The attack surface of Industrial IoT (IIoT) is enlarged by the interconnected devices and systems. Although many works have facilitated advanced approaches to help industrial entities against possible cyber threats, they may overlook the rich operational context of manufacturing processes, leaving the security evaluation context-agnostic. Recognizing that cyber attacks and production activities are increasingly intertwined, this paper introduces Meta-KadaSec, a novelMeta-Knowledge-drivenadaptiveSecurity provisioning approach that embeds security context within the natural operational fabric of manufacturing environments. Our approach integrates: (1) STKG-PPO, a reinforcement learning model that leverages manufacturing contextual knowledge through Knowledge Graphs with Spatial-Temporal associations; and (2) Reptile-CMDPs, a meta-reinforcement learning approach that enables rapid adaptation across diverse manufacturing contexts with theoretical guarantees for convergence. Evaluations are driven by realistic attack vectors from the Edge-IIoTset dataset and an open source factory simulator, demonstrating that our context-embedded model STKG-PPO improves production efficiency by 52.5% and reduces convergence time by 6.7% compared to context-agnostic baselines. Furthermore, our meta-learning approach Reptile-CMDPs accelerates adaptation, achieving 95.2% higher average rewards compared to training from scratch.

Dynamic multi-robot systems face the intricate multi-agent pathfinding (MAPF) challenge as a pivotal hurdle. It has been uncovered through recent research that tackling MAPF issues can be effectively approached through reinforcement learning, offering a fully decentralized solution. Nonetheless, the escalation in the scale of the multi-robot system introduces sample inefficiency, posing a significant barrier for learning-based methods. We introduce a novel hierarchical reinforcement learning architecture aimed at addressing large-scale MAPF by leveraging spatial and temporal abstraction. This approach enhances exploration efficiency by recognizing intermediate rewards. The framework employs an upper-tier controller that segments the map into linked regions, thereby streamlining the optimization of agents' paths on a regional basis to foster improved global outcomes. To tackle each segmented problem, a subordinate-level controller is designed, which integrates heuristic directions and an inter-agent communication strategy. The merit of our methodology is confirmed by empirical experiments, showcasing advancements over prevailing methods in success rates and reduction in completion time across test scenarios of various magnitudes.

Vehicular Fog Computing (VFC) is significantly enhancing the efficiency, safety, and computational capabilities of Intelligent Transportation Systems (ITS), and the integration of Unmanned Aerial Vehicles (UAVs) further elevates these advantages by incorporating flexible and auxiliary services. This evolving UAV-integrated VFC paradigm opens new doors while presenting unique complexities within the cooperative computation framework. Foremost among the challenges, modeling the intricate dynamics of aerial-ground interactive computing networks is a significant endeavor, and the absence of a comprehensive and flexible simulation platform may impede the exploration of this field. Inspired by the pressing need for a versatile tool, this paper provides a lightweight and modular aerial-ground collaborative simulation platform, termedAirFogSim. We present the design and implementation of AirFogSim, and demonstrate its versatility with five key missions in the domain of UAV-integrated VFC. A multifaceted use case is carried out to validate AirFogSim's effectiveness, encompassing several integral aspects of the proposed AirFogSim, including UAV trajectory, task offloading, resource allocation, and blockchain. In general, AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains. Our code will be available athttps://github.com/ZhiweiWei-NAMI/AirFogSim.

In cellular networks, cell handover refers to the process where a device switches from one base station to another, and this mechanism is crucial for balancing the load among different cells. Traditionally, engineers would manually adjust parameters based on experience. However, the explosive growth in the number of cells has rendered manual tuning impractical. Existing research tends to overlook critical engineering details in order to simplify handover problems. In this paper, we classify cell handover into three types, and jointly model their mutual influence. To achieve load balancing, we propose a multi-agent-reinforcement-learning (MARL)-based scheme to automatically optimize the parameters. Experimental results show that our proposed scheme outperforms existing benchmarks in balancing load and improving network performance.

