Leveraging their mobility and feasibility, Unmanned Aerial Vehicles (UAVs) present a promising solution for assisting covert communications to mitigate the risk of eavesdropping. However, existing studies mainly rely on passive optimization, where the UAV adjusts its transmit parameters according to the channel state, without actively balancing covertness constraints and average system throughput. To solve the above challenge, we propose for the first time a UAV relay-assisted covert communication framework with a buffer. Specifically, we derive the optimal detection threshold for the eavesdropper with mobility and uncertain locations, and obtain a closed-form solution for the lowest detection error probability. To solve the formulated average system throughput maximization problem, we transform the covertness constraint into a tractable analytical form, and obtain the optimal transmit power for both the UAV relay and the friendly UAV jammer. Then, through a rigorous theoretical analysis of upper and lower bounds on average system throughput, we prove the existence of optimal UAV trajectories. Finally, optimal transmission and reception decisions of the UAV relay are derived under covertness and buffer size constraints. Numerical results and theoretical analysis demonstrate the effectiveness of the proposed scheme in terms of average system throughput and covert performance.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through fullspace signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

Due to its capability to ensure communication security and improve spectral efficiency, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS)-assisted covert communications in Rate-Splitting Multiple Access (RSMA) networks have drawn widespread attention. However, existing studies often overlook the impact of the mobility of wardens and users on long-term covert performance, and lack joint optimization of covert rate and energy consumption. Meanwhile, introducing STAR-RIS significantly increases system parameter dimensionality, rendering traditional methods inefficient. Although reinforcement learning offers advantages, it still faces challenges in training efficiency and resource overhead within high-dimensional state-action spaces. Therefore, this paper proposes a quantum reinforcement learning-based algorithm, named QUERC, for STAR-RIS-assisted covert communications in RSMA networks. Specifically, we first formulate a long-term covert energy efficiency maximization problem under a dynamic environment with mobile users and wardens. Then, we propose a novel hybrid quantum neural network architecture to solve this problem. This architecture integrates a fully connected layer, a variational quantum circuit, an action post-processing mechanism, and a regularized objective, enhancing policy stability and generalization in high-dimensional action spaces. Finally, extensive experiments demonstrate that, compared with TPG, QPG, RIS-NOMA, RPS, and greedy approaches, QUERC algorithm achieves superior average covert energy efficiency and offers significant advantages in computational and inference performance.

Low Earth Orbit (LEO) satellite networks are expected to become a key component of Sixth Generation (6G) communication networks, to relieve the communication burden on ground networks. Driven by the rapid advancement of communication technologies and intelligent applications, dense traffic flow in the Internet of Vehicles (IoV) inevitably leads to a surge in task generation and an increased demand for network resources. The requirement for low latency further intensifies this challenge, making it difficult to rely solely on ground network resources to process tasks efficiently and promptly. Conversely, relying only on satellite networks for task processing results in high costs. Therefore, flexibly integrating LEO satellite links based on real-time traffic conditions, task demands, and the real-time state of ground network resources becomes an effective solution. However, achieving such a goal poses significant challenges in efficient allocation and balance between ground and LEO satellite network resources. Therefore, we propose a dynamic multi-task partial offloading algorithm based on LEO satellite-ground network collaboration to efficiently allocate resources between ground and satellite networks in real time. We first introduce the utility gain as a metric to evaluate task scheduling preference and design an improved iterative algorithm to jointly optimize the offloading ratio and channel allocation to maximize system utility. Finally, based on the real-world dataset of Shanghai (China), we demonstrate the significant advantages of the proposed strategy over representative methods in terms of delay, vehicle satisfaction, and system utility.

The integration of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs) is promising for providing flexible and intelligent communications to users in urban areas. Existing studies are founded either on the complete Line of Sight (LoS) or complete Non-LoS (NLoS) communication scenarios, while ignoring their coexistence. To solve the above challenge in complicated and dynamic communication scenarios, we formulate an average system sum rate maximization problem with the optimization of joint IRS-user association, multi-UAV trajectory optimization, IRS phase shifts and transmit power allocation. Since the highly complex and coupled variables, we propose a Multi-Agent Deep Reinforcement Learning (MADRL)-based scheme to maximize the average system sum rate. First, we derive two composite channel power gains for different communication conditions. Then, phase alignment theory is utilized to obtain optimal phase control. To guarantee long-term optimization, we propose a scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and Successive Convex Approximation (SCA) method to jointly optimize multi-UAV trajectories, multi-IRS association and transmit power allocation. Finally, experimental results reveal that the proposed MGBA shows considerable advantages in both the convergence speed and the average system sum rate.

