Personalized federated learning is a practical solution to provide personalized services for Internet of Things devices while protecting their privacy. However, recent research found that personalized federated learning is still vulnerable to attacks, and its privacy can still be compromised during the training process. With an increasing number of devices and more diverse data, information traceability is also much more complicated. To address these problems, this paper proposes pFedBlock, a blockchain-based split federated learning framework to alleviate privacy risks and enhance the traceability during model training. Through the application of blockchain decentralized and immutable characteristics, pFedBlock can save each model update in a secure way and preserve a trustworthy log for all training behaviors. Such design can be beneficial to protect the training process and minimize the possible attacks from adversarial behaviors. Meanwhile, we also design a hybrid aggregation strategy in the federated framework so that devices can perform model updates in a more secure way. Experimental analysis shows that compared with traditional personalized federated learning methods, pFedBlock can obtain better performance in both models performance and system security.

Ubiquitous, low-latency intelligence at the network edge is central to large-scale Internet of Things (IoT) deployments, yet effectively coordinating communication, computing, and backhaul operations across heterogeneous layers remains challenging. This paper presents a unified cross-layer framework for terrestrial–satellite edge computing IoT systems that integrates Non-Orthogonal Multiple Access (NOMA)-based terrestrial access with local, satellite, and cloud execution. Unlike conventional terrestrial Multi-access Edge Computing (MEC)/edge– cloud scheduling, we jointly optimize partial offloading and path selection over a NOMA-coupled uplink and a multi-hop satellite edge/cloud execution chain under end-to-end latency coupling. The framework jointly determines path selection and partial offloading to minimize a latency–energy objective using accurate end-to-end models. Within this framework, two complementary scheduling algorithms are developed. The Centralized Optimal Cross-Layer Scheduler (COCS) formulates the joint scheduling problem as a mixed-integer nonlinear program (MINLP) with logarithmic and bilinear terms. It employs the spatial branch-and-bound (sBB) method within a commercial solver to obtain a global solution, serving as a performance benchmark. The Decentralized Game-Theoretic Scheduler (DGTS) models user decisions as an ordinal potential game (OPG) and achieves distributed convergence via best-response dynamics (BRD), enabling scalability and adaptability to large networks. Extensive simulations demonstrate that COCS achieves the global optimum while DGTS attains near-optimal performance with much lower complexity. These results validate the effectiveness of the proposed cross-layer framework and highlight the importance of coordinated communication–computation–backhaul design for terrestrial–satellite integrated edge computing.

The rapid development of the Internet of Vehicles (IoV) has led to a surge of computation-intensive and latency-sensitive vehicular applications, which pose significant challenges to resource-constrained vehicles and conventional mobile edge computing (MEC) infrastructures. In dense urban scenarios, the high mobility of vehicles and the spatiotemporally uneven distribution of vehicular workloads often result in severe load imbalance among edge servers, degrading system performance. To address these challenges, this paper investigates a three-tier heterogeneous MEC-enabled IoV architecture that integrates vehicles, terrestrial base stations (BSs), and an unmanned aerial vehicle (UAV). In this architecture, the UAV acts as an elastic computing node that is dynamically deployed over hotspot regions to complement BS-based MEC, while task migration among edge servers is leveraged to alleviate localized overload. We formulate the multi-vehicle computation offloading and migration problem as a distributed game, where each vehicle autonomously selects among local computing, BS offloading, and BS-to-BS migration, while BS-to-UAV migration is available only when its nearest BS lies within the current UAV coverage region. A joint cost function that captures both computation latency and energy consumption is developed. By constructing an appropriate potential function, we prove that the proposed game admits a Nash equilibrium. Building on this property, a Workload-oriented Computation Offloading Game for UAV-assisted MEC (WCOG) is designed to enable scalable and distributed decision-making. Extensive simulation results demonstrate that the proposed algorithm achieves near-optimal system performance, effectively balances server workloads, and significantly reduces the computation cost compared with benchmarks, while maintaining strong scalability under increasing vehicular density or expanding server scales.

Edge computing enables distributed machine learning models to be deployed and trained near the user space. However, the intricate nature of edge computing raises several challenges to distributed machine learning frameworks: 1) inferior convergence arising from non-independent and identically distributed (non-IID) edge data; 2) inefficient structural adaptation, where device dynamism complicates the adjustment of aggregation structure; and 3) reduced training efficiency, as resource heterogeneity and fluctuations create systemic stragglers. To address these issues, a distributed hierarchical model training framework has been proposed by considering the dynamic aggregation structure and frequency in this paper. This framework designs an Edge Aggregation Structure and Frequency method, namely EASF, for distributed model training in heterogeneous edge computing environments. First, a dynamic distributed aggregation structure method is formulated to consider various data distribution patterns. This method constructs and modifies the aggregation structure in a distributed manner to adapt to variations in working edge devices. Second, a self-adapted aggregation frequency method and a timeout abandonment mechanism are proposed to allow each node to update its aggregation frequency adaptively. Lastly, a theoretical analysis demonstrates the convergence property of the EASF method in dynamic environments. Extensive experiments have been conducted on a set of open testbeds. Results show that the EASF significantly improves the efficiency and accuracy of hierarchical model training in heterogeneous edge computing.

As a complementary solution for Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) can temporarily provide reliable and flexible offloading services when edge servers are damaged or unavailable. However, existing UAV-assisted MEC systems suffer from issues such as uneven resource allocation, low utilization efficiency, load imbalance, and poor dynamic adaptability, affecting service quality. Moreover, sensitive user equipment (UE) information faces leakage during the computational process of UAVs. How to jointly optimize the scheduling of servers and UAVs for task offloading and resource allocation without compromising UEs' privacy remains a significant challenge. Thus, this paper proposed a privacy-preserving auction framework (namely Prizty) by considering the trajectory of UAVs, their constrained energy and computational capabilities, and the variability in UE distribution. Prizty employs a combinatorial obfuscation method to protect UEs' privacy and links bidding prices to computational resources and energy characteristics. It calls the sub-algorithm WPA to determine the winners by balancing social costs and utility. Theoretical analysis demonstrates that Prizty satisfies truthfulness and individual rationality while maintaining scalability for large-scale resource allocation problems. Extensive experiments on real-world datasets validate Prizty's effectiveness in critical metrics, including offload rate, average service latency, energy consumption, and social cost.

