Unmanned aerial vehicles (UAVs) play a crucial role in modern communication systems owing to their high mobility and broad coverage. However, due to the inherent open nature of the wireless channels, UAV-to-ground links are facing significant security threats from eavesdroppers and malicious jammers. To address these challenges, we propose a dynamic spectrum control (DSC) scheme integrating joint UAV trajectory and transmit power optimization to enhance UAV communication security in this paper. This scheme divides transmission channels from time and frequency dimensions and intelligently generates secure decision sequences using cryptographic principles based on real-time channel states, enabling transmissions for legitimate users without intra-cell interference. Based on a rapid-flooding time synchronization protocol, we analyze inter-cell collision probability (CP) and formulate an optimization problem for the secrecy rate. To further enhance security, we conduct a joint UAV trajectory and transmit power optimization. Through the successive convex approximation (SCA) method, we transform the non-convex optimization problem into a tractable convex form, obtaining a suboptimal solution. Simulations demonstrate that our proposed scheme significantly enhances security compared to conventional UAV communication methods.

Collaborative perception (CP) is a critical enabler for enhancing situational awareness, traffic safety, and mobility in connected autonomous vehicles (CAVs). By integrating sensory data from multiple CAVs, CP effectively mitigates perceptual blind spots, reduces the likelihood of traffic accidents, and alleviates congestion within complex environments. To advance CP capabilities within dynamic and resource-constrained network conditions, this paper proposes a proactive collaborative perception strategy that enables CAVs to selectively share perceptual data with other CAVs based on real-time network status and anticipated perceptual demands. Specifically, a collaborative framework, integrating communication and perception, is designed to enhance CP performance with data processing and fusion techniques. Within this framework, an innovative adaptive data compression algorithm is introduced, which dynamically adjusts the compression ratio based on the monitored real-time signal-to-noise ratio, therefore optimizing the data transmission efficiency. Additionally, a mathematical model is formulated to jointly optimize the communication and perception resources, and a multi-agent reinforcement learning algorithm based on Global State Proximal Policy Optimization (GSPPO) is developed to enhance communication resources distribution and CAV selection in complex and dynamic network environments. Experimental results demonstrate that the proposed proactive CP strategy can effectively reducing communication latency without compromising perception accuracy.

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6 G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the “value of information” as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6 G networks.

Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation mobile wireless communication systems. In this paper, we propose a novel framework for full-duplex ISAC systems that jointly incorporates dynamic tracking and robust secure beamforming. Specifically, a dual-functional radar-communication base station employs an extended Kalman filter to dynamically estimate the trajectories of mobile downlink (DL) users. To mitigate the impact of imperfect channel state information from multiple eavesdroppers, a robust beamforming strategy is devised by quantifying angular uncertainty via the Cramér–Rao bound (CRB). A total transmit power minimization problem is formulated under secrecy rate constraints for both DL and uplink (UL) communications, while simultaneously ensuring sensing accuracy through CRB and beam tracking mean squared error metrics. The optimization jointly considers the beamforming matrices, artificial noise covariance matrix, and UL power allocation strategy. To address the formulated non-convex problem, the S-procedure is employed to transform semi-infinite constraints into linear matrix inequalities. Successive convex approximation technique is then iteratively applied to obtain high-quality solutions. Extensive simulations verify the effectiveness of the proposed framework, demonstrating superiority in secrecy rate and power efficiency compared to benchmark schemes. Moreover, the results highlight the inherent trade-offs between secure communications performance and sensing accuracy, thereby offering insights into the design of future full-duplex ISAC systems.

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

Vehicular digital twins (DTs) create virtual representations of physical vehicles, enabling real-time data exchange to enhance intelligence and ensure safe driving. Reducing DT content delivery latency in infrastructure-deficient, sparsely populated areas is crucial. This paper develops a novel Satellite-UAV multi-path content delivery framework for data synchronization in vehicular DT applications. Satellites offer wide coverage but suffer from high latency, while UAVs provide rapid deployment and low-latency communication. The framework leverages these unique characteristics to facilitate simultaneous content downloading through multiple paths, thereby reducing latency. A Stackelberg game model is used to motivate effective resource allocation by UAVs. Given the typically private utility model of DTs, a learning-based algorithm is developed to determine optimal pricing strategies for UAVs. Simulation results demonstrate significant enhancements in UAV utility and reduced DT costs, meeting diverse service requirements.

