The rapid iteration of artificial intelligence (AI) and 5G technologies has created essential foundations for Metaverse, easing bottlenecks in content generation, real-time interaction, and large-scale deployment. Integrating unmanned aerial vehicle (UAV) with semantic communication (SemCom) further provides a lightweight sensing and uplink solution, but semantic compression and bias may degrade fidelity. Generative AI (GAI), with powerful contextual modeling and generation capabilities, helps mitigate these issues. Based on this, this paper proposes a GAI-aided SemCom architecture and evaluates the feasibility of applying it to low-altitude internet of things (IoT) networks. We build evaluation models for representative image compression and GAI-aided SemCom techniques, replacing traditional task assumptions with realistic cases. Then, taking UAV networks as an example, we design a path planning and two task allocation schemes. Specifically, the UAV path planning strategy together with a greedy task allocation scheme improves the Metaverse update frequency and ensures the data freshness, while a parametrized deep Q-network (PDQN) task allocation scheme jointly optimizes Metaverse fidelity and freshness through mixed action selection, i.e., preprocessing methods (discrete) and compression size (continuous). Extensive analysis and numerical results corroborate that GAI-aided SemCom technology has practical significance in low-altitude IoT networks. Through the collaboration of multiple data processing schemes and the rational resource allocation, the fidelity and freshness of Metaverse can be greatly improved.

Mars exploration holds the potential to uncover the origin of life, which requires a reliable and sustainable electrical power supply to support long-duration missions. Wireless Power Transfer (WPT) based on Space Solar Power Satellites (SSPSs) has emerged as a promising option due to its continuous 24 × 7 availability and renewable nature. In particular, Laser-based WPT (LWPT) is especially suitable for space energy transmission due to its narrow beam width and high directivity. However, the extremely long propagation distance, high dynamics, and harsh conditions such as atmospheric attenuation, dust storms, and plasma effects degrade the transmission efficiency. To improve the robustness of energy delivery under such uncertain conditions, we incorporate Intelligent Reflecting Surfaces (IRS) to reconfigure the wireless propagation environment. Specifically, IRSs are exploited to provide alternative reflective transmission paths and enable adaptive wavefront control at the Mars rover, thereby enhancing the reliability and efficiency of power transfer when the direct link is partially blocked or severely attenuated. Additionally, we consider the presence of a malicious rover attempting to steal energy or disrupt legitimate charging. To ensure secure charging, we propose an IRS-assisted Challenge-Response Physical-Layer Authentication (CR-PLA) scheme. The False Alarm (FA) and Miss Detection (MD) probabilities are adopted as key performance metrics to quantify the authentication reliability. Finally, we formulate an optimization problem to maximize the harvested energy at the legitimate rover by jointly optimizing satellite selection, active transmit beamforming, and the passive IRS reflection, subject to the FA and MD probabilities. To address this intractable non-convex problem, we propose an Alternating Optimization (AO) algorithm to solve it iteratively. Numerical results demonstrate that, compared with benchmark methods such as Successive Convex Approximation (SCA) and greedy approach, the proposed method significantly enhances the harvested energy while effectively reducing MD probability.

The autonomous driving, which integrates wireless communication, intelligent computing and environmental perception, not only improves traffic safety and reduces vehicle accidents, but also alleviates traffic congestion by optimizing traffic flow and brings comfortable and convenient travel experience to passengers. However, current autonomous driving technology is generally at the L3-L4 levels and faces many challenges, such as semantic segmentation. Semantic segmentation enables vehicles to correctly distinguish the surrounding environment, such as roads, vehicles, pedestrians, etc. It can assist drivers in perceiving the surrounding environment well and making correct decisions, improving driving safety. However, current semantic segmentation work mostly focuses on improving recognition accuracy and neglects inference speed. Slightly higher latency can easily prevent vehicles from making timely and correct decisions, leading to accidents such as car crashes. Therefore, we propose a Multi-Level Real-time Fusion Semantic Segmentation Network (MLRFNet) that improves inference speed while ensuring high semantic segmentation accuracy for autonomous driving. The MLRFNet utilizes two lightweight branches, achieving effectively extract RGB and depth features with low computational cost. In addition, the Feature Fusion Module (FFM) aggregates complementary features from them, while the Cross-Level Refine Module (CRM) merges high-level semantic features and low-level spatial information. Extensive experiments demonstrate that MLRFNet significantly improves the inference speed while ensuring high accuracy. On the Cityscapes validation set, MLRFNet achieves 251.8 FPS and 71.4% mIoU for 512 × 1024 images inputs.