Abstract The integration of the Internet of Vehicles (IoV) with the Intelligent Transportation System (ITS) plays a significant role in ensuring safe and efficient traffic management. However, communication signals may encounter natural or artificial obstacles that result in uncovered road segments, where connectivity is lost. Even minor collisions in these unprotected areas can escalate into major incidents or traffic bottlenecks, especially when vehicles are traveling at high speeds on highways. Therefore, it is crucial to promptly inform the ITS of any adverse events occurring in these unnoticed areas. The importance of Store-Carry-Forward (SCF) techniques increases in such scenarios when uncovered patches exist and intervehicle distances exceed the communication range. Store-Carry-Forward (SCF) is a technique where vehicles temporarily store data and forward it when a connection is available. To address improved coverage and reduced latency using SCF, we propose a beacon-based Store-Carry-Forward Scheme to Deliver Emergency Messages via vehicle-to-vehicle (V2V) Communication for Highways (SCF-EMD). The proposed method aims to minimize congestion and delays in disseminating information about adverse events. It involves selecting high-mobility vehicles to travel across both covered and uncovered highway segments. The scheme integrates V2V and vehicle-to-infrastructure (V2I) communication to ensure rapid delivery of emergency alerts to ITS. SCF-EMD demonstrated significant improvements over existing schemes in Network Simulator $$(NS-3)$$ , achieving over $$10\%$$ , $$16\%$$ and $$5\%$$ gains in information coverage, end-to-end delay, and packet delivery ratio (PDR), respectively.

Computer simulation platforms offer an alternative solution by emulating complex systems in a controlled manner. However, existing Edge Computing (EC) simulators, as well as general-purpose vehicular network simulators, are not tailored for VEC and lack dedicated support for modeling the distinct access pattern, entity mobility trajectory and other unique characteristics of VEC networks. To fill this gap, this paper proposes VEC-Sim, a versatile simulation platform for in-depth evaluation and analysis of various service caching and computation offloading policies in VEC networks. VEC-Sim incorporates realistic mechanisms to replicate real-world access patterns, including service feature vector, vehicle mobility modeling, evolving service popularity, new service upload and user preference shifts, etc. Moreover, its modular architecture and extensive Application Programming Interfaces (APIs) allow seamless integration of customized scheduling policies and user-defined metrics. A comprehensive evaluation of VEC-Sim’s capabilities is undertaken in comparison to real-world ground truths. Results prove it to be accurate in reproducing classical scheduling algorithms and extremely effective in conducting case studies. • Vehicular edge network modeling establish foundation for simulator design • Modular architecture and rich APIs enable flexible simulator customization • Realistic-enhanced mechanisms replicate heterogeneous access patterns • In-depth experiments validate VEC-Sim’s feasibility and effectiveness

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.

Abstract In recent years, cloud manufacturing has brought both opportunities and challenges to the manufacturing industry. Cloud manufacturing enables global manufacturing resources to be unified and shared, thus breaking down geographical constraints to enhance the level and efficiency of manufacturing. However, with the explosive growth of manufacturing resources and user demands, traditional cloud manufacturing platforms will face problems of insufficient computility, lack of real‐time data and difficulties in securing user privacy during the service selection process. In this article, a blockchain‐based decentralized cloud manufacturing service selection method is proposed, where the computility resource is deployed in multiple distributed nodes rather than the traditional centralized cloud manufacturing platform to solve the problem of insufficient computility. The credibility of the users is evaluated based on their performance on the contract and the PBFT consensus algorithm is improved based on the credibility of the users. In addition, a tri‐chain blockchain data storage model is designed to ensure the security, real‐time and transparency of data in the cloud manufacturing service selection process. The experimental results show that the method both speeds up service selection process and improves the quality of service selection results, and achieves a significant increase in manufacturing efficiency.

Abstract With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.

Cloud-edge-terminal orchestrated computing, as an expansion of cloud computing, has sunk resources to the edge nodes and terminal equipment, which can provide high-quality services for delay-sensitive applications and reduce the cost of network communication. Due to the high volume of data generated by Internet of Things (IoT) devices and the limited storage capacities of edge nodes, a significant number of terminal devices are now being considered for utilization as storage nodes. However, because of the heterogeneous storage capacity and reliability of these hardware devices and the different data requirements of user services, the performance and storage reliability of applications deployed in cloud-edge-terminal orchestrated computing environments have become urgent problems to be solved. Especially, for a distributed storage system in these environments, it is required to ensure reliable storage of the generated data and its’ replications. In this paper, we first implement a distributed storage system and construct a data replication placement model. Then, based on the constructed model, we formulate the data replication placement problem and design a data replication placement strategy called DRPS to solve it. The DRPS covers a ranks-based replication storage node selection algorithm and a greedy load balancing algorithm, which can select appropriate hardware devices for different data requirements of services and is implemented in the data storage system to store replications and balance loads. We design extensive experiments to verify the effectiveness of DRPS. The results indicate that the proposed strategy outperforms other state-of-the-art algorithms in terms of system delay reduction by 39.9%, an increase of 43.3% in the replication numbers, a 27.5% improvement in memory utilization, and a reduction of unreliability rate by 82.0%.