Robot assisted endoluminal intervention is an emerging tool for treating luminal lesions. Vision-based endoluminal navigation, particularly through video-CT registration, is a tangible way of obtaining absolute camera position information. By using pre-operative CT data, accurate endoscope localization can be achieved, without the need of additional tracking hardware intraoperatively. However, aligning preoperative CT with intraoperative domain remains a challenge. Although approaches such as style transfer have been explored, patient-specific textures and intra-operative artifacts can significantly complicate the task. To overcome these challenges, we propose R2Nav, a robust, real-time test time adaptation method for endoluminal navigation. R2Nav constructs a confidence buffer during the testing phase, refining the model only for frames with high uncertainty. We introduce a registration-augmented model refinement strategy, which enhances both accuracy and efficiency of the system by selecting relevant training samples from the virtual gallery. Additionally, we propose a novel warm-up strategy for the registration encoder during the initial testing phase, enabling the extraction of more robust features when the model is suboptimal. Extensive validation demonstrates that R2Nav outperforms the current state-of-the-art methods, offering significant advantages for real-time, intra-operative endoluminal navigation. Code is at: https://github.com/EndoluminalSurgicalVision-IMR/R2Nav.

In this paper, we investigate the trade-off issue between energy efficiency and communication quality in the unmanned aerial vehicle (UAV) enabled cargo delivery system. For a cellular-connected cargo UAV delivering parcels from the warehouse to each user’s location, minimizing both the energy consumption and expected outage time is essential. However, a trade-off exists between these two factors, optimizing one aspect is bound to diminished performance in the other. To jointly reduce the UAV’s energy consumption and expected outage time, we formulate an optimization problem with the objective function to minimize the weighted sum of UAV’s energy consumption and expected outage time. With the aid of radio map, a hybrid deep reinforcement learning (HDRL) algorithm, consisting of an improved ant colony optimization algorithm and the dueling double deep Q network algorithm, is proposed to solve the formulated problem. The delivery sequence and the flight trajectory of the UAV are then jointly optimized by solving the problem with the HDRL algorithm. Numerical results demonstrate that the proposed algorithm effectively reduces both energy consumption and outage time, while achieving a performance improvement of approximately 6% to 50% compared to the comparisons. Moreover, the communication quality of the UAV improves with an increased weight factor, yet gives rise to a higher energy consumption.

The proliferation of maritime activities has spurred the emergence of numerous computation-intensive and delay-sensitive marine applications and services. Given the inherent rationality, selfish nature, and limited computational abilities of marine devices, devising effective strategies to incentivize their participation in task processing become a critical challenge. In this article, we investigate the satellite-assisted marine multiaccess edge computing (MEC) and propose a two-tier task offloading scheme through a hybrid Stackelberg-Bargaining game approach to enhance offloading efficiency and maximize the utility of marine devices. Specifically, for the underwater acoustic communication, we consider the scenario where multiple autonomous underwater vehicles (AUVs), managed by maritime autonomous surface ships (MASSs), upload their collected data using nonorthogonal multiple access (NOMA) to optimize channel utilization. For the data transmission above the sea surface, we consider the scenario where a low-Earth orbit satellite (LEOS) functions as a space edge server to provide computing services, and MASS offloads workloads to LEOS through frequency division multiple access (FDMA) to prevent co-channel interference. we define the utility of AUVs, MASSs and LEOSs, and model the offloading process between AUVs and MASSs as a Stackelberg game, while representing the offloading interaction between MASSs and LEOSs as a Bargaining game. Additionally, we propose efficient algorithms to optimize AUV offloading strategies and MASS pricing strategies, while refining the bidding strategies for both MASSs and LEOSs. Simulation results demonstrate that the proposed algorithms significantly outperform benchmark schemes in achieving optimal solutions.

The widely deployment of infrastructure and wireless interfaces increases industrial IoT (IIoT) vulnerability to network intrusions, highlighting the requirements for robust network intrusion detection systems (NIDSs). Although deep learning (DL) provides a promising solution for NIDSs, it remains susceptible to adversarial attacks as minor input perturbations can lead to major misclassifications. In this paper, we propose a packet-level adversarial traffic generation (PATG) approach for attacking NIDSs in IIoT, which not only aligns with domain constraints but also evades various DL-based NIDSs. Particularly, we introduce a reversible abstract traffic representation to ensure that the original traffic can be effectively modified while preserving its functionality. We propose a packet-level generative adversarial networks to craft adversarial traffic by learning benign data distribution in feature space and simulating evasion behaviors, which escapes the DL-based NIDSs. We further design two defense schemes to enhance system resilience against proposed adversarial attacks. We evaluate PATG on nine state-of-the-art DL-based NIDSs in the Kitsune and CICIoT23 datasets. Experimental results demonstrate that PATG can achieve a maximum evasion increase rate of 99% with cost-effective execution, while the defense methods significantly mitigate the impact of the adversarial attacks.