As an emerging application of the Internet of Things (IoT), autonomous vehicles (AVs) has attracted widespread attention from scholars. Accurate vehicle positioning is crucial for AVs to navigate safely and efficiently. Among the key components of positioning systems, odometry plays a vital role in tracking the vehicle’s movement, especially when GPS is unavailable. Traditional single-modal odometry methods, which rely solely on either LiDAR or cameras, often experience limited accuracy in challenging environmental or weather conditions. Therefore, some researchers proposed an idea of combining information from both LiDAR and cameras, called LiDAR-camera odometry (LCO). However, the existing LCO methods face issues like insufficient data integration or complexity in system structure. To address these challenges, we propose a novel LCO method named EVP-LCO, which enhances the sparse features from LiDAR by pseudo-LiDAR. In our method, we use a data augmentation module to enrich the details of LiDAR point cloud. Furthermore, we devise a feature regrouping strategy in two-step Levenberg-Marquardt (LM) optimization process to estimate accurate pose and reconstruct colorful global map. The results on the KITTI Odometry dataset show that EVP-LCO significantly improves vehicle positioning accuracy.

Intelligent vehicles are becoming an essential means of transportation for users, which provide them with high-quality services and convenient travel experiences. It should be noted that while the number of intelligent vehicles is growing rapidly, the incidence rate of traffic accidents is also increasing synchronously. Thus, some researchers try to reduce the risks through trajectory prediction. The current trajectory prediction schemes can be divided into single-vehicle-based scheme and vehicle-road collaboration scheme. However, single-vehicle-based scheme is lack in the states of surrounding vehicles and environment. Vehicle-road collaboration scheme is unable to overcome the inevitably limited field of view. In addition, present trajectory prediction schemes are limited by the computing resources of vehicles. Digital twin has become a hot topic due to its high computing speed and adequate environment information based on its cloud platform. Combining digital twin and trajectory prediction can not only broaden the vehicle’s field of view but also alleviate vehicle computing resources. For this, we first propose an adaptive trajectory prediction scheme based on digital twin platform. The whole computational process is placed in the cloud to alleviate the limited computing power while improving the computing speed. Vehicles can obtain the information of surrounding environment and the prediction results in real time, which improves the accuracy of the data used for prediction. Moreover, we utilize multichannel attention mechanism to learn the trajectory’s relevance weights adaptively, which improves the processing speed of scheme. The experimental results show that the performance of our scheme is better than others, so that drivers can adjust their routes to avoid collisions.

With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.

The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.

Roadside units (RSUs) play a vital role in intelligent transportation systems (ITS), working as critical elements in delivering superior Internet of Vehicles (IoV) services. A large service coverage and fast accident information diffusion RSU deployment solution can reliably ensure the ITS’ quality of service. Simultaneously, with the development of the city and the ITS, changes in traffic flow lead to RSU load imbalance, which will reduce the benefit of the original RSU deployment, and it is necessary to adjust RSU locations with minimal cost. Besides, due to the high visibility of the ITS, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment work. Specifically, Sybil attack is one of the most dangerous attacks against ITS, and it can reshape the network state by forging multiple identities, interfering with risk sensing, etc. Motivated by these, we respectively propose the PSO-meme joint heuristic deployment algorithm (PJHDA) and the heuristic RSU multi-objective adaptation adjustment algorithm (HRMA3) to carry out deployment and adaptation adjustment of the city’s RSUs, taking into account the constraint of Sybil attack detection. Numerical results demonstrate that the multi-attribute performance of PJHDA is superior to the existing schemes. Compared with benchmark schemes, the HRMA3 excels in achieving advanced service coverage and load balancing while controlling costs, and both proposed schemes exhibit higher Sybil attack detection rate.

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios.