Accurate indoor localization and navigation enable real-time, ubiquitous, location-based services. Over the past decade, data-driven approaches for inertial odometry have shown the potential to enhance indoor positioning accuracy. However, low-cost inertial measurement units (IMUs), commonly used in smartphones and IoT devices, are prone to significant noise, leading to drift and degraded performance in navigation algorithms. This article presents a novel, lightweight, and real-time end-to-end framework, DeepILS, designed to process raw inertial data for precise pedestrian localization in indoor environments. DeepILS utilizes a residual network enhanced with channel-wise and spatial attention mechanisms, enabling accurate velocity and position estimation across diverse motion dynamics. The framework’s effectiveness is validated using four benchmarks and two newly introduced datasets in real-time edge scenarios. These datasets were collected across diverse indoor environments at the KAIST campus and Incheon National Airport, using multiple hardware platforms, including the KAIST IoT positioning module and Android smartphones. Experimental results, including tests on unseen data and comprehensive ablation studies, demonstrate that DeepILS improves localization accuracy by 70% compared to state-of-the-art methods while effectively mitigating sensor noise and enhancing robustness in real-world environments. Specifically, DeepILS exhibits excellent edge performance on IoT devices, making it highly suitable for real-time applications.

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

Deep neural networks (DNN) has been widely applied in big data-driven Internet of Things (IoT) for excellent learning ability, while the black-box nature of DNN leads to uncertainty of inference results. With higher interpretability, convolutional fuzzy neural network (CFNN) becomes an alternative choice for the model of IoT applications. IoT applications are often latency-sensitive. By jointly utilizing computing power of IoT devices and edge servers, end-edge collaborative CFNN inference improves the insufficiency of local computing resources and reduces the latency of computing-intensive CFNN inference. However, the calculation amount of fuzzy layers is hard to get directly, bringing difficulty to CFNN partition. In addition, the profit of service providers is often ignored in existing work on distributed inference. In this article, an end-edge collaborative inference framework of CFNNs for big data-driven IoT, named DisCFNN, is proposed. Specifically, a novel CFNN structure and a method of fuzzy layer calculation amount assessment are designed at first. Next, computing resource allocation and CFNN partition decisions are generated on each edge server based on deep reinforcement learning. Then, each IoT device sends the request of CFNN inference service to a certain edge server or infer the whole CFNN locally according to the task offloading strategy obtained through many-to-one matching game. Finally, the effectiveness of DisCFNN is evaluated through extensive experiments.

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offline learning which may yield subpar performance, when concept drift occurs, i.e., distributions of historical and future data vary. Online learning can detect concept drift during model training, thus more applicable to TFF. Nevertheless, the existing federated online learning method for TFF fails to efficiently solve the concept drift problem and causes tremendous computing and communication overhead. Therefore, we propose a novel method named Resource-Efficient Federated Online Learning (REFOL) for TFF, which guarantees prediction performance in a communication-lightweight and computation-efficient way. Specifically, we design a data-driven client participation mechanism to detect the occurrence of concept drift and determine clients’ participation necessity. Subsequently, we propose an adaptive online optimization strategy, which guarantees prediction performance and meanwhile avoids meaningless model updates. Then, a graph convolution-based model aggregation mechanism is designed, aiming to assess participants’ contribution based on spatial correlation without importing extra communication and computing consumption on clients. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of REFOL in terms of prediction improvement and resource economization.

The gradual advancement of Internet-connected vehicles has transformed roads and highways into an intelligent ecosystem. This advancement has led to a widespread adoption of vehicular networks, driven by the enhanced capabilities of automobiles. However, managing mobility-aware computations, ensuring network security amidst instability, and overcoming resource constraints pose significant challenges in heterogeneous vehicular network applications within Fog computing. Moreover, the latency overhead remains a critical issue for tasks sensitive to latency and deadlines. The objective of this research is to develop a Mobility-aware Secured offloading and Scheduling (M-SOS) technique for a Dew-enabled vehicular Fog-Cloud computing system. This technique aims to address the issues outlined above by moving the computations closer to the edge of the network. Initially, a Dew-facilitated vehicular Fog network is proposed, leveraging heterogeneous computing nodes to handle diverse vehicular requests efficiently and ensuring uninterrupted services within the vehicular network. Further, task management is optimized using a Fuzzy logic that categorizes tasks based on their specific requirements and identifies the target layers for offloading. Besides, a cryptographic algorithm known as SHA-256 RSA enhances security. Moreover, a novel Linear Weight-based JAYA scheduling algorithm is introduced to assign tasks to appropriate computing nodes. The proposed algorithm surpasses the comparable algorithms by 23% in terms of AWT, 18% in terms of latency rate, 14% and 23% in terms of meeting the hard-deadline ($H\_d$) and soft-deadline ($S\_d$), and 35% in terms of average system cost, respectively.

This paper researches the secrecy outage probabili-ty for the rate splitting multiple access-based integrated satellite-aerial-terrestrial networks. Specially, owing to some practical reasons, imperfect hardware is further analyzed for all the network nodes. Moreover, a UAV is utilized to help the signal transmitting from the satellite to the ground destination in the presence of an eve. Besides, by considering these limitations, the detailed analysis for the secrecy outage probability are gotten, which offer a good way to calculate the impacts of channel parameters and system factors on the secrecy networks. Finally, several representative Monte Carlo simulations are presented to confirm the rightness of the analytical results.

Abstract The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

The development of cloud, fog, and edge computing has led to great advances in reducing latency and saving bandwidth, and these methods have therefore been broadly applied in various domains, including healthcare, transportation, and the Internet of Things (IoT). Traditional edge computing solutions have proven to be insufficient in fulfilling the demanding prerequisites of low latency and high data rates. Additionally, publicly available edge cloud solutions fail to meet the required standards for ensuring privacy protection. Consequently, Private Edge Cloud Systems (PECSs) have garnered attention as a prospective solution owing to their capacity to mitigate privacy risks and their significant computing capacity. PECS research has seen significant growth, but there is a lack of detailed review of its issues, approaches, and applications in the literature. To explore the potential application value of PECS, this paper provides a systematic review of intelligent platforms and architecture for PECSs. Specifically, an overview of the fundamental characteristics of PECSs is provided. Second, we classify intelligent platforms and architectures and analyze their implementation techniques and realization methods. Third, we discuss four specific application scenarios. Finally, promising future research directions are discussed. The findings of this research show that PECSs can effectively meet the requirements for low latency and privacy protection and are a fertile domain for further research.