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.

This paper explores the fairness issues on physical layer security (PLS) in intelligent reflecting surface (IRS) assisted multiple-user uplink systems. Due to unknown instantaneous eavesdropper channel state information (CSI), it is not possible to acquire exact secrecy rate of a PLS system. In this paper, we introduce secrecy outage probability (SOP) as a security metric, instead of secrecy rate as used in most existing works, and formulate a minimization problem of maximum (min-max) SOP among multiple users. To solve this problem, we propose two independent approaches: one is alternating optimization (AO) and the other is a deep learning based (DL) scheme. The AO scheme decouples the problem into two sub-problems to alternately optimize phase shift matrix and receiver beamforming vectors, which gives a near-optimal performance but with a high complexity. The DL scheme, on the other hand, works based on neural networks through offline training, which is used for online generation of phase shift matrix and receiver beamforming vectors with a lower complexity. As traditional self-supervised neural networks cannot achieve a good solution to the max-min problem, we design a multiple-stage booster (MSB) framework to solve this problem. Simulations demonstrated that SOP is improved significantly with the proposed schemes compared to benchmark schemes. In particular, the AO scheme outperforms the DL-based approach slightly at the cost of a relatively high computational complexity.

In this paper, we study intelligent multi-domain collaborative computing offloading within the space-air-ground integrated Internet of Things (SAG-IoT). While non-terrestrial transmission alleviates the burden on scarce terrestrial resources, it introduces significant propagation delay, rendering it unsuitable for all tasks. To address this issue, we categorize tasks into priority and general groups and design a dynamic priority resource management (DPRM) framework. This framework strategically pre-allocates resources to priority tasks, ensuring their completion on edge nodes. Within this framework, we formulate an optimization problem focused on offloading path selection and multi-dimensional resource management, to maximize the completion rates of general tasks while meeting the quality of service requirements for priority tasks. We introduce a hierarchical hybrid policy optimization based on DPRM (HHPO-DPRM) algorithm to tackle the aforementioned problem in highly dynamic network environments. Comparative analysis with two traditional algorithms underscores the effectiveness of our approach.

This paper delves into the realm of intelligent and cooperative computing offloading within satellite-assisted Inter-net of Vehicles (Sat-IoVs). More specifically, it focuses on enabling efficient computing offloading for highly mobile vehicle users by formulating and executing an offloading path selection and multidimensional resource management (OPS-MDRM) optimization problem at a central controller. Given the complex amalgamation of continuous and discrete action spaces, along with various timescales inherent to the OPS-MDRM problem, we introduce a two-timescale framework. In this framework, we present a hierarchical hybrid policy optimization (HHPO) based on-policy algorithm to effectively tackle the aforementioned problem. Our comparative analysis against three traditional resource allocation methods underscores the outstanding performance achieved by the HHPO-based approach in the Sat-IoV networks.

The advent of deep learning (DL) technology has increasingly captivated the research community’s interest in harnessing DL to enhance data transmission efficiency. Notwithstanding, prevalent methodologies often overlook the computation delay of DL processing data during the inferencing procedure, and fail to adjust intelligent algorithm complexity based on user features. To bridge this gap, we introduce $\mathbf{C o m}^{2}$ (Communication-Computation) framework, to synergize the optimization of communication and computational delays. $\mathrm{Com}^{2}$ adeptly navigates the trade-offs between communication and computation delays, facilitated by autoencoders of varying depths, thus one user can reduce communication delay through more computation latency, and vice versa. Further enhancing this framework, we present an optimization algorithm that marries QMIX with a cascaded graph neural network (GNN), designed to select the optimal autoencoder depth and optimize transmission resources in a distributed manner. This algorithm pioneers a label-free training regime, employing reinforcement learning and unsupervised learning to adaptively improve without the need for high-quality labels. Simulation results show that $\mathrm{Com}^{2}$, alongside the proposed optimization algorithm, maximizes the utility of users’ computing and transmission resources, significantly curtailing the overall data transmission delay by intelligently managing delay trade-offs.

This article investigates the issue of cooperative real-time trajectory design for multiple unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas. To restore communication links rapidly between mobile users (MUs) and the ground base stations, UAVs equipped with both radio frequency (RF) modules and free space optics (FSO) modules are utilized as relay nodes. Given the challenges of setting up a central controller for the UAVs and the urgency of emergency communication, the trajectory design problem for these UAVs is formulated as a distributed cooperative optimization problem. Based on the enhanced${K}$-mean algorithm and multiagent PPO (MAPPO) algorithm, a cooperative trajectory design method, abbreviated as KMAPPO, is proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. Compared to the state-of-the-art deep reinforcement learning (DRL) methods, simulations reveal KMAPPO’s superior performance. It converges 32% faster, boosts RF allocation efficiency, and augments FSO communication backhaul capacity.