UAV-enabled multi-access edge computing (MEC) is expanding possibilities for integrated space-air-ground networks, especially in the 5G era and beyond. In this scenario, tasks from mobile users (MUs) are offloaded to nearby UAVs for execution, with results returned upon completion. However, the unpredictable mobility of MUs, coupled with dynamic network conditions and fluctuating resource availability, can degrade the reliability of communication links, leading to increased delivery latency, particularly for tasks involving large computational results. To meet stringent QoS requirements, adaptive task migration across UAVs is essential to minimize latency. To address this issue, in this paper, we first investigateComputationTaskMiGration (CTMiG) problem in UAV-enabled dynamic MEC networks, focusing on joint optimization of task-serving (offloading and migration) decisions to reduce latency for all MUs. We propose the ILCTS algorithm, an imitation learning-based joint optimization method that adaptively adjusts scheduling strategies in response to environmental changes. An improved PPO algorithm is first proposed to train a policy and generate expert data, followed by generative adversarial imitation learning to imitate the data and continuously explore new ones through online learning to enhance the policy. Experimental results demonstrate that our algorithm achieves superior performance in training accuracy and average latency compared to other representative methods.

The development of Autonomous Vehicles (AVs) provides users with high-quality services and convenient travel experiences. As one of the most important functions in the automotive field, mobile positioning has attracted widespread attention from scholars. However, using a single-modal sensor (LiDAR or camera) poses challenges for precise localization due to their measurement flaws. Therefore, some scholars have proposed Visual-LiDAR Odometry (VLO). Nevertheless, most of the existing VLO solely use a single-modal sensor as their main framework and utilize another sensor for optimization, which does not fully leverage the complementary behavior of sensors in different environments. Thus, this paper presents a novel LiDAR-camera fusion method for improving the odometry estimation. Firstly, we employ a depth completion network to convert the image into pseudo-LiDAR to compensate for the missing depth values in the LiDAR point clouds. Then, we adopt Bayesian inference to enhance the robustness of the fusion method in different environments. Finally, evaluations on the public KITTI odometry show that the proposed method outperforms several state-of-the-art methods.

The development of 5G-vehicle-to-everything (5G-V2X) technology makes vehicle-road-cloud collaboration possible. Vehicles and roads transmit sensor data to the cloud via 5G-V2X technology and then the cloud sends the data to the target vehicle. The target vehicle utilizes dynamic environmental data from surrounding vehicles and roadside units to predict the driving trajectory of surrounding vehicles in order to ensure its own safety. However, many existing trajectory prediction schemes are based on incomplete single-vehicle perception and ignore surrounding road conditions, which will greatly limit their value in real-world scenarios. Therefore, this paper proposes a flexible multi-channel vehicle trajectory prediction scheme based on vehicle-road collaboration. Specifically, we first design a flexible multi-channel vehicle trajectory prediction scheme that can extract different vehicle and map features from various information sources. Then, we use the Transformer model to generate predicted trajectories of surrounding vehicles by fusing features from different sources, and achieve parallel computing effects. The most popular dataset, INTERACTION, is used to evaluate the proposed scheme. The results show that our scheme is robust across different scenarios and possesses better accuracy.

As a critical element in intelligent transportation systems (ITS), roadside units (RSUs) are pivotal in delivering superior Internet of Vehicles (IoV) services encompassing intelligent traffic management, accident prevention, and emergency rescue. Considering the high deployment and maintenance costs of RSUs, many studies focus on the efficient RSU deployment issues. However, due to the high visibility of the ITS system, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment researches. Specifically, the Sybil attack is one of the most dangerous attacks against ITS, it can reshape the network state by forging multiple identities, interfering with the operator’s reputation assessment or causing severe DDoS. Therefore, we propose a joint heuristic scheme that combines the advantages of particle swarm optimization and double local-search memetic algorithm to solve the city RSU deployment problem in Sybil attack environments. It can find solutions with higher fitness values and guarantees that the IoV has the capability to detect Sybil attacks. Numerical results show that our proposed scheme not only outperforms other traditional solutions regarding signal validity coverage, overlap rate, and initial propagation speed of accident information, but also performs satisfactorily in Sybil attack detection.