Integrated Sensing and Communication (ISAC) can assist Uncrewed Aerial Vehicle (UAV) secure communications by acquiring information about eavesdroppers. However, existing studies have not systematically investigated ISAC beamforming for simultaneously sensing the channel information of ground eavesdroppers, jamming eavesdropping links, and communicating with users, which poses significant challenges in ensuring both sensing accuracy and communication confidentiality. To address this issue, we propose a UAV-ISAC secure communication algorithm to maximize average secrecy rate by jointly optimizing communication and sensing beamforming, user scheduling, sensing time allocation, and UAV trajectory. We address the formulated NP-hard problem by decomposed it into three subproblems. We first relax binary user scheduling and sensing time allocation by a penalty-based successive convex approximation approach. The UAV trajectory is then iteratively optimized while beamforming is designed using semidefinite relaxation, with matrix lifting applied to handle the rank-one constraint. A triple-layer iterative algorithm is constructed by integrating these steps to achieve a suboptimal solution. Numerical experiment results and theoretical analysis validate the superiority of the proposed algorithm in terms of average secrecy rate, convergence and computational complexity.

The flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

Over-the-Air Federated Edge Learning (OTA-FEEL) has emerged as a promising paradigm for collaborative AI model training across heterogeneous edge devices. Despite its advantages in communication efficiency and privacy preservation, OTA-FEEL faces critical challenges, including channel fading, energy constraints of edge devices, and non-i.i.d data distributions. This paper is the first to investigate a joint impact of local data distribution heterogeneity and transmission distortion on model convergence of OTA-FEEL. Accordingly, we analyze the gap between global expected and optimal losses, and formulate the gap minimization problem under long-term energy consumption constraints. To solve this problem, we propose an energy-aware alternating resource allocation algorithm based on Lyapunov optimization framework, jointly addressing transmit power control and device sampling rate selection. Specifically, we transform the non-convex problem based on inverse convex optimization. Then, we employ first-order Taylor expansion to linearize the non-convex constraint, and also develop an iterative framework based on block coordinate descent and successive convex approximation to enable rapid convergence. Extensive simulations under three types of non-i.i.d data distributions validate the effectiveness of the proposed EARA algorithm, which consistently outperforms representative algorithms by achieving test accuracy approaching the theoretical upper bound, while maintaining significantly low energy consumption.

Due to the ability to improve data transmission efficiency and extend coverage, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and autonomous aerial vehicle (AAV)-assisted Internet of unmanned agents (IUAs) has become an attractive paradigm. However, in multi-AAV and multi-STAR-RIS coexistence scenarios, device data transmission and energy harvesting (EH) processes are highly coupled with phase and amplitude optimization of STAR-RISs, which involve a large number of coupled variables that need to be carefully decoupled and processed. Therefore, to address the above challenges, we propose a distributed scheduling algorithm, MINT, for multi-STAR-RIS and multi-AAV assisted IUA, by jointly scheduling AAV trajectories, AAV association variable, charging time allocation, and STAR-RIS coefficient matrices to maximize system energy-efficiency. First, by considering device energy constraint, AAV energy consumption, and time constraints, we formulate the energy-efficiency maximization problem and model it as a Markov decision process. Second, we design a multiagent deep reinforcement learning-based MINT algorithm to solve the formulated optimization problem. Finally, experimental results demonstrate that MINT algorithm outperforms the existing algorithms regarding energy-efficiency, the number of uploaded bits, and convergence performance.