This paper investigates the power efficiency of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted multi-user uplink channels. Existing research works usually focus on enhancing secrecy performance, and neglect measures to improve power efficiency. In this paper, the optimization problem is formulated to minimize the sum radio frequency (RF) power of multiple users in the uplink channel subject to secrecy outage probability constraint. This problem is solved by an alternating optimization (AO) algorithm that includes three optimization sub-problems, i.e., phase shift matrix, receiving matrix, and RF power optimization. Furthermore, to reduce the complexity of the proposed AO algorithm, a deep learning (DL)-based approach is proposed to optimize the sophisticated phase shift matrix optimization process. Simulation results demonstrate that the proposed scheme can significantly reduce the average RF power, and the DL-based scheme achieves similar performance as AO algorithm while reducing the time complexity significantly.

In recent years, 5G-V2X has promoted the advancement of autonomous vehicles, enabling the latter to obtain more information via 5G networks. However, fast-moving vehicles have to perform frequent handover authentication with base stations in vulnerable wireless channels, which can cause access failures and affect smooth driving. The digital twin is the virtual agent in cyberspace to reliably provide real-time decisions and added-value services to improve the quality of communication for vehicles by analyzing raw data and interacting with the 5G core network. Based on the capabilities of digital twin, in this paper, we propose digital twin-assisted handover authentication scheme that uses the digital twin as the bridge to exchange necessary parameters in 5G-V2X, thereby intelligently assisting in completing mutual authentication and key negotiation between the vehicle and the target base station in advance and reducing the complexity of the handover process. Furthermore, the security and performance analysis demonstrates that our proposed scheme is secure and efficient.

As an integral component of space-air-ground integrated networks (SAGINs), the low Earth orbit (LEO) satellite networks have displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites poses unprecedented challenges in network management, multi-dimensional resource scheduling, and service delivery. In this paper, we study the service function chain (SFC) orchestration in dynamic LEO satellite networks, with the aim of achieving flexible and efficient service provision. Considering the service requirements and the load fairness of LEO satellite networks, we formulate the SFC deployment problem as an integer nonlinear programming (INLP) problem. We then introduce a load-aware SFC orchestration algorithm to improve serving capacity and load fairness. Additionally, we address the issue of SFC migration in dynamic LEO satellite networks to ensure service continuity. To minimize the service interruption and network resource wastes, a Tabu search (TS)-based approach is presented to optimize the virtual network function (VNF) migration. Simulation results demonstrate that our proposed approaches outperform the benchmark by a substantial margin in terms of load fairness, without compromising service acceptance.

In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state–action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor–critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially, in scenarios with scarce spectrum resources and heavy traffic loads.

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.

In this paper, we investigate the joint cache placement and content delivery in satellite-terrestrial integrated cloud radio access networks. We consider the scenario where cache-enabled access points (APs), including multiple base stations (BSs) and one low orbit earth satellite, are connected to a central processor via backhaul links and cooperatively serve users via joint beamforming. To minimize the long-term power consumption, we formulate an optimization problem to jointly optimize the cache placement, AP clustering, and multicast beamforming, while satisfying the constraints on the transmission power, quality-of-service, and caching storage. Since the formulated problem has mixed timescales, it is decoupled into two sub-problems. For the short-term delivery sub-problem, we first reformulate it as an equivalent sparse multicast beamforming problem and approximate the non-convex objective function by a concave smooth function, and then solve it with a convex-concave procedure approach. For the long-term cache placement sub-problem, we propose an alternating based method to tackle it iteratively. Simulation results validate the advantages of our proposed method and show the impacts of different cache capacities and file numbers on the long-term power consumption.

In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous quality-of-service (QoS) requirements, and then solve it with a multi-agent deep deterministic policy gradient (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one. Moreover, the proposed MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the SADDPG-based and random schemes.

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “Reinforcement on Federated” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet of things (IIoT) networks. To efficiently orchestrate the limited spectrum and computing resources in IIoT networks for massive DNN inference tasks, a resource management problem is formulated with the objective of maximizing the average inference accuracy while satisfying the quality-of-service of DNN inference tasks. Considering the strict delay requirements of inference tasks, we transform the formulated problem into a Markov decision process, and propose a deep deterministic policy gradient based learning algorithm to obtain the solution rapidly. Simulation results show that the proposed algorithm can achieve high average inference accuracy.