With the advantages of seamless coverage and ubiq-uitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services to reduce End-to-End (E2E) delay and alleviate the network traffic for future 6G applications including autonomous driving, eHealth, and metaverse. However, the ultra-density of LEO satellites complicates the selection of caching nodes, while the heteroge-neous caching hardware and communication environments make optimization of content deployment highly difficult. To address these issues, we propose an intelligent hierarchical caching and asynchronous updating scheme. Specifically, a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme is first adopted to select the caching LEO satellites to reduce the system propagation delay. Then, the Multi-Agent Reinforcement Learning-based Hi-erarchical Caching and Asynchronous Updating (MARL-HCAU) strategy is proposed to meet caching service demands. Simulation results illustrate that compared with the benchmarks, the overall cache hit ratio increases by 14.2 % with the reduced packet drop rate and transmission delay by 8.78 % and 0.94s, respectively.

With the advances in artificial intelligence, robotics, and data fusion, large numbers of drones operating in a coordinated manner will become commonplace for a wide range of commercial and military uses. At present, the application methods of drone swarms are mainly divided into fully autonomous methods and controlled methods with human participation. Because of the limited level of artificial intelligence, controlled drone swarms will be the main way for the application of large-scale drone swarms for a long time. However, there is less research on achieving global coordination in a limited time for a controlled large-scale drone swarm. Therefore, a new large-scale drone swarm framework is proposed firstly in this paper, which achieves global coordination through local interaction and reduces the impact of limited channel resources. Secondly, this paper proposes a local interaction-based fast coordination method and introduces a prediction mechanism, to ensure that large-scale drone swarms can quickly achieve coordination even in the presence of node loss. Moreover, the numerical integration method is used to update the consensus state, so that the drones can increase the iteration period, reduce the number of packets, and further reduce the channel burden. Finally, considering that large-scale drones swarm are usually composed of drone swarms launched at different locations and times, a consensus algorithm considering the merging behavior of drone swarms is also proposed. The simulation results show that the large-scale drone swarm using the proposed architecture can achieve the leader-follower consensus in a very short time and even in a confrontational environment with poor communication conditions. Besides, after the merger of multiple drone swarms, the consensus problem can still be solved in very few iteration cycles.

Behavior-oriented decision-makers are critical components in generating intelligent decisions for user virtual interactions in metaverse. In this work, we study the efficiency and security of behavior-oriented decision-makers in metaverse from perspective of autonomous driving (AD), where modeling human uncertain driving behaviors is the key factor of their performance. We first explore the ability of different deep-neural-network-based decision-makers used in deep reinforcement learning for efficient autonomous vehicle control, and then we propose a novel neural backdoor attack against them using spatiotemporal driving behaviors, rather than an immediate state. With our attack, the adversary acts as a normal driver and can trigger attacks by driving his vehicle following specific spatiotemporal behaviors. Extensive experiments show that our proposed backdoor attack can achieve high stealthiness and effectiveness (less than 1% clean performance variance rate and more than 98% attack success rate) on behavior-oriented decision-makers, and is sustainable against existing advanced defenses.

The Internet of Vehicles (IoV) is a large-scale interactive network that operates in a dynamic and changeable environment, encompassing diverse types of private information. In recent years, safeguarding vehicle location privacy in IoV has been a topic of concern. However, the independent location privacy protection mechanisms cannot consistently meet the rigorous security requirements of various IoV scenarios, which will pose a significant threat to the location privacy of IoV users. In contrast, risk sensing as a preventive security strategy needs lower computing costs and is more suitable for the intricacies of complex city environments. Unfortunately, the existing works lack that combination of risk assessment with trust assessment to conduct a comprehensive study on location privacy risk sensing. Therefore, considering the city vehicles’ spatial clustering phenomenon and the strong regularity of traffic flow, we propose a risk-sensing approach to vehicle location privacy based on the continuous adaptive risk and trust assessment strategy. This approach employs the Ripley method to analyze space clustering characteristics and combines the traffic flow prediction model to establish the risk assessment scheme. Furthermore, to enable our risk-sensing approach to have historical memory that can identify and continuously track malicious users, we incorporate a penalty factor into the trust assessment scheme that updates in a time iterative format. Extensive numerical results demonstrate the adaptability and reliability of our proposed risk-sensing approach to vehicle location privacy.