The Industrial Internet of Things (IIoT) faces serious data privacy issues, such as the risk of data leakage during aggregation and transmission. However, existing studies rarely consider data privacy protection from the perspective of 5G core networks (CNs). This article proposes a federation chain-based data privacy protection system for the control plane in 5G CN to facilitate secure and decentralized communications between IIoT devices and other networks components, enhancing data integrity and confidentiality. Using XPRO instrument, 5G CN signaling storm simulation test platform, Free5GC, UERANSIM simulator, and Kali platform, the system simulates and generates realistic control plane data streams in IIoTs. To prevent unauthorized data access, we design an authentication algorithm based on the Bulletproofs zero-knowledge proof technique. Additionally, we implement a data encryption and decryption algorithm based on the Paillier partially homomorphic encryption for user privacy. We design a foundation model-based anomaly flow detection and analysis module to improve the security of the system for anomalous signaling flows. The feasibility and effectiveness of the system are validated on a 5G CN simulation testing platform, and experimental results show that the proposed approach ensures robust and scalable IIoT data privacy protection.

As distributed edge intelligence (DEI) advances within the Internet of Electric Vehicles (IoEV), the deployment of mobile charging stations (MCSs) offers a solution to the uneven distribution of fixed charging stations (FCSs), enhancing energy access in remote areas. However, MCS faces the problem of passive scheduling, limiting effective resource utilization and prolonging charging waiting time. This article proposes a hybrid charging model algorithm (HCMA) to address the above challenge, particularly in regions with limited available FCS. We first formulate a multiobjective optimization problem to optimize electric vehicle (EV) charging modes, volumes, and MCS scheduling arrangements. Then, we decompose the original problem into two subproblems. By determining EV charging locations and EV charging mode, the two subproblems are solved, respectively. Finally, simulations based on real-world data demonstrate that HCMA performs better compared to several representative methods, including random working, ARMM, and RBA, in terms of average charging waiting time, extra traveling distance, average unit price of energy, and number of MCS schedules.

With the explosive growth in Industrial Internet of Things (IIoT) devices, the volume of multimedia data in the field of flexible manufacturing has also increased significantly in recent years, especially the vast amount of unlabelled image data. Image annotation provides machines with a more natural way to interact with users, enhancing the level of intelligence in IIoT flexible manufacturing. This article proposes a multifeature fusion multikernel learning image annotation method to tackle imbalanced label distribution, image weak labeling, and varying representational abilities of features. Initially, oversampling techniques with synthetic minority class samples address the influence of minority classes, while a label enhancer extends label vectors to overcome the influence of weak labeling. Subsequently, the integration of traditional visual features with deep features based on multikernel learning is investigated to enhance feature representation capability. This approach combines complementary information from multiple features, establishing intrinsic connections between images and annotated keywords. Experimental evaluations are conducted on three benchmark datasets, comparing our method with several classical methods. Evaluation results demonstrate that our proposed method captures semantic information more accurately and comprehensively. By effectively accomplishing automatic image annotation, our method can enhance human-machine-interaction to improve the level of intelligence in IIoT flexible manufacturing.

To address the problem of real-time path planning for autonomous aerial vehicle (AAV) formations in complex environments, this article proposes a hybrid path planning control system tailored for AAV-enabled Internet of Things (IoT) utilizing near-field communication (NFC) among them. We model the AAV formation problem as an undirected graph, and first design a virtual leader-follower method based on the consistency principle of distributed systems. After that, we present an innovative hybrid path planning method (named ASAP-A*), combining A* and artificial potential field methods. It effectively reduces redundant points in the path and optimizes the AAV trajectory by B-spline smoothing to meet AAV trajectory requirements. Finally, we construct a user-friendly AAV swarms formation control system based the proposed methods, tackling the complexity of operating existing systems, and verifying the effectiveness of our solutions.

In response to the critical need for rapid post-disaster assessments, this article introduces an innovative application of artificial intelligence (AI) in unmanned aerial vehicles (UAVs) for disaster relief. A lightweight distributed learning algorithm (namely, YO-FR), is designed to enable multiple UAV agents to share and process environmental data, highlighting the importance of data and knowledge-empowered distributed learning. Moreover, we create a real-world mini-data set collected by UAVs for post-disaster road defects (mini-UPRDs), followed by a data enhancement technology to facilitate feature extraction and promote knowledge-driven learning. The viability of YO-FR is underscored by its enhanced detection precision and processing speed, as evidenced by its performance on the enhanced mini-UPRD data set, surpassing that of existing algorithms. By implementing AI algorithms on UAV platforms, this research offers a theoretical and practical foundation for the practical deployment of IUA in critical application areas, such as emergency management and disaster response.

