The vast geographic coverage and sparse customer distribution in rural areas lead to inefficiency in traditional last-mile delivery. Truck-drone collaborative delivery systems have emerged as a promising solution to these rural logistics challenges. Accordingly, we introduce a multi-truck multi-drone collaborative delivery framework. Within this framework, we propose a novel graph embedding module—the edge-gated graph attention network (EG-GAT)—which incorporates multi-dimensional edge features into the attention mechanism and introduces a learnable gating module for adaptive multi-head fusion. We further propose a graded flexible time window mechanism, which permits limited service advancement or deferral while applying graded incentive-penalty structures. This approach better captures the temporal flexibility inherent in rural customer service requirements. The resulting multi-objective truck-drone routing problem is modeled as a rewardmaximization task and solved using multi-agent proximal policy optimization (MAPPO) under a centralized-training with decentralized-execution framework. Extensive experimental results demonstrate that the proposed method outperforms other approaches. Furthermore, studies assess the individual effects of graded flexible time window settings and objective function weight coefficients on the optimization performance of collaborative delivery. Finally, we evaluate the practical advantages of our proposed model using real-world rural road cases.

Mobile Crowdsensing (MCS) is a new data acquisition method that has emerged with the proliferation of smart mobile devices. With the expanding scale of urban sensing, the locations of tasks and users become critical information, which plays a significant role in crowd-sensing and task scheduling areas. Tasks in areas with a high concentration of users can be completed quickly, whereas tasks in sparsely populated areas are challenging to accomplish. To address this issue, existing research has primarily focused on task assignment to designated users, assuming that users' motivations are rational, while neglecting the impact of psychological factors on their motivations. Therefore, we propose an incentive mechanism based on prospect theory, analyzing the decisions users might make under irrationality and then adjusting corresponding rewards to influence user decisions. This paper transforms the problem of maximizing the data value in crowdsensing into an ordered submodular function model. Our proposed incentive mechanism consists of three components: User Decision-Making, User Selection, and Payment Determination. In the User Decision-Making phase, users calculate the prospect value based on the auction results from the previous round to make decisions. In the User Selection phase, users are chosen based on marginal value. In the Payment Determination phase, rewards for winning users are designed based on the ordered submodular model. The platform provides auction results as a reference for the next round. In the experimental section, we demonstrate that the incentive mechanism can enhance the platform's value.

Recently, federated learning (FL) has attracted much attention as a promising decentralized machine learning method that provides privacy and low latency. However, the communication bottleneck is still a problem that needs to be solved to effectively deploy FL on wireless networks. In this article, we aim to minimize the total convergence time of FL by sparsifying and quantizing local model parameters before uplink transmission. More specifically, we first present the convergence analysis of the FL algorithm with random sparsification and quantization, revealing the impact of compression error on the convergence speed. Then, we jointly optimize the computation, communication resources and the number of quantization bits, sparsity to minimize the total convergence time, subject to the energy and compression error requirements derived from the convergence analysis. By simulating the impact of different compression errors on model accuracy, we reveal that the low-precision updates do not inherently yield a better balance between efficiency and accuracy than the high-precision updates. Furthermore, compared with the equal resource allocation schemes and the unilateral compression optimization schemes on four different data distributions, the proposed scheme has faster convergence speed and less total convergence time.

The deployment of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) has proven to be an effective means to extend coverage and improve wireless signal quality. STAR-RIS in wireless networks for aided Unmanned Aerial Vehicle (UAV) communications enables a significant boost in network capacity and the provision of virtual line-of-sight links to efficiently meet the quality-of-service (QoS) requirements of user equipment (UE). Accordingly, this paper proposes a novel STAR-RIS-aided multi-UAV communication framework to exploit energy efficiency and total throughput maximally. We formulate the long-term optimization problem as a decentralized, partially observed Markov decision process (DEC-POMDP). Then, we formulate the discrete association scheduling problem as a non-cooperative theoretical game and propose the UA-CFG algorithm to realize the UE association scheme that converges to a Nash equilibrium (NE). Then, a multi-agent reinforcement learning (MARL) method with well-established robustness is devised to continuously optimize the trajectories and energetic consumption of UAVs through centralized training and distributed implementation. Experimental results reveal that the performance of the proposed algorithm is considerable compared to other traditional schemes.