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Mobile edge computing (MEC) has emerged as a promising approach to address the time-sensitive requirements of mobile Internet of Vehicles (IoVs) systems. Unfortunately, the current deployment density of roadside units (RSUs) is relatively sparse, and the direct V2I communication coverage is limited, making it impossible to meet the communication and computing requirements of all vehicles. There is an urgent need for V2V communication to assist V2I communication, which can achieve a wider coverage of RSUs, a diversified selection of task processing locations, and even load balancing between RSUs. However, V2V communication also faces a series of challenges. On the one hand, due to the sparsity, time-varying, and high-speed mobility of vehicle nodes in IoVs, the selection of collaborative communication paths becomes more difficult. On the other hand, there are inevitably malicious vehicles in IoVs, and how to achieve efficient task processing while ensuring privacy and driving safety is also a problem worth studying. Existing research generally optimized the delay of direct V2I task offloading, ignoring the necessity of V2V-assisted communication and the presence of malicious communication nodes. To address the above challenges, we present a vehicular edge computing network structure with multiple communication modes, including V2V, V2I, etc, and use a recommended trust model to analyze the trust degree between the nodes in IoVs. Then, we discuss the issue of trusted task offloading for IoVs and propose a Deep Deterministic Policy Gradient (DDPG) scheme. The numerical results indicate that our proposed strategy outperforms current methods in terms of task offload latency and credibility.

To meet the future demands for ubiquitous communication coverage and temporary / unexpected computing resources, aerial computing networks have been envisioned as a new paradigm. Nevertheless, dynamic changes on the network make it particularly challenging to achieve global optimal resource allocation. As an emerging technology, digital twin (DT) can represent real objects in physical network by creating virtual models. With the help of DT, we can easily obtain comprehensive real-world high-fidelity state information for model training, so as to achieve intelligent efficient decision-making. Accordingly, DT-based aerial computing networks have emerged as a potential solution. Note that available researches mostly assumed simple ground user distribution like uniform distribution, and adopted binary / partial offloading in task processing, neglecting the task separability and data inter-dependency among subtasks. Toward this end, we introduce DT into aerial computing networks, and study the problem of intelligent UAV deployment and resource allocation. Specifically, we firstly propose a DT-assisted UAV deployment strategy and model the data inter-dependency among subtasks. After that, two DT-assisted hybrid (binary and partial) task offloading schemes are presented, i.e., heuristic greedy and DQN-based schemes. Extensive analysis and numerical results confirm the effectiveness of our proposed DT-assisted UAV deployment and hybrid task offloading strategies.

In order to meet the ever-increasing task processing demands of computation-intensive and delay-sensitive applications in the era of autonomous driving, a promising approach is to adopt nearby roadside units (RSUs) or/and vehicles passing by to provide edge computing services, i.e., vehicular edge computing (VEC). However, due to the untrustworthiness of fast-moving vehicles, the vehicles' tasks may face false result attacks or processing timeout. Note that there is little research on the vehicle trust evaluation in VEC networks, especially taking processing delay minimization into consideration. Toward this end, this paper studies the joint optimization problem of vehicle trust evaluation and task processing delay, aiming to ensure the security of the vehicles with tasks and minimize the task offloading delay. To solve this problem, we propose an efficient and trusted VEC offloading scheme based on fuzzy comprehensive strategy (FCS) and adopt the concept of game theory to motivate nearby vehicles to share computing resources. Experimental results corroborate that our proposed scheme can accurately evaluate the trustworthiness of vehicles and improve service security in VEC networks. Moreover, it can significantly reduce task offloading delay.