Unmanned aerial vehicle (UAV) swarm shows great potential as an effective tool for target tracking through completing complex tasks by collaboration of heterogeneous UAVs. However, UAV swarm scheduling faces challenges with poor quality communication and obstacles, especially in communication denial environment with multiple obstacles. To overcome these challenges, first, this paper proposes a scheduling slot model which divides the scheduling process into multiple time slots, allowing UAVs to communicate in communication slots while predicting instead of communication in communication denial slots. In communication denial slots, this model utilizes route fitting and two-stage Kalman filtering for UAV location prediction and optimizes UAV scheduling to align with predicted positions. In enabled slots, this model corrects position deviations to obtain precise UAV locations manually. Then, we propose an obstacle avoidance strategy to facilitate swarm scheduling for target searching under communication constraints. The obstacle avoidance strategy simplifies obstacles as regular hexagons and facilitates the determination of UAV avoidance routes by introducing intermediary points. Finally, to optimize UAV scheduling strategy, we propose a region co-evolution algorithm (RCEA), which emphasizes the collaboration among diverse individuals or populations. RCEA adopts area evaluation and Pareto strategy to enhance scheduling efficiency with following three steps. RCEA divides the overall scheduling region into multiple sub-regions, generates the foundational solution pool through the implementation of the area evaluation or Pareto strategy, and then proceeds to execute the region cooperation process base on the foundational solution pool. Simulation experiments are conducted to validate the performance of human-UAV interaction scheduling model with proposed scheduling methods and obstacle avoidance strategy. The simulation results demonstrate that RCEA outperforms other scheduling algorithms for UAV swarm in communication denial environment with multiple obstacles. Note to Practitioners—This paper addresses challenges inherent in real-world application scenarios, and the proposed algorithm has the potential to bring many benefits to practitioners. Firstly, the scheduling slot model can be applied not only to UAV swarm for target searching but can also be extended to other swarm devices for complex tasks with collaboration relying on communication support while facing poor quality communication or obstacles. Secondly, the proposed RCEA focuses on collaboration and region partitioning, the algorithm demonstrates remarkable scalability, effectively tackling challenges across diverse scales and complexities. Thirdly, the experimental scenarios can serve as a validation dataset for other peer researchers, and although the simulation experiment is based on a 2D movement model, this study still offers theoretical support applicable to a 3D movement model.

In the academic research field, identifying suitable collaboration partners and selecting appropriate journals for publication remain significant challenges for researchers. Existing academic recommendation systems often fail to provide personalized, accurate, and efficient recommendations. To address these issues, this article proposes an innovative academic recommendation system that incorporates multisimilarity features. By constructing an academic collaboration network and optimizing the transfer probability matrix to reflect scholars’ relationships, the system captures scholars’ collaborative tendencies and potential connections. A key innovation of this work is the proposed Muls-IRWR algorithm, which improves traditional random walk with restart (RWR) by integrating various similarity measures. Using a subset of the DBLP citation data, we develop our academic collaboration network to calculate precise scholar similarities. Experimental results demonstrate that our system significantly outperforms existing models in terms of recommendation accuracy and efficiency, highlighting its practical value and potential for use in real-world academic applications.

The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.

Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.

The integration of Unmanned Aerial Vehicles (UAVs) and Wireless Power Transfer (WPT) is promising to provide charging and communication services to on-ground devices. However, traditional studies of UAVs at a fixed height to provide services to devices, ignoring the flexible characteristic of UAVs. To fully exploit the potential of UAVs in wearable networks, we optimize the height of UAVs to maximize the network throughput and minimize the task completion time. Considering the UAV coverage and energy constraints, we formulate the service time allocation and UAV height control optimization problem as a Constrained Markov Decision Process (CMDP). Then, we propose a Lagrangian-based Proximal Policy Optimization (PPO)-CMDP algorithm to optimize the primal pair-based policy optimization problem. Finally, simulation evaluation shows that our proposed algorithm has significant advantages in terms of network throughput and task completion time.