The multi-drone-truck collaborative delivery, where unmanned trucks serve as mobile supply stations for drones, effectively combines the strengths of both vehicles and presents wide application prospects. But the majority of existing literature restricts drone launch and retrieve operations (LARO) to stationary trucks, and potential drone route collisions are mostly ignored. This leads to inability to fully exploit the capability of drones. We address these gaps and introduce a new variant of multi-drone-truck collaborative delivery. However, the scheduling for drones and truck faces high-dimensional solution space and complex constraints, making it almost impossible for centralized solving. To this end, we develop a hierarchical solution framework that decomposes the complete problem into two levels of subproblem. The upper solver centrally allocates tasks and schedules when drones to launch, while the lower solver, based on multi-agent reinforcement learning (MARL), plans paths for each drone agent in a decentralized but cooperative manner. In addition, we validate the effectiveness of our method by benchmarking it against three state-of-the-art approaches, demonstrating its superiority in terms of both efficiency and collision avoidance.

Achieving advanced UAV autonomy is increasingly pivotal in complex low-altitude environments, while developing such AI-driven autonomous capabilities critically depend on benchmarking suites. However, existing solutions often address fundamental, isolated domains (e.g., network-centric, traffic-centric), failing to capture the intricate, comprehensive mission-level interactions in low-altitude operations. This paper introduces AirFogSim, a high-fidelity simulation platform for validating UAV autonomy in low-altitude missions. The cores of AirFogSim are a novel workflow-agent-state (WAS) modeling approach and standardized APIs for external data sources, empowering AirFogSim to simulate diverse UAV behaviors under realistic, dynamic environmental conditions. To demonstrate the practical utility, we generate a benchmark dataset for UAV situation awareness with varied weather and electromagnetic interference conditions. Multiple AI techniques are implemented and evaluated on this dataset, including traditional machine learning algorithms and large language models (LLMs). The platform is currently open-sourced on GitHub.

The evolving Industrial Internet of Things (IIoT) is shifting towards decentralized collaborative manufacturing, posing heightened network security issues within interconnected value chains, thus requiring advanced Network Intrusion Detection (NID) systems to identify potential threats. In this context, traditional centralized NID systems are insufficient due to cross-industrial privacy concerns and interconnected secure threats. Federated Learning (FL) has emerged as a promising solution to enable the sharing of security insights without compromising privacy across participants. However, establishing an FL-based NID framework in realistic IIoT scenarios faces several hurdles, including the limited availability of large-scale devices and heterogeneous attack data distributions. The former leads to inconsistent client participation and degraded performance, while the latter hinders model convergence. To address these, we propose a novel Federated Learning-based Industrial Network Intrusion Detection (FedIn-NID) framework, incorporating a multidimensional client selection strategy and a dynamic global aggregation strategy. The selection strategy synergistically considers multidimensional factors including client availability, local dataset distribution, and dataset size. This approach accommodates clients with varying availability and avoids the selection of biased clients with data concentrated in a few categories. During model aggregation, the proposed strategy leverages the concept of exponential moving average to dynamically balance the holistic yet slightly older knowledge in the global model with the partial but relatively newer knowledge in local models, ensuring effective aggregation and convergence of the global NID model. Experiments demonstrate that FedIn-NID outperforms baselines by 10% to 30%, showcasing remarkable robustness with increasing data distribution heterogeneity and device count.

Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slices in IIoT, this paper proposes a novel slice-specific secure IIoT (SSIOT) architecture with an AI-enabled solution. The SSIOT architecture separates the control and data planes, where the control plane orchestrates the Security Service Function Chains (SSFC) across network slices and the data plane analyzes the slice-specific features like traffic patterns, resource SLA guarantees, and Virtual Security Network Function (VSNF) dependencies. To extract these spatial-temporal features from the dynamic IIoT environments, we facilitate the powerful deep reinforcement learning (DRL) methods and propose a structural GS2L approach. GS2L is maliciously designed with the core principles of graph convolutional network (GCN) and Gated Recurrent Unit (GRU), enabling a thorough understanding of physical resource distribution and the request dynamics across slices. Extensive experiments are conducted in diverse IIoT slices with the real-world USNet and fat-tree topologies. Simulation results demonstrate that GS2L outperforms state-of-the-art learning and heuristic benchmarks, showcasing an overall 15.2% improvement with efficient and stable resource utilization.