With the gradual commercialization of 5G, especially the widespread application of artificial intelligence (AI) technology, the Internet of Things (IoT) continues to expand and has integrated into every aspect of our lives. While enjoying the convenience brought by IoT, we also face unprecedented challenges, including ubiquitous and unpredictable demands for communication and computing resources. In consideration of their flexible deployment, low cost, and easy expansion, UAV edge computing IoT networks (UECINs), which adopt unmanned aerial vehicles (UAVs) to provide fast communication and computing services, have emerged as a promising solution. Note that there have been a number of studies focusing on UAV’s position deployment and trajectory design, resource allocation in UECIN. However, most existing works proposed short-term service provisioning systems with a fixed number of UAVs, ignoring the problem of UAVs’ limited battery power and the possible changes of ground users’ number, locations, and resource requirements. To address these issues, we present a dynamic UECIN framework with autonomous prediction characteristics, aiming to stably provide mobile-edge computing services for ground users in a certain area over a long period of time. This framework can not only support UAV’s dynamic entry and exit according to the real-time needs of ground users but also update their position deployment based on the distribution of ground users. As we know, we are the first to propose UECIN with a dynamic entry and exit mechanism. Besides, an efficient and load-balancing task allocation scheme is further given, and extensive analysis and numerical results corroborate the feasibility and superior performance of our framework.

The development of software-defined industrial networks (SDIN) promotes the programmability and customizability of the industrial networks and is suitable to cope with the challenges brought by new manufacturing modes. For building more scalable and reliable SDIN, a distributed control plane with multicontroller collaboration becomes a promising option. However, as the brain of SDIN, the security of the distributed control plane is rarely considered. In addition to suffering direct attacks, each controller is also subjected to attacks propagated by other controllers because of information sharing or management domain takeover, resulting in the spread of attacks in a wider range than a single controller. Therefore, in this article, we study attacks against SDIN with distributed control plane, demonstrate their propagation across multiple controllers, and analyze their impacts. To the best of our knowledge, we are the first to study the security of SDIN with distributed control plane. In addition, since the existing defense mechanisms are not specifically designed for distributed SDIN and cannot defend it perfectly, we propose an attack mitigation scheme based on deep reinforcement learning to adaptively prevent the spread of attacks. Specifically, the novelty of our scheme lies in its ability of learning from the environment and flexibly adjusting the switch takeover decisions to isolate the attack source, so as to tolerate attacks and enhance the resilience of SDIN.

Increasingly serious data security and privacy protection issues make federated learning (FL) gradually evolve to be an important technology in the field of artificial intelligence (AI). Meanwhile, in consideration of the huge demands for network access and computing resources from massive IoT devices, ultra-dense edge computing (UDEC), which integrates mobile edge computing (MEC) and ultra-dense network (UDN), has turned out to be a promising network architecture in the era of 5G and even 6G. Facing requirements on ultra-low processing latency, performing FL for UDEC confronts many challenges, one of which is how to relieve the barrel effect caused by the difference in computing power of local devices while ensuring overall FL efficiency. Nevertheless, little work can be found in this area. Toward this end, the paper takes the lead in studying FL for UDEC, and proposes an inter-server collaborative federated learning method by grouping the servers and clients. Theoretical analysis and numerical results corroborate that our proposed inter-server collaborative method can significantly reduce the waiting time during local training without reducing the learning accuracy, thus improving the overall efficiency.

Due to the outstanding characteristics of unmanned aerial vehicles (UAV), i.e., maneuverability and flexibility, UAV enabled mobile edge computing (MEC) has become a widely attractive research direction. However, single-UAV cannot be qualified for numerous tasks and application scenarios in view of its limited computing capacity, while multi-UAV enabled MEC is still in the initial stage, and most existing work transformed the problem of multi-UAV enabled MEC into multiplied single-UAV. The UAV swarm can make UAVs cooperate intelligently, and accomplish diversified tasks in complex environments at low cost, which is regarded as a promising development direction of UAV technology. Nevertheless, it is inefficient since each UAV node is responsible for both communication and computation, and multi-hop transmission among UAVs may lead to a very high delay. Toward this end, the paper takes the lead in studying the problem of grouping and role division in UAV swarm-based edge computing, and puts forward a grouping and role division algorithm to solve it. Final experimental results corroborate that the complexity of our algorithm is less than that of the traditional algorithm, and role division can maximize the use of communication and computing resources.