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Driven by the increasing computational demand of real-time mobile applications, Unmanned Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges and constructing high-throughput line-of-sight links for ground users. Most exsiting studies consider simplified scenarios, such as a single UAV, Service Provider (SP) or service type, and centralized UAV trajectory control. In order to be more in line with real-world cases, we intend to achieve distributed trajectory control of multiple UAVs in UAV-assisted MEC networks with multiple SPs providing differentiated services. Our objective is to minimize the short-term computational costs of ground users and the long-term computational cost of UAVs, simultaneously based on incomplete information. We first solve the formulated problem by reaching the Nash Equilibrium (NE) of the game among SPs based on complete information. We further formulate a Markov game model and propose a Deep Reinforcement Learning (DRL)-based UAV trajectory optimization algorithm, where only local observations of each UAV are required for each SP's flying action execution. Theoretical analysis and performance evaluation demonstrate the convergence, efficiency, scalability, and robustness of our algorithm compared with other representative algorithms.

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, successive convex approximation (SCA) and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Blockchain has been leveraged to secure transactions for the m-commerce. However, the intensive computation in the mining process restricts the participation of mobile devices. Currently, some studies have deployed edge computing services to support the mining process, where edge servers managed by one Service Provider (SP) are considered. This paper investigates a more practical scenario with multiple SPs, where servers managed by different SPs have distinct capacities and prices, making miners’ offloading decisions rather complicated. To tackle the above challenges, we consider task offloading, block propagation and miner mobility comprehensively to maximize utilities of miners. Specifically, we first formulate a Markov game, and then design a learning-based offloading algorithm for off-chain computation, where a novel learning model is constructed by integrating Deep Reinforcement Learning (DRL) and Mean Field Theory (MFT) to guarantee a Nash equilibrium. Different from existing studies, each miner merely needs to respond to the average effect from others in our system, insteading of knowing policies of others. Finally, both theoritical and performance results show that our designed algorithm has superiority on average miner utilities and algorithm convergence time compared with other representative algorithms.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed$\epsilon$-Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

The paradigm of Internet of Things (IoT) has attracted much attention in both academia and industry areas in the past decades. Recently, the integration of IoT and social networks is advocated to promote further development. This article focuses on the applications, solutions, and challenges of Social Internet of Things (SIoT) over collaborative edge computing, which exploits the advantages of both mobile edge computing and social relationships among SIoT users. First, applications of collaborative offloading, caching, and streaming data processing are illustrated, followed by representative social-aware solutions, including auction, coalition game, and federated learning. Finally, several research challenges are described. The main contributions of this article are as follows: 1) we specify the role of social ties in traditional applications of IoT and its impacts on individual’s selection; 2) we elaborate the reasons why the presented three approaches can be applied in SIoT; and 3) the discussed challenges can contribute to the future development of secure, robust, and intelligent SIoT frameworks.

Big data and cognitive computing have a wide range of applications in smart homes, smart cities, artificial intelligence, and computational social systems. Visible light positioning systems have attracted more and more attention as one of the application scenarios of computational social systems. In visible light positioning systems, the light-emitting diodes (LEDs) ceiling layout makes the smartphone usually obtain less than three LEDs in captured images. Due to the lack of necessary positioning information, most scholars combine the inertial measurement unit (IMU) with the modified filter algorithms to achieve positioning under the sparse light source. However, these systems have the following problems: 1) the azimuth angle obtained by the IMU is always not accurate, which decreases the positioning accuracy and 2) during the dynamic positioning process, the system’s initial position is difficult to automatically determine. In this article, we propose indoor high-precision visible light positioning under the sparse light source. First, we propose the geometric correction mechanism, which uses ellipse fitting to calibrate the azimuth angle, so as to increase the positioning accuracy for the static system. Then, we build a motion model for the entire positioning process through the unscented particle filter (UPF), which does not need to manually set initial state parameters, due to random generated particles. It can increase the positioning accuracy for the dynamic system. We evaluate our designed system, and the experimental results show that the average azimuth angle error is 2.04° and average positioning error is 8.8 cm, under the sparse light source.

The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users’ privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-in-the-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method.

Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the industrial Internet of things (IIoT). Artificial intelligence (AI)-enhanced distributed resource management in edge networks has aroused researchers’ widespread attention. However, dynamically arrived service requests and limited edge resources complicate the service scheduling issue. In this article, we establish a dynamic network virtualization technique enabled service function chain (SFC) orchestration framework in IIoT, formulate the joint optimization problem to maximize total utility and decompose it into two subproblems, i.e., SFC selection and dynamic SFC orchestration. A dynamic orchestration of SFC (DOS) scheme, consisting of resource-aware matching algorithm and averaged multistep double deep q-network algorithm, is designed to embed SFC requests distributedly on the optimal virtualized network function chains. At last, we validate the superiority of our proposed DOS scheme by experimental results.