In this paper, we focus on the task offloading problem for zero-trust vehicular fog computing (VFC) to promote trustworthy collaborative computing among vehicles. We propose a blockchain-enabled zero-trust VFC framework (BlockZT-VFC) to continuously verify and dynamically authorize the vehicle nodes. To overcome the reliability and efficiency issues in BlockZT-VFC, a multi-attribute task offloading and group-based continuous verification (MTOCV) scheme is designed. In particular, multiple attributes are extracted from both vehicles and tasks, ensuring that tasks are offloaded to FVs with authorized trustworthiness, and the group-based continuous verification efficiently guarantees the resulting authenticity by evaluating independent probability mathematically. Experimental simulations demonstrate an 18% increase in throughput and a 34% decrease in latency across two different scenarios, showcasing the superiority of our mechanism in improving system performance and reliability.

The Industrial Internet of Things (IIoT) continues to evolve alongside advancements in 5G and beyond 5G communication technologies, and network slicing has emerged as a promising technique to offer isolated slices and tailored services across industrial use cases, which profoundly affects the traditional security solution. As the future IIoT evolves towards the post-5G era, the anticipated growth in network slices will unavoidably exacerbate challenges in security service management, but developing an adaptive and effective security strategy remains a significant challenge. This study introduces a novel slice-specific IIoT (SSIOT) architecture, meticulously crafted to address the unique security needs of each slice based on network function virtualization. To adapt to the SSIOT, an AI-driven GS2L model is presented, which combines graph convolutional network (GCN) with sequence-to-sequence (Seq2Seq) deep reinforcement learning (DRL). The GS2L offers the network topology explanation module, service request analysis module, and slice-specific distribution extraction module, and adeptly navigates the multifaceted Security Service Function Chain (SSFC) embedding conundrum and resource allocation in slice-specific systems. Comprehensive experimental evaluations underscore GS2L's superiority, showcasing its proficiency in delivering augmented QoS satisfaction while ensuring prudent resource utilization over the learning-based and heuristic benchmark.

While autonomous intersection management (AIM) emerges to facilitate signal-free scheduling for connected and autonomous vehicles (CAVs), several challenges arise for planning secure and swift trajectories. Existing works mainly focus on addressing the challenge of multi-CAV interaction complexity. In this context, multi-agent reinforcement learning-based (MARL) methods exhibit higher scalability and efficiency compared with other traditional methods. However, current AIM methods omit discussions on the practical challenge of CAV heterogeneity. As CAVs exhibit different dynamics features and perception capabilities, it is inappropriate to adapt identical control schemes. Besides, existing MARL methods that lack heterogeneity adaptability may experience a performance decline. In response, this paper exploits MARL to model the decision-making process among CAVs and proposes a novel heterogeneous-agent attention gated trust region policy optimization (HAG-TRPO) method. The proposed method can accomplish more effective and efficient AIM with CAV discrepancies by applying a sequential update schema that boosts the algorithm adaptability for MARL tasks with agent-level heterogeneity. In addition, the proposed method utilizes the attention mechanism to intensify vehicular cognition on disordered ambience messages, as well as a gated recurrent unit for temporal comprehension on global status. Numerical experiments verify that our method results in CAVs passing at the intersection with fewer collisions and faster traffic flow, showing the superiority of our method over existing benchmarks in terms of both traffic safety and efficiency.