Mobile edge computing (MEC) is a highly promising approach for achieving low-latency and high-performance computing services for mobile users. However, traditional MEC systems face challenges in meeting the increasing demands of mobile users due to the limited coverage and flexibility of fixed MEC servers. Integrating unmanned aerial vehicles (UAVs) with MEC has gained significant attention as a promising way to improve the MEC networks’ performances and meet the demands of next-generation networks. UAVs can act as flying edge servers, providing mobile users with flexible and on-demand computing resources. This article shows a new way to use the grey wolf optimizer (JDTS-GWO) algorithm to improve both the placement of UAVs and the scheduling of tasks in a multi-UAV MEC system. The objective is to minimize the overall system’s energy consumption while meeting various constraints, such as UAV coverage, collision avoidance, and task execution requirements. The proposed approach formulates the joint optimization approach, considering the deployment of UAVs, offloading decisions, and resource allocation. An encoding scheme is proposed to represent UAV deployment and task allocation within the JDTS-GWO framework. Simulations demonstrate significant improvements in energy efficiency and task completion compared to existing benchmarks, with up to 35% energy savings and a 98% task completion rate. Sensitivity analysis confirms the approach’s scalability and robustness. The problem is modeled as a mixed-integer nonlinear programming (MINLP) problem, taking into account the consumed energy of mobile nodes, UAVs, and the MEC system. The JDTS-GWO algorithm is adapted to solve the optimization problem efficiently.

Through Internet of Things (IoT) communication technology, collaborative perception enhances a vehicle’s capacity to discern its surroundings while driving by integrating and synchronizing sensor data from multiple agents. With the advancement of cooperative perception techniques in single-modality methods, there has been a growing trend toward integrating multimodal data from heterogeneous sensors in recent years. However, due to the data heterogeneity inherent in diverse sensors, Bird’s Eye View (BEV) maps generated from different types of sensors may exhibit local discrepancies in the spatial representation of entity positions. Furthermore, individual agents may produce uncertain and flawed feature representations in real noisy environments. The influence of this indeterminacy exacerbates the issue of local inconsistency, leading to misalignment of the detected target during BEV alignment and fusion, thereby reducing detection accuracy. To address these problems, we propose a modal decision-making spatial alignment cooperative perception network (MDNet). First, the network generates BEV feature maps through dense depth image supervision for voxel feature extraction and model-guided selective feature fusion. Subsequently, we achieve enhanced accuracy in object detection by performing spatial alignment of BEV representations generated from two distinct sensors, both globally and locally within the spatial domain. Besides, we employ a cascaded centralized pyramid strategy during the message fusion stage, facilitating flexible sampling across horizontal and vertical spatial dimensions, promoting deep interaction among multiple agents. We conduct quantitative and qualitative experiments on the public OPV2V and DAIR-V2X-C benchmarks, and our proposed MDNet exhibits superior performance and stronger robustness in the 3-D object detection task, providing more precise target detection results.

The rise of low Earth orbit (LEO) satellites and autonomous aerial vehicles (AAVs) has facilitated the integration of space-air–terrestrial networks, enabling ubiquitous data collection in Internet of Remote Things (IoRT) networks. However, limited battery capacity of sinks/AAVs and scarce spectrum in these networks make it challenging to build spectrum-energy efficient backbone networks for timely data collection. In this article, we explore the resource allocation problem in Sink-AAV-LEO-based data collection scenarios, where sink nodes gather data from sensors, and AAVs relay it to LEO satellites. To ensure data collection timeliness while optimizing energy and spectrum use, we focus on optimizing the three-hop network structure, power control for sink nodes, path selection and power control for AAVs, and bandwidth allocation for AAV-LEO links. Although these tasks should theoretically be modeled as a global optimization problem, solving them within tolerable time complexity is difficult. Therefore, we first establish a three-hop network using our proposed optimization mechanism. Then, we describe the Sink-AAV and AAV-LEO resource allocation problems, and propose the corresponding solutions via utilizing our improved deep reinforcement learning algorithm, namely ISS-proximal policy optimization. Finally, we solve the global problem based on the solutions to the above two local problems. Besides, our simulation results are compared with various benchmark schemes, validating the effectiveness and superiority of our proposed solutions across different perspectives.

