another machine are insecure. Usually, critical data are exchanged over the IoE, so authentication and confidentiality are significant concerns. Asymmetric key cryptosystems increase computation and communication overheads. This paper proposes a lightweight and certificateless end-to-end secure communication scheme to provide security services against replay attacks, man-in-the-middle (MITM) attacks, and impersonation attacks with low computation and communication overheads. The system is implemented on Linux-based Lubuntu 20.04 virtual machines using Java programming connected to NFV-based large-scale hybrid IPv4-IPv6 virtual networks. Finally, we compare the performance of our proposed security scheme with existing schemes based on the computation and communication costs. In addition, we measure and analyze the performance of our proposed secure communication scheme over NFV-based virtualized networks with regard to several parameters like end-to-end delay and packet loss. The results of our comparison with existing security schemes show that our proposed security scheme reduces the computation cost by 38.87% and the communication cost by 26.08%. Full article

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network. Full article

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms. Full article