Unmanned aerial vehicle (UAV) delivery in urban environments demonstrates significant growth potential due to its efficiency and eco-friendliness. The challenges of autonomous navigation and obstacle avoidance in complex environments are crucial study aspects of UAV delivery. This paper proposes a depth camera-LiDAR fusion based UAV autonomous navigation protocol, enabling autonomous navigation and obstacle avoidance for UAV parcel delivery in complex urban environments. Experiments are conducted in a highly realistic simulation environment to validate performance of the proposed method. The results indicate that the proposed method can efficiently and accurately achieve autonomous navigation and obstacle avoidance with high robustness.

The rise of collaborative manufacturing, driven by the rapid proliferation of Industrial Internet of Things (IIoT) technologies, has markedly enhanced agility and productivity in industrial environments. However, this advancement has also significantly broadened the attack surface and uncovered unique vulnerabilities intrinsic to these interconnected systems. This paper introduces a novel Hierarchical Federated Incremental Learning Network Intrusion Detection (HFIN) approach. To our knowledge, this is the first attempt to address the ever-evolution network intrusion detection (NID) challenges in IIoT landscapes from the continuous attack-defense perspective. Our proposed HFIN capitalizes on decentralized model training across multifarious IIoT devices, ensuring data privacy and empowering continuous learning capabilities. It utilizes distributed data sources for secure experience sharing, collaboratively enhancing the continuous detection performance of the global model. Furthermore, regarding the inherent resource constraints of IIoT devices, we proposed a novel edge-client Weighted Transmission Optimization strategy (WTO). This strategy adeptly balances effective intrusion detection with the operational constraints of IIoT devices. By holistically considering detection capabilities and data volume across different attack types, it prioritizes the transmission of more critical and scarce attack data for training within bandwidth constraints. This maintains the comprehensive detection capability of the global model against various network attacks. To validate the effectiveness of HFIN, we conduct extensive experiments using the NF-UQ-NIDS-v2 and NF-ToN-IoT-v2 datasets. Experimental results demonstrate that our method outperforms baselines by approximately 10% in terms of accuracy and F1-score, highlighting the applicability and effectiveness of HFIN in enhancing security against sophisticated industrial environments and ever-evolving cyber threats.

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this article, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-orthogonal multiple access (NOMA) in both uplink and downlink relaying hops in an amplify-and-forward (AF) based UAV relaying network with multiple source-destination (SD) user pairs. Specifically, taking NOMA design in both hops into a joint consideration in relaying networks is investigated for the first time and presents a new challenge for the joint optimization problem in terms of the deployment of the UAV relay, the two-hop NOMA user grouping, and the transmit power control for both the source users and the UAV. To maximize the system sum rate, we propose an efficient joint uplink and downlink NOMA-based relay (JUDNR) scheme to decompose the problem into three sub-problems and adopt the alternating optimization (AO) method to iteratively obtain a promising solution. In detail, we provide a joint NOMA groups design of both the uplink and downlink and propose a novel recursive two-hop NOMA grouping (RTNG) algorithm to efficiently group users. Simulation results demonstrate that our proposed JUDNR scheme outperforms various baselines in terms of sum rate.

Unmanned aerial vehicle (UAV) relays are increasingly playing a significant role with their advantages of quick establishment for emergency communication, expansion of communication coverage, and increase of system capacity. As demand for massive simultaneous connections is ever-increasing, UAV relaying communication aided by non-orthogonal multiple access (NOMA) can more efficiently utilize spectrum resources to further improve capacity. To fully unlock the potential of NOMA in complex and practical relaying networks, we investigate a pioneering two-hope NOMA UAV relaying network with multi-pair separated users, where the uplink and downlink resources are jointly considered. For the purpose of enhancing the efficiency of our challenging UAV relaying, a developed joint uplink and downlink NOMA relay (JUDNR) scheme is proposed, which jointly optimizes UAV location, user grouping, and power control. Specifically, based on alternating-optimization (AO) method, we iteratively solve non-convex sub-problems by successive-convex-approximation (SCA) algorithm, a novel recursive reformulation grouping (RRG) algorithm based on fractional programming, and difference-of-convex (DC) algorithm, respectively. Simulation results demonstrate that our scheme can significantly enhance the system sum rate performance compared with reference strategies.