With the advent of the sixth-generation (6G) wireless communications, transmission speeds are projected to exceed tenfold those of 5G, reaching theoretical peak download speeds of up to 1 Tbps. Data transmission capacity and speed will be significantly enhanced, enabling emerging applications, such as mixed reality, federated learning, and digital twins, driving exponential data traffic growth. To address this, the space-air–ground integrated network (SAGIN) combines satellite, aerial, and ground communication technologies, offering seamless global coverage and high-speed connectivity. In this article, we proposes an SAGIN framework integrated with mobile edge computing (MEC) to jointly optimize system energy consumption and delay costs. Specifically, we decompose the optimization problem into three subproblems: 1) uncrewed aerial vehicle (UAV) computational resource allocation; 2) satellite computational resource allocation; and 3) task offloading and channel allocation. The subproblems are then transformed and addressed using Newton’s interior point method and the deep reinforcement learning DQN algorithm to derive optimal allocation strategies for UAV and satellite computing resources, along with task offloading and channel resources, that our proposed algorithm effectively reduces system energy consumption and delay costs compared to other algorithms.

Cloud gaming represents a major part of contemporary gaming. To boost the Quality-of-Experience (QoE) of cloud gaming, the integration of Dynamic Adaptive Video Encoding (DAVE) with Multi-access Edge Computing (MEC) has become the natural candidate owing to its flexibility and reliable transmission support for real-time interactions. However, as multiple gamers compete for limited resources to achieve personalized QoE, such as ultra-high video quality and ultra-low latency, how to support efficient edge resource optimization is a fundamental and important problem. Furthermore, determining the optimal game video encoding configuration in real-time poses significant challenges, especially when lacking the information on future video and edge network resources. To address these key issues, we jointly optimize the video encoding as well as computing and communication resource allocation by active mutual adaptation of video coding configurations and physical resources in a Software Defined Networking (SDN)-assisted edge network. This eliminates the performance bottleneck caused by decoupling optimization of coding parameter configuration and physical resource allocation. The SDN-assisted edge network architecture supports efficient on-demand resource management, provides global network information, and meets the stringent time-varying game requests. Due to the significant time scale difference between video chunk and physical resource block, we propose a novel Asynchronous Decision-Making Multi Agent Proximal Policy Optimization algorithm (AD-MAPPO), which can address the credit assignment problem with a single agent. It can also adapt to the highly dynamic cloud gaming environment without prior knowledge and a deterministic environmental model. Extensive experimentation based on real cloud gaming datasets convincingly demonstrates that our approach can significantly enhance the overall QoE of gamers.

Mobile Crowdsensing (MCS) has emerged as a promising sensing paradigm for accomplishing large-scale tasks by leveraging ubiquitously distributed mobile workers. Due to the variability in sensory data provided by different workers, identifying truth values from them has garnered wide attention. However, existing truth discovery schemes either offer limited privacy protection or incur high participation costs and lower data aggregation quality due to malicious workers. In this paper, we propose an Efficient and Trusted Bilateral Privacy-preserving Truth Discovery scheme (ETBP-TD) to obtain high-quality truth values while preventing privacy leakage from both workers and the data requester. Specifically, a matrix encryption-based protocol is introduced to the whole truth discovery process, which keeps locations and data related to tasks and workers secret from other entries. Additionally, trust-based worker recruitment and trust update mechanisms are first integrated within a privacy-preserving truth discovery scheme to enhance truth value accuracy and reduce unnecessary participation costs. Our theoretical analyses on the security and regret of ETBP-TD, along with extensive simulations on real-world datasets, demonstrate that ETBP-TD effectively preserves workers’ and tasks’ privacy while reducing the estimated error by up to 84.40% and participation cost by 54.72%.

The rapid evolution of the Internet-of-Vehicles (IoV) has amplified the need for mobile computing resources, driving the shift toward offloading tasks to edge servers or vehicles with idle resources to optimize computational efficiency. To this end, an approach based on Deep Reinforcement Learning (DRL) is presented in this paper, termed DVTP, which integrates Variational Graph Attention Networks (VGAT) and Transformer models to optimize Directed Acyclic Graph (DAG) task scheduling in vehicular networks. DVTP effectively captures both the spatiotemporal information and task dependencies, enabling more accurate and efficient task offloading decisions. Extensive simulation experiments demonstrate that DVTP outperforms traditional methods in reducing task completion times across various multi-vehicle and multi-edge server scenarios, showcasing its potential for real-world IoV applications.