With the gradually integration of Internet of Vehicles (IoV) and artificial intelligence (AI), artificial intelligence of vehicles (AIoV) is emerging as a novel paradigm with advanced capability for information gathering and decision-making. Leveraging massive traffic information facilitated by vehicle-road coordination in AIoV, path planning has the potential to effectively mitigate existing traffic problems, such as road congestion, improving traffic performance. However, the dynamic nature of traffic flow and the complexity of road networks increase the difficulty of path planning, posing a serious threat to road safety. In response to this challenge, a reinforcement learning based path planning scheme with traffic flow prediction, named RPFP, is proposed. RPFP consists of two fundamental components: 1) precise traffic flow prediction and 2) intelligent path planning. Specifically, the temporal convolutional network (TCN) is innovatively integrated into the spatiotemporal graph neural network (STGNN), providing accurate traffic flow prediction by comprehensively capturing spatial and temporal patterns. Informed by predicted traffic congestion, a path planning method utilizing dueling double deep q-network (D3QN) algorithm is employed to navigate within complex road networks. Eventually, RPFP was evaluated for its effectiveness through comprehensive experiments conducted on real traffic data sets. The superiority of RPFP was further substantiated via comparisons with multiple baseline schemes.

The proliferation of Internet of Things (IoT) applications and real-time services brings severe performance pressure on IoT systems with cloud computing, so edge computing is increasingly being adopted in IoT systems to assist cloud computing in providing services. Systems with cloud-edge computing deploy parts of services on edge servers located closer to IoT devices, thus enabling real-time data processing and analysis and improving the quality of experience (QoE) of users. However, inevitable extreme events (e.g. meteorological disasters) and the aging of the physical infrastructure cause varying degrees of performance impairment to edge servers, which adversely affects the service provisioning capability of IoT systems. Therefore, there is a serious challenge to cope with the lack of service provisioning capability owing to the impaired edge server performance in extreme environments. In this paper, an unmanned aerial vehicle (UAV) -assisted service provisioning framework for the IoT systems in cloud-edge computing is introduced, and a UAV-enhanced service caching scheme based on a potential game (G-USC) is proposed for this framework. Besides, to provide a prerequisite for service caching, a UAV position update scheme based on a deep Q-network is designed. The experimental analysis proves that G-USC effectively solves the problem of insufficient service provisioning capability of edge servers in extreme environments.

Summary With the development of artificial intelligence, cloud‐edge computing and virtual reality, the industrial design that originally depends on human imagination and computing power can be transitioned to metaverse applications in smart manufacturing, which offloads the services of metaverse to cloud and edge platforms for enhancing quality of service (QoS), considering inadequate computing power of terminal devices like industrial sensors and access points (APs). However, large overhead and privacy exposure occur during data transmission to cloud, while edge computing devices (ECDs) are at risk of overloading with redundant service requests and difficult central control. To address these challenges, this paper proposes a minority game (MG) based cloud‐edge service offloading method named COM for metaverse manufacturing. Technically, MG possesses a distribution mechanism that can minimize reliance on centralized control, and gains its effectiveness in resource allocation. Besides, a dynamic control of cut‐off value is supplemented on the basis of MG for better adaptability to network variations. Then, agents in COM (i.e., APs) leverage reinforcement learning (RL) to work on MG history, offloading decision, QoS mapping to state, action and reward, for further optimizing distributed offloading decision‐making. Finally, COM is evaluated using a variety of real‐world datasets of manufacturing. The results indicate that COM has 5.38% higher QoS and 8.58% higher privacy level comparing to benchmark method.

Intelligent Transportation Systems (ITS) supported by smart vehicles have revolutionized modern transportation, offering a wide range of applications and services, such as electronic toll collection, collision avoidance alarms, real-time parking management, and traffic planning. However, the open communication channels among various entities, including smart vehicles, roadside infrastructure, and fleet management systems, introduce security and privacy vulnerabilities. To address these concerns, we propose a novel security framework, named blockchain-assisted lightweight authenticated key agreement security framework for smart vehicles-enabled ITS (BASF-ITS), which ensures data protection both during transit and while stored on cloud servers. BASF-ITS employs a combination of efficient cryptographic primitives, including hash functions, XOR operator, ASCON, elliptic curve cryptography, and physical unclonable functions (PUF), to design authenticated key agreement schemes. The inclusion of PUF significantly enhances the system’s resistance to physical attacks, preventing tampering attempts. To ensure data integrity when stored on the cloud, our framework incorporates blockchain technology. By leveraging the immutability and decentralization of the blockchain, BASF-ITS effectively safeguards data at rest, providing an additional layer of security. We rigorously analyze the security of BASF-ITS and demonstrate its strong resistance against potential security ass aults, making it a robust and reliable solution for smart vehicle-enabled ITS. In a comparative analysis with contemporary competing schemes, BASF-ITS emerges as a promising approach, offering superior functionality traits, enhanced security features, and reduced computation, communication, and storage costs. Furthermore, we present a practical implementation of BASF-ITS using blockchain technology, showcasing the computational time versus the “transactions per block” and the “number of mined blocks”, confirming its efficiency and viability in real-world scenarios.Note to Practitioners—This article is motivated by designing an efficient, lightweight, and anonymous blockchain-enabled authenticated security framework that can fix the security and privacy concerns in insecure environments for ITS applications, such as automated road speed enforcement, collision avoidance alarm systems, and traffic planning and management, etc. Authenticated key agreement schemes are extensively used to secure communications in the ITS environment. However, the existing state-of-the-art schemes are not efficient in terms of performance, are not resilient against potential security attacks, and do not support anonymity, untraceability, and unlinkability. Therefore, we propose the authenticated security framework to secure communication among the participating entities in the ITS environment. It utilizes efficient cryptographic primitives, such as hash function, XOR-operator, ASCON, elliptic curve cryptography, and PUF. It is shown that the proposed framework can be deployed as a robust tool to address the ITS security problems efficiently. Moreover, the proposed framework is lightweight and efficient and can be easily deployed in various ITS applications and other resource-constrained environments. However, the participating entities, such as vehicles and roadside units, must be PUF-enabled to deploy the proposed framework.