Vehicular fog computing (VFC) can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal distribution of computing resources, resulting that some regions have idle computing resources while others cannot satisfy the requirements of tasks. Therefore, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering that the existing centralized offloading mode is not scalable enough and the high complexity of cooperative task offloading, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the hierarchical architecture and the distributed algorithm improves the efficiency of global performance.

Flying Ad-hoc Networks (FANETs) consisting of multiple flexible unmanned aerial vehicles (UAVs) have gained ever-increasing attention due to their advantages in flexible deployment and enhanced data link connectivity. However, existing FANET schemes do not consider the mutual influence between data routing and UAV trajectory scheduling, resulting in limited network performance especially when the number of UAVs in FANET is large. This paper proposes a joint optimization framework for routing and UAV trajectory scheduling in FANET to enhance communication reliability and efficiency. Unlike previous studies that consider routing and trajectory scheduling separately, this framework formulates the routing problem as a Markov Decision Process (MDP) and employs trajectory schedule to guide the state transition probability, addressing the limitation of routing algorithms that passively perceive network topology. In addition, we exploit reinforcement learning to iteratively determine the optimal routing and trajectory scheduling strategy with the support of network topology prediction. Simulation results demonstrate that our proposed approach can significantly reduce latency, communication overhead, and packet loss in FANET.

Electric vehicles (EVs), one of the most effective solutions to reduce gas emission and realize fossil fuels replacement, are enjoying growing popularity from governments to customers. The development of EVs leads to significant advances in vehicle automation and electrification, but meanwhile poses additional heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing (VFC) and smart EV charging for joint optimization and propose an integrated charging and computing (IC2) architecture for EV-included smart grid. In the proposed IC2 architecture, charging stations are profit-driven third-party power prosumers that also help compute tasks offloaded by smart grid while EVs act as both energy consumers and computation providers. We employ the contract theory to provide a multiattribute contract-based charging protocol for EVs and charging stations in an information asymmetry scenario. To obtain the optimal contract, we derive KKT conditions and design a convex–concave-procedure-based contract optimization algorithm. We also design a heuristic offloading algorithm to assign heterogeneous tasks toward different EVs. Numerical results indicate that the proposed multiattribute contract-based charging-computing scheme can effectively benefit both the charging stations and EVs, and meanwhile improves the task computation capability in EV-integrated smart grid.

Vehicular fog computing (VFC) is regarded as a promising framework for vehicular computing applications by utilizing local spare resources of nearby vehicles to conduct ubiquitous time-critical and data-intensive tasks. Meanwhile, how to provide stable and low-latency services through real-time task offloading has become a heated issue. Opposite to the traditional task-to-individual offloading manner, in this article, we propose a novel task-block (TB)-based offloading paradigm for VFC, in which the tasks are merged into blocks to be assigned and offloaded. This TB-based offloading paradigm effectively alleviates the offloading decision-making burden and, thus, reduces the overall computation latency in the dynamic vehicular environment. Faced with transmission-reliable and time-intensive requirements of TBs, we turn to cooperation among vehicles and further propose a TB-based overlapping matching-coalition (TBOMC) scheme integrating overlapping coalition formation (OCF) game with matching theory to address the complicated offloading problem. The OCF game framework encourages vehicular fog nodes to devote their resources and form collaborative computing groups in a distributed method. Numerical results demonstrate that the TBOMC scheme better exploits local computing capabilities and outperforms from 5% to 12% over other existing benchmarks.