Network-assisted full-duplex (NAFD) cell-free (CF) massive MIMO systems enable simultaneous uplink and downlink transmissions, where interference suppression and beamforming are critical for improving spectral efficiency and system performance. However, the asymmetric time-varying properties of current network traffic, coupled with the interference problems associated with complex network topologies, make existing resource allocation and interference management strategies difficult to handle, and unable to satisfy the low-latency, high-reliability Quality-of-Service (QoS) requirements of the growing number of terminal devices (TDs). To address these challenges, we propose a novel access method based on decoupled uplink-downlink multi-connectivity transmission to achieve flexible access selection and formulate an optimization problem that maximizes the cumulative fair spectral efficiency by simultaneously optimizing power allocation and link scheduling. To solve this mixed-integer nonlinear programming (MINLP) problem, we propose an optimized transfer scheme that reduces the dimensionality of the action and constraint spaces. Then, we characterize the network states as heterogeneous graph structures and employ node-level and metapath-level attention mechanisms for message passing and aggregation, and obtain graph-level scheduling policy via the heterogeneous graph neural network (HGNN). Finally, in light of the superior performance of Deep Reinforcement Learning (DRL) in exploration-based tasks, we design a holistic updating mechanism using environmental feedback and advantage state-action function, named as HGN-DRL for this end-to-end learning framework. Simulation results demonstrated the effectiveness and scalability of HGN-DRL in large-scale cell-free scenarios.

Task offloading is a widely used technology in Edge Computing (EC), which declines the makespan of user task with the aid of resourceful edge servers. How to solve the competition for computation and communication resources among tasks is a fundamental issue in task offloading. Besides, real-life user tasks often comprise multiple interdependent subtasks. Dependencies among subtasks significantly raises the complexity of task offloading, and makes it difficult to propose generalized approaches for scenarios of different size. In this paper, we study the Dependent Task Offloading (DTO) problem within both single-user single-edge and multi-user multi-edge scenario. First, we use Directed Acyclic Graph (DAG) to model dependent task, where nodes and directed edges represent the subtasks and their interdependencies respectively. Then, we propose a task scheduling method based on Graph Attention Network (GAT) and Deep Reinforcement Learning (DRL) to minimize the makespan of user tasks. More specifically, our method introduces a multi-discrete action DRL scheduler that simultaneously determines which subtask to consider and whether it should be offloaded at each step, and employs GAT to encode the graph-based state representation. To stabilize and speed up DRL scheduler training, we pretrain GAT encoder with unsupervised learning. Extensive experiments demonstrate that our proposed approach can be applied to various environments and outperforms prior methods.

In the digital divide regions, the edge computing can improve the performance of application services for the Internet of Things (IoT) devices. However, the lagging of information and communication technology (ICT) results in congested access spectrum and imbalanced computational load. Moreover, the mobility of IoT devices further exacerbates the fluctuating quality of communication links and the frequent changing of access positions. So, how to realize the reliable service requirements of devices in a heterogeneous environment with multiscale constraints should be considered appropriately and comprehensively. In this article, we model a relay-assisted multiaccess edge computing (MEC) framework, employing multihop transmission to enable the cross-domain service coverage. Under this framework, we formulate a quantitative model to characterize communication and computation processes within task migration, and derive analytical results for service latency. To improve the access resource efficiency, we adopt a joint nonorthogonal multiple access (NOMA) scheme to extend the transmission dimension, and employ proportional fairness to dynamically allocate resources. Besides, we propose a multiagent deep reinforcement learning (DRL) for optimizing the long-term task offloading scheduling, address the optimization problem of maximizing the system throughput efficiency. And we improve the action exploration and output dimensions of DRL to achieve convergence and performance enhancement. Simulation and analytical results show that our proposed algorithm outperforms the comparison algorithms in the key performance indicators.

Mobile Crowd Sensing (MCS), as a promising sensing paradigm, significantly relies on wireless communication networks and widely distributed mobile workers to capture data from the surroundings. However, the positioning-dependent nature of most MCS tasks often requires workers to embed their positionings in reports, which may result in privacy leakage and a decline in their participation enthusiasm. Considering workers’ diverse perceptions of positioning privacy, in this paper we propose the Lightweight Positioning-Privacy Protection Scheme with Double-Layer Incentives (L3P-DLI) to meet their personalized privacy requirements in an efficient and low-cost way while stimulating their participation. To the best of our knowledge, this scheme is the first attempt to employ proxy forwarding to protect workers’ sensitive positionings while ensuring high-quality sensing results. Moreover, our double-layer incentivizing mechanism is elaborately designed to motivate workers to actively participate or serve as proxies. Specifically, the bidirectional auction between data collectors and proxies can safeguard the security of data collectors, and compensate for the potential privacy leakage cost of proxies helping to forward data. Additionally, the reverse auction mechanism enables the platform to reward recruited workers to compensate for their various costs. Extensive experiments conducted on real-world datasets validate that L3P-DLI effectively preserves workers’ positioning privacy while maximizing their income to encourage participation.