It is critical to reduce the operating cost of shared buses for bus companies and improve the user experience of passengers. However, existing studies focus on either bus scheduling or route planning, which cannot accomplish the above mentioned goals concurrently. In this paper, we construct a joint bus scheduling and route planning framework to maximize the number of passengers, minimize the total length of routes and the number of required buses, as well as guarantee good user experience of passengers. First, we establish a system model based on a real-world scenario and formulate a multi-objective combinational optimization problem. Then, based on the extracted traffic topology of urban traffic networks and the generated candidate line set, we propose an offline algorithm to cope with the similar passenger flow distributions, e.g., morning or evening peak of every day. In order to cope with dynamic real-time passenger flows, an online algorithm is designed. Experiments are carried out based on real-word scenarios. The results show that the proposed algorithms can greatly reduce the operating cost of bus companies and guarantee good user experience based on real-world scheduling data in comparison with several existing methods.

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

Intelligent Transportation System (ITS) is critical to cope with traffic events, e.g., traffic jams and accidents, and provide services for personal traveling. However, existing researches have not jointly considered the user data safety, utility and system latency comprehensively, to the best of our knowledge. Since both safe and efficient transmissions are significant for ITS, we construct a blockchain-enabled crowdsensing framework for distributed traffic management. First, we illustrate the system model and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two subproblems, and propose the corresponding schemes, i.e., a Deep Reinforcement Learning (DRL)-based algorithm and a DIstributed Alternating Direction mEthod of Multipliers (DIADEM) algorithm. Extensive experiments are carried out to evaluate the performance of our solutions, and experimental results demonstrate that the DRL-based algorithm can legitimately select active miners and transactions to make a satisfied trade-off between the blockchain safety and latency, and the DIADEM algorithm can effectively select task computation modes for vehicles in a distributed way to maximize their social welfare.

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark.

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable because of the large number of prior network traffic information, which needs to consume expensive network resources for sampling. Motivated by that, we propose an effective prediction mechanism using multitask learning (MTL), which is a special paradigm of transfer learning. A deep learning architecture constructed by MTL and long short-term memory is designed. This deep architecture takes advantage of link loads as additional information to improve prediction accuracy. We provide a theoretical analysis for the MTL mechanism. The effectiveness is evaluated by implementing our mechanism on real network.

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

Pervasive edge computing refers to one kind of edge computing that merely relies on edge devices with sensing, storage and communication abilities to realize peer-to-peer offloading without centralized management. Due to lack of unified coordination, users always pursue profits by maximizing their own utilities. However, on one hand, users may not make appropriate scheduling decisions based on their local observations. On the other hand, how to guarantee the fairness among different edge devices in the fully decentralized environment is rather challenging. To solve the above issues, we propose a decentrailized computation offloading algorithm with the purpose of minimizing average task completion time in the pervasive edge computing networks. We first derive a Nash equilibrium among devices by stochastic game theories based on the full observations of system states. After that, we design a traffic offloading algorithm based on partial observations by integrating general adversarial imitation learning. Multiple experts can provide demonstrations, so that devices can mimic the behaviors of corresponding experts by minimizing the gaps between the distributions of their observation-action pairs. At last, theoretical and performance results show that our solution has a significant advantage compared with other representative algorithms.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.

Due to the ever-increasing requirements of delay-sensitive and mission-critical applications in 5G, mobile edge computing is promising to react and support real-time interactive systems. However, it is still challenging to construct a 5G-enabled traffic management system, owing to the qualification of ultra-low latency and ubiquitous connectivity. Furthermore, the computing resources and storage capacities of edge nodes are limited, thus computation offloading is a fundamental issue for real-time traffic management. This paper puts forward a hybrid computation offloading framework for real-time traffic management in 5G networks. Specially, we consider both nonorthogonal-multiple-access-enabled and vehicle-to-vehicle-based traffic offloading. The investigated problem is formulated as a joint task distribution, subchannel assignment, and power allocation problem, with the objective of maximizing the sum offloading rate. After that, we prove its NP-hardness and decompose it into three subproblems, which can be solved iteratively. Performance evaluations illustrate the effectiveness of our framework.