With increasing time-critical and computation-intensive tasks generated by mobile applications, vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on roadside units (RSUs) or cloud centers. In VFC, tasks are offloaded to vehicular fog nodes local to the client devices, which exploits the under-explored computational resources of nearby vehicles. In this paper, we propose a novel cooperative vehicular fog computing architecture from an overlapping perspective, termed Overlapping-enabled Cooperative Vehicular fog Computing (OCVC) to fully utilize vehicular fog nodes' local potential resources. Different from traditional cooperative VFC architecture where each vehicle only works in one fog computing group at one time, the proposed OCVC architecture enables vehicles to participate in different computing groups simultaneously, and thus is able to fully exploit potential computational resources in an overlapping manner. In addition, we provide a distributed OCVC scheme to solve the complicated computing group formation, overlapping resource allocation, and task assignment problem by employing the overlapping coalition formation (OCF) game framework and a heuristic offloading algorithm. We conduct simulations for performance comparison in terms of diversified performance metrics and numerical results show that the proposed OCVC scheme performs better than other benchmarks under different conditions.

Leaking oil from a stuffing box (packing) during the process of oil extraction may lead to serious economic as well as environmental problems. In recent years, a number of deep learning based methods have been proposed to build a real-time oil leakage detection system by analyzing data collected from different sensors. However, deep learning is a data hungry technology. To train a leakage detection model with satisfactory accuracy, numerous diversified training data are needed, which is hard to be collected by a single enterprise. Building collaboration among different enterprises by sharing sensor data for training can yield a better oil leakage detection model. But such collaboration is usually limited by concerns on sensitive information contained in sensor data being leaked. To handle these concerns and exploit benefits from collaboration training, in this paper, we leverage federated learning (FL), which is an emerging decentralized deep learning paradigm, to build an oil leakage detection model in a collaborative but privacy-protecting manner. In addition, considering data across enterprises may be labeled in different manners, we also carefully design an FL personalized strategy so that our collaboration paradigm can be conducted on data with label shift problems. We evaluate our methods on a real-world industry dataset and demonstrate that 1) Models yielded by our FL system can achieve very competitive results compared with models yielded by data-sharing collaboration; 2) Our proposed personalized strategy can properly handle the label shift problems under traditional FL setup.

With the ever-increasing traffic congestion issues and fast development of autonomous and connected vehicles, autonomous intersection management (AIM) has been recently proposed as a promising concept to effectively and safely enhance the traffic efficiency at intersections. However, most current literature focus on discrete space modeling, and there is few investigation on continuous space modeling in AIM due to high computational complexity, though continuous modeling can lead to better accuracy and performance. In this paper, we propose a novel multi-agent reinforcement learning-based AIM protocol with continuous intersection modeling and action space. To address the challenge of high data dimensionality and computational complexity in continuous modeling, we further provide an attention mechanism in our learning-based AIM protocol, termed as self-attention proximal policy optimization (SA-PPO) algorithm. The proposed SA-PPO algorithm can make a vehicle to control the speed more precisely and extract relevant information from neighboring vehicles to reduce data complexity. Results demonstrate that our proposed protocol can improve the traffic performance by up to 30% compared with commonly used DQN algorithm in complex and dense traffic environments.

Electric vehicles (EVs) are enjoying growing popularity from governments to customers. However, the development of EVs inevitably poses heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing and EV charging for joint optimization and propose an integrated charging-computing

Confronted with the increasing computation-intensive requirements of vehicular applications, vehicular fog computing (VFC) has emerged as the promising solution to mitigate the load at the edge of vehicular network. In VFC, vehicles are employed as vehicular fog nodes to provide reliable services with applicability. However, considering the individual serving and offloading intentions of the privately-owned vehicles, the many-to-many task offloading in dynamic vehicular environment becomes a challenging problem. In this paper, we propose a distributed dynamic many-to-many task offloading framework based on vehicle-to-vehicle (V2V) trading paradigm to improve the fog resource utilization in VFC. In order to reach an effective and stable offload-and-serve cooperation between vehicles as service demanders and vehicles as computation providers in the proposed framework, we formulate the trading process as a partially observable Markov decision processes (POMDP) and design a Multi-Agent Gated actor Attention Critic (MA-GAC) approach, leading to an efficient offloading optimization process in a distributed manner. Theoretical analysis and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the proposed MA-GAC approach outperforms other benchmarks in the dynamic environment.