Owing to the breakthrough in mobile wireless communication technologies, almost everyone has been immersed into social networks, while fake news and misinformation are also being pushed into people’s minds with astonishing speed and breadth. The rising disparity between limited computing resources and the exploding news size necessitates innovative solutions to handle the challenge posed by booming data volume and make it more likely to differentiate fake news. In response to the aforementioned dilemma, the social-aware computation offloading system is analyzed, where the digital twin (DT) paradigm is used to simulate tasks offloading and assess the associated costs. Next, to obtain the best offloading choice, we fully consider the social relationship constraints and further propose an online task execution method that includes two stages of cluster selection and computing offloading, named TOFDS. Specifically, it exploits the technologies from multiobjective optimization and deep reinforcement learning (DRL) and realizes the joint optimization of resource utilization, load balancing, service latency, and energy consumption. Eventually, the comparative experiments demonstrate that TOFDS performs well when dealing with fake news data and can adapt to changes in dataset size and service clusters.

Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

As an emerging concept, the metaverse incorporates a range of advanced technologies and offers a great opportunity to enhance the experiences of healthcare in clinical practice and human health. However, many cyber security issues often occur in the metaverse healthcare analytics such as DDoS attack, probe attack, and port scanning attack. Fortunately, 6G-enabled intrusion detection can detect anomalous activities with the help of an anomaly detection algorithm for metaverse healthcare analytics. Nevertheless, different from static data, data streams in metaverse healthcare have the intrinsic characteristics of infiniteness, correlation, and distribution change. Traditional static data anomaly detection algorithms do not consider these characteristics, which may result in low accuracy and efficiency. In this paper, aDataStreamAnomalyDetection (DS_AD) approach driven by 6G network is proposed for metaverse healthcare analytics, which incorporates a sliding window and model update into LSHiForest. DS_AD uses a change detection mechanism to optimize the model update. The core design utilizes hash functions to partition data spaces to find anomalies. To validate the feasibility of DS_AD, multiple groups of experiments are designed and executed on SMTP and HTTP datasets. Experimental results show that compared with baselines, our proposal performs favorably for data streams in terms of accuracy and efficiency.

Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.

Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

Meteorological decision-making is a crucial element in the meteorological disaster warning and prevention field. With the increasing frequency of meteorological disasters and the rapid development of edge intelligence, there is an urgent need to establish a meteorological early-warning platform that reduces human resource investment, decreases operating costs, and provides targeted information and response suggestions. Therefore, we propose the development of real-time decision-making based on edge intelligence-driven meteorological knowledge graph (EMKG), and aim to achieve meteorological emergency decision-making by combining the knowledge graph with edge intelligence. First, we collect data through edge devices and perform preprocessing and preliminary analysis on these devices to reduce the time and bandwidth requirements for data transmission to the cloud. Based on the above data, meteorological entity recognition and relation extraction were completed using techniques such as BERT, BiLSTM, CRF, and data augmentation. Then we trained a text generation model and deployed it on edge devices to achieve real-time meteorological decision-making. The experimental results show that EMKG effectively integrates edge intelligence and knowledge graph, and further improves the real-time and accuracy of meteorological decision-making.

The industrial revolution 4.0 (I4.0), internet of things developments, and the expansion of online web services have caused exponential growth and deployment in the number of cloud data centers(CDC). Cloud computing is a paradigm that enables tenants to use storage and computing resources in the pay-per-use model. Cloud service providers maximize their profits by distributing the tenant’s demands to the reserved storage and computing servers, minimizing the reservation cost with the satisfaction of the tenant’s quality of service level agreement. Workload prediction and resource management are fundamental and critical problems due to cloud-distributed infrastructure and nonlinear dynamic workload conditions. Conventional prediction techniques in a cloud environment provide the one-dimensional output. Existing solutions mostly forecast resources, such as CPU and memory usage, each as a single output. However, the one-dimensional output in the form of resource provision and usage is not able to capture the relationship of application requirements of multiple resources such as CPU, memory, CPU cores, Disk, and network, which result in inaccurate prediction results and limited information. Efficient resource management requires predicting multiple resource parameters using multivariate state variables for efficient resource allocation. This study proposes an intelligent computing framework based on multivariate time-series bidirectional long short-term memory (BiLSTM) forecasting for predicting cloud virtual machine resources. We consider multi-dimensional resources such as CPU provisioned and usage, memory provisioned and usage, CPU cores, Disk write and read throughput, and network receives and transmit throughput. We investigate several deep learning techniques the proposed multivariate BiLSTM, LSTM, stacked BiLSTM, stacked BiLSTM with LSTM, and BiLSTM auto-encoder. Furthermore, we evaluate the effectiveness of the proposed framework on two real workload traces: Bitbrains traces f astStorage and R nd. The performance metrics used to evaluate forecasting accuracy are the root mean square error , mean absolute error and mean absolute percentage error. Furthermore, we observe the training-testing data size and the historical window size variation effects on these models.