Mobile edge computing (MEC) has been envisioned as a promising technology to meet ever-increasing demands on computational resources. Due to the fixed deployment and limited coverage of conventional MEC, unmanned aerial vehicle (UAV) edge computing began to receive attention with the advantage of flexibility and controllability. However, single UAV edge computing is not competent for complex scenarios with the limitations of computing capability and coverage. Further-more, although multi-UAV edge computing could improve the situation, the long processing delay and insufficient utilization of resources still restrict the communication and cooperative computation among UAVs. Characterized by the unique swarm cooperative communication and computing, UAV swarm-based edge computing can realize more complex task computing and higher computational efficiency. Toward this end, we provide this paper to study the cooperative task offloading problem in UAV swarm-based edge computing, aiming to minimize the overall task processing delay. To solve this problem, we adopt an optimal cooperative computation offloading method. Experimental results demonstrate the importance and high performance of UAV swarm-based edge computation and the low complexity of our proposed method.

Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs' limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.

In conventional terrestrial cellular networks, mobile devices at the cell edge often suffer from poor channel conditions, and thus unmanned aerial vehicles (UAVs) are introduced in recent years to improve the reliability of communication links. However, with the rapid development of Internet of Things (IoT) technology, the emerging IoT applications have blooming demands for high computation capacity from the resource-constrained IoT mobile devices (IMDs), motivated by which, mobile edge computing has been envisioned as an appealing solution to the resource bottleneck problem of IMDs. In order to cope with poor communication performance and high computation demands of cell-edge IMDs, we in this paper leverage UAV-aided edge computing to collaboratively assist computation offloading, taking account of the limited battery life of both IMDs and the UAV. We investigate a joint optimization problem of collaborative computation offloading, bandwidth portion, bit allocation, and UAV trajectory design, aiming to minimize the weighted energy consumption of IMDs and the UAV. Extensive numerical results validate the necessity of introducing UAV-aided edge computing to cellular networks, and the advantages of our proposed scheme on energy savings.

The prompt development of wireless communication network and emerging technologies such as Internet of Things (IoT) and 5G have increased the number of various mobile devices (MDs). In order to enlarge the capacity of the system and meet the high computation demands of MDs, the integration of ultra-dense heterogeneous networks (UDN) and mobile edge computing (MEC) is proposed as a promising paradigm. However, when massively deploying edge servers in UDN scenario, the operating expense reduction has become an essential issue to be solved, which can be achieved by computation offloading decision-making optimization and edge servers' computing resource configuration. In consideration of the complicated state information and ever-changing environment in UDN, applying reinforcement learning (RL) to the dynamical systems is envisioned as an effective way. Toward this end, we combine the deep learning with RL and propose a deep Qnetwork based method to address this high-dimensional problem. The experimental results demonstrate the superior performance of our proposed scheme on reducing the processing delay and enhancing the computing resource utilization.

The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the configuration of computing resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing resource configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.

To meet the surging demands on network throughput and spectrum resources arising with billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to be a promising technology, which gives rise to the so-called ultradense Internet-of-Things (IoT) networks. Meanwhile, with the constant emergence of new IoT applications, the conflict between computing-intensive applications and resource-constrained IMDs is increasingly prominent. By offloading computing-intensive tasks to the edge servers in close proximity, mobile-edge computing is expected as an effective solution to address this issue. However, computation offloading research in ultradense IoT networks is still scarce until now. Toward this end, we provide this paper to study the energy-aware task offloading problem with multiple edge servers in ultradense IoT networks, where diverse kinds of computation tasks are randomly requested by the IMDs and the computing resources at the edge servers change dynamically. An iterative searching-based task offloading scheme is proposed as our solution, which jointly optimizes task offloading, computational frequency scaling, and transmit power allocation. Extensive numerical results demonstrate the superior performance of conducting task offloading among multiple edge servers, and corroborate the advantages of our scheme over existing works which either fixed computational frequency and transmit power, or neglected the impact of the IMDs' residual battery.