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.

Unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian, with its unique advantages of flexibility, convenience, and wide coverage. As flying stations, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate a two-way multihop UAV relaying network, where there are two ground users as sources and multiple UAVs as relays to help the two ground sources exchange information. For the purpose of enhancing the efficiency of the investigated UAV-assisted relaying, we come up with a productive two-way multihop UAV relaying pattern, which can achieve a data rate of$({1}/{2})$data packets per time slot with the decode-and-forward protocol. Then, we further formulate a joint transmit power and trajectory optimization problem for the UAVs in this two-way multihop relaying scenario. The formulated problem is nonconvex which makes it difficult to solve directly; hence, we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Numerical results demonstrate that our proposed two-way multihop UAV relaying network achieves significant throughput gains compared with other benchmark schemes.

Based on the advantages of small size, light weight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling and the dynamic UAV trajectory to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Abstract The electromagnetic situation, which can promote the abilities of understanding and decision‐making for the battlefield, has attracted significant interest recently in information‐based warfare. This paper investigates the deep learning‐based electromagnetic situation analysis and judgment in a complicated battlefield environment. To comprehensively simulate the two‐sided battling process, a turn‐based confrontation strategy is proposed, and an electromagnetic situation analysis and judgment model are then designed based on the AlphaGo Zero algorithm to achieve efficient situation analysis and decision‐making. In addition, an electromagnetic situation‐based attack‐defense platform is developed to realize and evaluate this designed model. Simulation results demonstrate that this designed model achieves significant performance in electromagnetic situation analysis and judgment compared with the Monte Carlo Tree Search based baseline.

Abstract With the explosive growth of data communications, existing infrastructure networks are under ever‐increasing pressure. Due to the advantages of fully controllable mobility, rapid deployment, and low cost, the unmanned aerial vehicles (UAVs) have attracted much attentions from both industry and academia in recent years, and it has become an inevitable trend to employ UAVs to enhance the network performance in different environments. As an important paradigm of UAV‐assisted communications, UAV relaying communications has been regarded as a promising solution in enhancing connectivity and improving transmission rate. This paper for the first time comprehensively summarizes UAV relaying communications and its application scenarios, including single UAV relaying networks, multi‐user UAV relaying networks, multi‐hop UAV relaying networks, as well as Internet of UAVs, and deeply analyzes the key technologies and challenges to be solved under this topic. Furthermore, the state‐of‐the‐art researches and opportunities of UAV relaying communications are discussed in detail.

Based on the advantages of small size, lightweight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate full-duplex (FD) UAV relaying for multiple source-destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source-destination pairs, we propose a scheduling protocol that exploits time-division multiple access (TDMA) to serve different source-destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling, the dynamic UAV trajectory, and the UAV transmit power to maximize the system throughput. The formulated problem is nonconvex that makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed FD-based UAV relaying network achieves significant throughput gains compared with the half-duplex (HD) baseline, and the TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source-destination pairs.

Broadband connectivity and mobile technology have been widely applied in the world. With these advanced technologies, the proliferation of smart devices and their applications by accessing mobile internet have come up with a giant leap forward, leading to the ever‐increasing scale and complexity of cellular networks. This presents imminent challenges to anomaly detection in cellular networks. In this study, the authors discuss challenges and current literature of anomaly detection for cellular networks to embrace the ‘big data’ era. First, they review the state‐of‐the‐art techniques in the area of anomaly detection in cellular networks. Then, the challenges are pinpointed for anomaly detection due to the cellular network big data. Finally, they introduce a big data analytic‐based anomaly detection method for cellular networks.