Abstract It is anticipated that sixth‐generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context‐aware security protocol. Physical layer security solutions can compete for low‐complexity, low‐delay, low‐footprint, adaptable, extensible, and context‐aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF‐FP) for location estimation is proposed, wherein the RF‐FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road‐side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF‐FP scheme increases SC by up to 25% for the same signal‐to‐noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF‐FP‐based location estimation provides much better results as compared to the existing physical layer security schemes.

6G-based wireless communication system is poised to redefine the next-generation network landscape by enabling novel services and applications, such as intelligent link establishment, power control, data collection, transmission, and distribution. However, security issues, particularly recently revealed channel access attack (CAA), present significant challenges to performance optimization tasks in the heterogeneous wireless networks of 6G, namely, Age of Information (AoI) oriented Network (AoN), Throughput oriented Network (ToN), and Latency oriented Network (LoN). To address these challenges, this article presents a game theory-based smart optimization solution to enable unmanned aerial vehicles (UAV) to resist CAA within a 6G-based heterogeneous network. Our methodology begins by outlining the advantages and challenges associated with UAV usage, followed by the design of performance indicators and intelligent resource allocation schemes under the influence of CAA. Subsequently, we introduce definitions and categories within game theory, encompassing the concept and equilibrium of three typical game models. The efficacy of our proposed framework is validated through simulation results, which demonstrate the achievement of optimal AoI, enhanced throughput, and reduced latency compared with baseline methodologies when countering CAA in a UAV-assisted heterogeneous network.

The integration of thriving information and communications technology (ICT) and cyber–physical systems (CPSs) has spawned several innovative applications, such as remote healthcare, smart and intelligent transportation, smart logistics, smart grids, and public safety. An emerging software-defined networks (SDNs) technology further enabled to optimize the communication among Industrial IoT (IIoT) and CPS entities. Nonetheless, the communication on public channel among different IIoT entities in an SDN-enabled environment may be exposed to various security threats due to wireless and insecure communication channels. To counter these security challenges in the way of wider CPS or IIoT adoption, we propose a novel three-factor authenticated key exchange mechanism (SUSIC) for SDN-enabled IIoT ecosystem. The SUSIC enables a registered user to access real-time data from physical IIoT environment directly after having mutual authentication performed through SDN-enabled controller node. The scheme is proved to be secure under rigorous formal and informal security analysis. Moreover, the simulation results and performance evaluation signifies toward achieving a better tradeoff between security functionalities and computational overheads comparatively.

The distribution of edge resources in the edge computing (EC) environment has an important impact on the Quality of Service (QoS) of edge services. Unreasonable server placement will inevitably lead to problems, such as server overload or underload, deteriorating workload balancing and service wait time. Therefore, the key issue to be addressed in server placement is how to enhance the QoS of edge services through efficient edge server (ES) placement strategies under multiple requirements, such as average task wait time and data privacy. EC-assisted with blockchain technology was argued to be the most potential solution. In this article, we propose a blockchain-assisted secure ES placement algorithm named ETS_GA. ETS_GA is based on the elite-preserving genetic algorithm (EGA), which is proven to converge. The premature problem of traditional genetic algorithm (GA) is effectively solved by using tabu search (TS) and niche sharing (NS). In addition, we construct an adaptive state supervising machine (ASM) to realize real-time algorithm supervision and adaptively iterate the optimization strategy. Blockchain-based privacy protection methods are also deployed in the placed servers to provide real-time privacy protection. Finally, our proposed method is experimentally compared with four baselines using the real Shanghai Telecom base station data set, whose results demonstrate the superiority of ETS_GA in terms of convergence and global search capability.

Unmanned aerial vehicles (UAVs) will be a vital part of the massive Industrial Internet of Things (IIoT) in the 5G and 6G paradigms. The UAVs are required to collaborate with each other to deal with some computation-intensive IIoT applications in an autonomous UAV system. However, due to the limited processing capacity of UAVs, they are occasionally unable to handle certain tasks adequately (e.g., crowdsensing). Therefore, it is an important issue to realize efficient offloading of these computation-intensive IIoT applications. In this article, we first partition the computation-intensive IIoT application into a directed acyclic graph with multiple collaborative tasks. Then, we establish a joint optimization problem based on the models of the processor resources and energy consumption for the task offloading scheme. Third, we propose a cooperative resource allocation approach to optimize the joint optimization problem under the constraints of resource and communication latency, and then can migrate more computation-intensive tasks to the edge clouds. Finally, we build an aerial computing simulation system and make a comparative evaluation and analysis of our proposed cooperative resource allocation approach in terms of effectiveness and performance. The experimental results show that our proposed approach performs better than other related approaches.

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the traditional CNNs struggle to extract global context information, resulting in poor anomaly detection performance. Thus, a masked Swin Transformer Unet (MSTUnet) for anomaly detection is proposed. To solve the problem of insufficient abnormal samples in the training phase, an anomaly simulation and mask strategy is first applied on anomaly-free samples to generate a simulated anomaly and, then, the Swin Transformer's powerful global learning ability is used to inpaint the masked area. Finally, a convolution-based Unet network is used for end-to-end anomaly detection. Experimental results on industrial dataset MVTec AD show that MSTUnet achieves superior anomaly detection and localization performance.

Recommender systems are technology-driven marketing solutions for businesses that analyze user behavior data. However, collaborative data sharing between enterprises is often prohibited by privacy protection regulations, leading to insufficient data for graph neural networks (GNNs) training. Fortunately, federated learning (FL), a collaborative training framework without exposing source data, can be applied congruently. Nevertheless, most of FL-based GNN model training methods adopt federated averaging, which performs poorly on highly heterogeneous graph data. To solve this problem, a FL-based GNN Model Training framework for cross-enterprise recommendation, named FL-GMT, is proposed. Specifically, a GNN-based recommendation model is deployed as the local training model. Then, considering the performance inequity caused by uneven sample quality, a loss-based federated aggregation algorithm is designed, effectively improving the performance of disadvantaged participants. To improve the system stability at the end of the aggregation, a dynamic update method of loss attention is designed. Extensive experiments on benchmark datasets demonstrate that FL-GMT outperforms baselines in terms of system fairness, stability, and accuracy.

Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

An unprecedented prosperity in artificial intelligence promotes the development of Internet of Vehicles (IoV). Assisted by edge computing, vehicles enable to offload data to edge servers in close proximity to users for processing, thus making up for the shortage of local computing resources. However, due to the uneven space-time distribution of traffic flow, edge servers of a certain road segment may be overwhelmed by the surge of service requests. Furthermore, IoV system will incur significant additional energy consumption and time delay because of the absence of a proper computation offloading scheme between edge servers. To cope with above challenges, a computing offloading method for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV is proposed. We first design the graph weighted convolution network (GWCN) that can fully excavate the connectivity and distance relation information between road segments to conduct traffic flow prediction. The short-term prediction results are utilized as the basis for adjusting the resource allocation of edge resources in different regions. Then, a computation offloading method driven by deep deterministic policy gradient (DDPG) is leveraged to obtain an optimal computation offloading scheme for edge servers. Finally, extensive comparative experiments demonstrate the low prediction error of GWCN and superior performance of DDPG-driven method in reducing total time delay and energy consumption.

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

The cognitive radio-enabled$massive$Internet of Things (CR-$m$IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR-$m$IoT, the sensing process becomes a major hurdle for CR-$m$IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.

The rapid development and deployment of Internet of Things (IoT) devices in modern networks and Industry 4.0 have attracted substantial interest from cybersecurity researchers. In this study, we propose a software-defined framework that improves network intrusion detection systems by using manufacturer usage description (MUD) to enhance the behavioral monitoring in IoT networks. We aim to explore whether Industrial IoT (IIoT) devices typically serve a common role in cyber–physical systems, and their communications exhibit predictable patterns that can be defined in MUD profile(s) formally and succinctly. We design a framework that utilizes the concept of digital twins and software-defined networking to improve the security of IIoT environments. The MUD data are profiled, and the actions are evaluated on the network digital twin before they are used in the physical network. The behavioral profiling system is updated in real time, thereby improving the overall system security and compliance to policies in the IoT deployment. Evaluation results show that our solution outperforms existing approaches substantially in terms of attack detection accuracy, predicting security incidents, response time, and resource usage.

In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users’ check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users’ long-term dependencies and interests; and 3) although the overall users’ check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user’s interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.

Software-defined networks (SDNs) can help facilitate dynamic network resource provisioning in demanding applications, such as those involving Industrial Internet of Things (IIoT) devices and systems. For example, SDN-based systems can support increasing demands of multitenancy at the network layer, complex demands of microservices, etc. A typical (large) manufacturing setting generally comprises a broad and diverse range of IoT devices and applications to support different services (e.g., transactions on enterprise resource planning (ERP) software, maintenance prediction, asset management, and outage prediction). Hence, this work introduces a demand–supply-based economic model to enhance the efficiency of different multitenancy attributes at the network layer, which captures the computational complexity of industrial ERP-IoT transactions and performs network resource provisioning, based on the demand–supply principle. The proposed model is accompanied by a flow scheduler, which dynamically assigns ERP-IoT traffic flow entries on network devices to specific preconfigured queues. This scheduler is used to increase service providers’ utility. The evaluation of the proposed model suggests the utility of our proposed approach.

Ensuring security and privacy in the Internet of Things (IoT) while taking into account the resource-constrained nature of IoT devices is challenging. In smart home (SH) IoT applications, remote users (RUs) need to communicate securely with resource-constrained network entities through the public Internet to procure real-time information. While the 6LoWPAN adaptation-layer standard provides resource-efficient IPv6 compatibility to low-power wireless networks, the basic 6LoWPAN design does not include security and privacy features. A resource-efficient authenticated key exchange (AKE) scheme becomes imperative for 6LoWPAN-based resource-constrained networks to render indecipherable communication functionality. This article presents a lightweight user AKE scheme for 6LoWPAN-based SH networks (LAKE-6SH) to achieve authenticity of RUs and establish private session keys between the users and network entities by employing the SHA-256 hash function, exclusive-OR operation, and a simple authenticated encryption primitive. Informal security validation illustrates that LAKE-6SH is protected against different pernicious security attacks. The security is further validated formally through the random oracle model. Moreover, through Scyther validation, it is demonstrated that LAKE-6SH is secure. In addition, it is demonstrated that LAKE-6SH renders better security features aside from its low communication and computational overheads.

The Internet of Vehicles (IoV) environment consists of a number of latency-critical and data-intensive application (e.g., real-time video analytics). In this article, we posit the potential of leveraging the sixth-generation (6G) mobile networks to minimize communication delay, particularly for latency-critical task execution. In particular, the 6G-enabled network in boxes (NIBs) deployed in the vehicles can communicate in real time with the edge servers or the NIBs in other vehicles. Although NIBs are capable of providing dynamic and flexible computing resources to support real-time IoV services, there are significant energy costs associated with the communication and computing activities. Seeking to achieve an optimal balance between energy consumption and time cost during service migration, we design a NIB task migration (NTM) method for IoV in this article. In our approach, the IoV framework is designed and the routing mechanism is established. The strength Pareto evolutionary algorithm (SPEA2) is then utilized to determine the migration strategy. Findings from our experiments demonstrate the reliability and efficiency of our proposed approach.