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

Driving is an important part in daily life. But it is also dangerous when people drive in a bad mood or when people are tired. In this paper, we propose SAR, a novel social aware route recommendation system, which is designed to recommend proper routes to drivers to help diminish negative mood and fatigue and help improve driving experience. SAR integrates multiple sources of sensing data from smartphones to recommend a proper route to the driver considering the driver’s real-time mood-fatigue, history record, and social information. Experiments have been conducted with SAR to demonstrate the system’s effectiveness as well as reasonable computation and communication overheads on smartphones.

Due to the heterogeneous and resource-constrained characters of Internet of Things (IoT), how to guarantee ubiquitous network connectivity is challenging. Although LTE cellular technology is the most promising solution to provide network connectivity in IoTs, information diffusion by cellular network not only occupies its saturating bandwidth, but also costs additional fees. Recently, NarrowBand-IoT (NB-IoT), introduced by 3GPP, is designed for low-power massive devices, which intends to refarm wireless spectrum and increase network coverage. For the sake of providing high link connectivity and capacity, we stimulate effective cooperations among user equipments (UEs), and propose a social-aware group formation framework to allocate resource blocks (RBs) effectively following an in-band NB-IoT solution. Specifically, we first introduce a social-aware multihop device-to-device (D2D) communication scheme to upload information toward the eNodeB within an LTE, so that a logical cooperative D2D topology can be established. Then, we formulate the D2D group formation as a scheduling optimization problem for RB allocation, which selects the feasible partition for the UEs by jointly considering relay method selection and spectrum reuse for NB-IoTs. Since the formulated optimization problem has a high computational complexity, we design a novel heuristic with a comprehensive consideration of power control and relay selection. Performance evaluations based on synthetic and real trace simulations manifest that the presented method can significantly increase link connectivity, link capacity, network throughput, and energy efficiency comparing with the existing solutions.

As an emerging communication platform, opportunistic Cloud of Things (CoT) is promising for clients to exchange messages through opportunistic contacts in cloud computing-enabled Internet of Things. Recently, numerous socially aware schemes have been put forward, leveraging users’ social attributes and contact history to predict future contacts with the purpose of improving message forwarding efficiency and network throughput. However, individual privacy is generally overlooked in the prediction process and transmission stage of opportunistic CoT. In this paper, we construct a privacy-preserving message forwarding framework for opportunistic CoT to guarantee individual privacy and improve transmission efficiency. We first set up a two-layer architecture of a cloud server to improve communication efficiency for terminal clients. By integrating a security-based mobility prediction algorithm with a routing decision process, our scheme can effectively protect individual privacy. We integrate an attribute-based cryptographic algorithm with a message delivery process to enable our scheme to resist attacks, such as Sybil attack, drop for profit, and data tampered attack. Compared with some existing solutions, our scheme improves network security significantly at the cost of slightly increased communication overhead.

Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computational resources for end users, by which low latency can be guaranteed. In this paper, we put forward a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. First, we construct a distributed city-wide traffic management system, in which vehicles close to road side units can be utilized as fog nodes. Then, we model parked and moving vehicle-based fog nodes according to a queueing theory, and draw the conclusion that moving vehicle-based fog nodes can be modeled as an $M/M/1$ queue. An approximate approach is developed to solve the offloading optimization problem by decomposing it into two subproblems and scheduling traffic flows among different fog nodes. Performance analyses based on a real-world taxi-trajectory datasets are conducted to illustrate the superiority of our method.

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low‐pass component of network traffic that describes the long‐range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low‐pass component. Otherwise, for the remaining high‐pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

Opportunistic Network (OppNet) is an emerging communication paradigm, by which nodes inside forward messages through personal contact opportunities. Recently, numerous studies have focused on predicting nodes meeting to promote routing efficiency and reduce transmission delay. However, individual privacy would likely be revealed to strangers or attackers during the execution of prediction. In this paper, we construct a privacy-reserved network framework for message forwarding to guarantee both efficient communication and individual privacy in OppNets, including a security-based prediction method and an attribute-based cryptosystem. Simulation results demonstrate that, our algorithm outperforms TRSS on average delivery ratio, generally by 15% for dropping probability and data tempered probability.