In order to meet the ever-increasing demands on computational and spectrum resources in the era of 5G and Internet of Things (IoT), mobile-edge computing (MEC) and ultra-dense heterogeneous network (UDN) have been envisioned as two promising technologies, which gives rise to the so-called ultra-dense edge computing. Note that existing works on task offloading for ultra-dense edge computing mostly considered simple task offloading scenarios, ignoring the random request for types of computation tasks from the mobile devices (MDs) and the random arrival of the tasks at the edge servers. Toward this end, we provide this paper to study the multi-user task offloading problem in ultra-dense edge computing with multiple types of tasks requested by the MDs. To minimize the MDs' energy consumption and thus prolong their battery lifetime, task offloading, computation frequency scaling, and transmit power allocation are jointly optimized in this paper. After that, the problem is divided into two subproblems, i.e., local energy minimization, and joint task offloading and transmit power allocation. A game-theoretical joint offloading scheme is proposed as our solution. Extensive numerical results corroborate the superior performance of our proposed scheme rather than those with single edge server, fixed computation frequency and transmit power at the MDs.

To meet the mobile devices' surging demands for throughput and computation resources in 5G networks, heterogeneous networks (HetNets) and multi-access edge computing (MEC) are expected to be two key distinct but complementary technologies. By offloading the computation tasks of the mobile devices (MDs) to the nearby MEC servers at the edge of radio access networks, MEC can largely augment the MDs' computation resources and battery lifetime. However, existing research on mobile-edge computation offloading only focused on multi-user single-MEC scenarios and little work can be found on designing computation offloading schemes for the case of multi-user multi-MEC. Toward this end, we investigate the problem of collaborative mobile-edge computation offloading in 5G HetNets and propose a game-theoretical computation offloading scheme. Numerical results corroborate that our collaborative computation offloading scheme for multiple MEC servers can not only reduce the overall computation overhead efficiently, but also achieve a Nash equilibrium in a finite number of steps.

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.

With the development of Internet of Things and intelligent transport system, the intelligent connected vehicle (ICV) represents the future direction of the vehicle industry. Due to the open wireless medium, high speed mobility and vulnerability to environmental impact, vehicle data faults are inevitable, which may lead to traffic jam or even accident threatening the life of the driver and passengers. At present, there are few studies for fault detection and repairing of ICV while using traditional methods directly for ICV has a low accuracy. In this paper, we propose a threshold-based fault detection and repairing scheme using a dynamic Bayesian network (DBN) model, which can obtain the temporal and spatial correlations of vehicle data for accurate real-time or history fault detection and repairing. In addition, we give an algorithm of how to select the threshold to achieve the best effect by history data before fault detection and repairing process. Finally, simulation results show that the proposed scheme possesses a good fault detection and repairing accuracy as well as a low false alarm rate compared to other available methods.

In order to address the conflict between resource- hungry mobile applications and resource-constrained mobile devices (MDs), mobile-edge computing (MEC), which offers cloud computing capabilities at the edge of networks in close proximity to the MDs, is envisioned to be a promising approach. However, existing mobile-edge computation offloading studies only took the resource allocation between the MDs and MEC servers into consideration, and ignored the resource allocation between MEC and centralized cloud computing servers. Moreover, current MEC Hosted Networks mostly adopt the networking technology integrating cellular and core networks, which has the shortcomings of single networking mode, high congestion, high latency and energy consumption. Toward this end, we provide in this paper an architecture of centralized cloud and distributed MEC over hybrid fiber-wireless network, which has the features of supporting diverse network techniques, easy expansibility, high capacity and reliability, low latency and energy consumption. The problem of cloud-MEC collaborative computation offloading is studied and an approximation collaborative computation offloading scheme is proposed as our solution. Numerical results corroborate the energy efficiency of our proposed collaborative scheme.

Mobile edge computing (MEC), as an extension of the cloud computing paradigm to the edge network, is a promising solution to provide resource-intensive and time-critical applications to mobile users. It overcomes some obstacles of traditional mobile cloud computing by offering ultra-short latency and less core network traffic. This paper proposes a new framework based on the architecture of MEC to deliver cloud services to the edge. We introduce enumeration based optimal placement algorithm (EOPA) and divide-and- conquer based near-optimal placement algorithm (DCNOPA) to attain minimal data traffic by distributing virtual machine replica copies (VRCs) of applications to the edge network. Simulation results show that compared to the famous K-medians clustering algorithm (KMCA), the performance of DCNOPA is much closer to that of EOPA with lower computational complexity. Furthermore, we investigate the optimal number of VRCs within a given limitation of benefit-to-cost ratio.