Integrating lightweight web application programming interfaces (APIs) into mobile Apps is a promising way for quick and cost-effective development of mobile Apps with desired functions. Web APIs, on the other hand, are created by distinct enterprises or organizations, making it challenging to develop compatible and diverse mobile Apps by combining existing web APIs. It has been demonstrated that this process is an NP-hard problem. In mobile Apps development, it is often necessary to read confidential information, leading to the business privacy leakage of enterprises. Thus, we devise a novel efficient web APIs recommendation (E-WAR) approach based on locality-sensitive hashing for recommending desirable web APIs to developers. Through analyzing industrial enterprises’ expected needs, E-WAR efficiently makes compatible and diverse web APIs recommendations while guaranteeing privacy protection. Finally, extensive experiments on real-world web APIs datasets are conducted. The results show that E-WAR can achieve significant performance improvements over the existing approaches.

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments.

In the Internet of Vehicles (IoV), various latency-critical and data-intensive applications have recently emerged to support smart traffic solutions. The sixth generation mobile networks (6G) greatly reduce the communication delay for the latency-critical tasks. However, the computing resources and execution efficiency for data-intensive tasks are still inadequate. The stationary edge computing (EC) servers, on the other hand, lack the flexibility to give service to moving vehicles. Therefore, Cybertwin is introduced in the IoV paradigm to provide a unified access point for EC. In addition, the 6G-enabled network in box (NIB) is deployed in vehicles to provide flexible computing power. However, in this article, the optimization of NIB task migration is still a challenge; thus, NIB task migration method (NTM) for IoV is proposed. The Pareto envelope-based selection algorithm is employed to determine the strategy. Finally, NTM is evaluated by a real-world dataset of the service requests.

Traffic flow prediction plays a critical role in reducing traffic congestion in transportation systems. However, accurate traffic flow prediction becomes challenging due to the impact of complex spatio-temporal (ST) correlations and the diversity of ST correlations. When modeling complicated ST correlations, researchers usu did not take the diversity of ST correlations into consideration, resulting in poor prediction accuracy. In this paper, we propose ST-InNet, a deep spatio-temporal Inception network for collectively predicting traffic flow in each city region. Specifically, ST-InNet employs two Inception networks to simultaneously capture various spatial and temporal correlations of traffic data, including temporal closeness, temporal periodicity, nearby spatial dependencies, and distant spatial dependencies. For the diversity of spatial correlations, ST-InNet presents an improved variant of an Inception module to explicitly capture the different contributions of spatial correlations for each region. For the diversity of temporal correlations, ST-InNet designs a fusion component to explicitly model the varying contributions of temporal correlations on prediction. The experiments are conducted on a real-world traffic dataset in Nanjing, demonstrating that ST-InNet outperforms five state-of-the-art baselines in short-term and long-term traffic flow predictions with an average accuracy improvement of 32.09% and 30.97%, respectively.

In the vehicular ad hoc networks (VANETs), minimizing the broadcast storm that arises due to message rebroadcast during emergency message dissemination in extremely mobile environments under sparse or dense networks is a significant challenge. Proper selection of rebroadcasting vehicles guarantees acceptable end-to-end delay, high delivery ratio, and efficient bandwidth utilization. To date, many protocols have been proposed to select an appropriate rebroadcasting vehicles based on vehicle position information only. However, such approaches neglect the fact that both vehicle velocity and position information can be utilized efficiently to alleviate rebroadcast message collisions and control bandwidth consumption. In this work, we present a new broadcast suppression protocol, named, velocity and position-based broadcast suppression for VANETs (VP-CAST), which can work in both sparse and dense network situations. VP-CAST does rely on periodic beacon messages, rather the position and velocity information of broadcasting vehicle are included in a broadcast message. Moreover, the transmission range of broadcasting vehicle is divided into dynamic time slots based on velocity and position information of broadcasting and receiving vehicles.The proposed scheme assigns shorter and dynamic waiting time to the vehicles moving at high velocities and located farther from the sender vehicle that eventually reduces both the message re-transmission delay and the number of rebroadcasting vehicles. The proposed protocol is compared with the DV-CAST in terms of end-to-end delay, message delivery ratio, and message overhead.

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines.

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs.

Summary DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high‐level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s‐shaped and z‐shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. TheINSWFfilter outperformed the existing fixed‐length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis.

The development of vehicular Internet of Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. The mobility aware services and network-based security are fundamental requirements of these applications. However, multi-side offloading enabling blockchain and cost-efficient scheduling in heterogeneous vehicular fog cloud nodes network become a challenging task. The study formulates this problem as a convex optimization problem, where all constraints are the convex set. The goal of the study is to minimize communication cost and computation cost of applications under mobility, security, deadline, and resource constraints. Initially, we propose a novel vehicular fog cloud network (VFCN) which consists of different components and heterogeneous computing nodes. The ensure mobility privacy, the study devises Mobility Aware Blockchain-Enabled offloading scheme (MABOS). It extends blockchain enable multi-side offloading (e.g., offline offloading and online offloading) with proof of work (PoW), proof of creditability (PoC) and fault-tolerant techniques. The purpose is to offload all tasks under the secure network without any violation. Furthermore, to ensure Quality of Service (QoS) of applications, this work suggests linear search based task scheduling (LSBTS) method, which maps all tasks onto appropriate computing nodes. The experimental results show that devise schemes outperform all existing baseline approaches to the considered problem.

The secure distribution of protected content requires consumer authentication and involves the conventional method of end-to-end encryption. However, in information-centric networking (ICN) the end-to-end encryption makes the content caching ineffective since encrypted content stored in a cache is useless for any consumer except those who know the encryption key. For effective caching of encrypted content in ICN, we propose a novel scheme, called the Secure Distribution of Protected Content (SDPC). SDPC ensures that only authenticated consumers can access the content. The SDPC is a lightweight authentication and key distribution protocol; it allows consumer nodes to verify the originality of the published article by using a symmetric key encryption. The security of the SDPC was proved with BAN logic and Scyther tool verification.