In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement learning and then design a deep deterministic policy gradient (DDPG)-based solution. Through training the DDPG-based resource management model offline, optimal vehicle association and resource allocation decisions can be obtained rapidly. Simulation results demonstrate that the DDPG-based resource management scheme can converge within 200 episodes and achieve higher delay/quality-of-service satisfaction ratios than the random scheme.

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

Platooning has been identified as a promising vehicular traffic management strategy to improve road capacity, energy efficiency, and on-road safety in intelligent transportation systems (ITS). Inter-vehicle communications within a platoon and among multiple platoons can assist platoon control by maintaining a constant inter-vehicle distance, which in turn enhances road safety. An efficient method of sharing inter-vehicle information successfully and timely is critical to many platooning applications. In this paper, a resource allocation (RA) approach is proposed to support inter-vehicle communications underlaying cellular network for a multiplatooning (a chain of platoons) scenario. By applying the evolved multimedia broadcast multicast services (eMBMS) in the Evolved Node B (eNB), the transmission delay for intra-platoon and inter-platoon communications can be reduced. Then, using the proposed subchannel allocation and power control schemes, the number of required subchannels and the transmission powers of each vehicle and the eNB can be minimized. Numerical results show that the proposed approach outperforms the candidate RA scheme in terms of transmission delay, especially in a multiplatooning scenario with a large number of vehicles.

Due to the limited battery power of sensor nodes and harsh deployment environment, it is of fundamental importance and a great challenge to achieve high energy efficiency and strong robustness in large-scale wireless sensor networks (LS-WSNs). To this end, we propose two self-organizing schemes for LS-WSNs. The first scheme is the energy-aware common neighbor scheme, which considers the neighborhood overlap in link establishment. The second scheme is energy-aware low potential-degree common neighbor (ELDCN) scheme, which considers both neighborhood overlap in topology formation and the potential degrees of common neighbors. Both schemes generate clustering-based and scale-free-inspired LS-WSNs, which are energy-efficient and robust. However, the ELDCN scheme shows higher energy efficiency and stronger robustness to node failures, because it avoids establishing links to hub-nodes with high potential connectivity. Analytical and simulation results demonstrate that our proposed schemes outperform the existing scale-free evolution models in terms of energy efficiency and robustness.

Enabling vehicular communications is expected to revolutionize the transport infrastructure and support many traffic management applications such as platooning. Sharing vehicle information such as speed and acceleration wirelessly among platoons plays an effective role in platoon control by maintaining a constant inter-vehicle and inter-platoon distances. However, the performance of (inter and intra-) platoon communications in terms of throughput, transmission delays and packet transmission collisions can undermine the effectiveness of information sharing on platoon control. In this paper, we present probabilistic performance analysis of IEEE 802.11p Distributed Coordination Function (DCF) for inter-platoon communications in a multiplatooning scenario (i.e, a chain of platoons). The expressions for the transmission attempt probability, packet collision probability, network throughput and packet delay are derived accordingly. Numerical results show that the performance of inter-platoon communications depends on the vehicle's role in one platoon and its platoon position within the multiplatoon and that the end-to- end delay of platoons can be reduced by adjusting the contention window size.

In this paper, we present three network evolution models for generating fault-tolerant and energy- efficient large-scale peer-to-peer wireless sensor networks (WSNs) based on complex networks theory. Being scale-free is one of the intrinsic features of complex networks-based evolution models that generates fault- tolerant topologies. In this work, we argue that fault- tolerant topologies are not necessarily energy efficient. The three proposed energy-aware evolution models are energy-aware common neighbors (ECN), energy- aware large degree promoted (ELDP) and energy-aware large degree demoted (ELDD). ECN considers neighborhood overlap, whereas ELDP and ELDD consider topological overlap for node attachment. The ELDP model promotes the establishment of links to nodes with a large degree, whereas the ELDD model demotes this strategy. Performance evaluations demonstrate that the proposed models outperform a candidate clustering-based model, thereby providing greater energy savings and fault- tolerance. Among the proposed models, ECN is the winner in-terms of energy efficiency, ELDD performs best in- terms of fault-tolerance, and ELDP conveniently provides balance between the two.

This paper presented the new method to tune the rule base in fuzzy control system. The consequent part fuzzy values are the important control values, that effect the performance of fuzzy system output. We proposed the real-time method to tune the consequent part fuzzy values using fuzzy arithmetic operations, according to the change status of system error, hence the rule base is regulated. The simulation result shows the method is valuable and easy to implement.