Unmanned Aerial Vehicles (UAVs) can be employed as short-term aerial base stations or as access points for User Equipments (UEs) to communicate with other UEs effectively. However, communication links may be obstructed by buildings, leading to poor data transfer performance and significant energy consumption. Deploying Reconfigurable Intelligent Surfaces (RIS) as part of the UAV-assisted communication system proves to be an effective means to avoid building obstructions and enhance wireless information quality. However, the complexity of communication relationships in multi-UAV systems with RIS-aided communication poses a significant challenge in energy reduction. Therefore, this study investigates a new RIS-aided multi-UAV communication framework for edge computing systems. The system aims to meet the quality-of-service (QoS) for UEs while minimizing the total energy consumption. To optimize the total energy consumption of RIS-aided multi-UAV communication, the impact of communication between multiple UAVs and differences between UE clusters on that system’s performance is also considered. We introduce a Stackelberg game to deal with the communication relationship between multiple UAVs and design a K-means-based clustering algorithm to segment UEs periodically. A model-free deep reinforcement learning algorithm grounded in maximum entropy is proposed to jointly optimize UAV trajectory design, phase shift control, and power allocation to reduce energy consumption further. Experimental results indicate that the system proposed performs favorably concerning both energy consumption and throughput.

In the disaster-hit areas where ground network infrastructure has been severely damaged, one challenging problem for multi-UAV-assisted disaster relief networks is how to improve the coverage probability of each UAV. On the basis of solving this problem, the second challenging problem is how to design a channel and power-beam allocation scheme to optimize system throughput while meeting spectrum-energy efficiency constraint. In this paper, we first propose a new method for measuring single UAV coverage quality, which considers both the ratio of effective coverage time to single loop flight time and that of the ground terminals with effective coverage time to the total ground terminals. Then, we develop a set of new algorithms to take advantage of the uneven distribution of ground terminals, which can achieve the total coverage probability improvement and the reduction of deployment costs of UAVs. Finally, we formulate the second problem as Markov decision process (MDP) and develop a solution based on deep deterministic policy gradient (DDPG). Simulation results demonstrate the validity and superiority of our proposed solutions compared with other benchmark strategies in different perspectives.

Mobile edge computing (MEC), located at the networks edge, enhances distributed computing. However, its fixed position presents limitations during emergencies. Integrating unmanned aerial vehicles (UAVs) into MEC systems offers a solution but introduces challenges in managing UAV collaboration. This paper proposes a Reinforcement Deep Q-Learning based multi-UAV MEC framework to optimize quality of service (QoS) and route planning. The proposed framework addresses these challenges by modeling user demand and using multi-factor optimization considering user demand, risk, and distance. A Markov Decision Process (MDP) models user demand for higher QoS. The reinforcement learning reward matrix incorporates terminal user demand, risk, and distance for efficient energy use and resource allocation. Simulations demonstrate the effectiveness of our proposed method, offering valuable insights for future research in this domain.

Heterogeneous communication environments and broadcast feature of safety-critical messages bring great challenges to mode selection and resource allocation problem. In this paper, we propose a federated multi-agent deep reinforcement learning (DRL) scheme with action awareness to solve mode selection and resource allocation problem for ensuring quality of service (QoS) in heterogeneous V2X environments. The proposed scheme includes an action-observation-based DRL and a model parameter aggregation algorithm considering local model historical parameters. By observing the actions of adjacent agents and dynamically balancing the historical samples of rewards, the action-observation-based DRL can ensure fast convergence of each agent’ individual model. By randomly sampling historical model parameters and adding them to the foundation model aggregation process, the model parameter aggregation algorithm improves foundation model generalization. The generalized model is only sent to each new agent, so each old agent can retain the personality of its individual model. Simulation results show that the proposed scheme outperforms the comparison algorithms in the key performance indicators.

The vision of heterogeneous vehicle networks (HetVNETs) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating mobile-edge computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multivehicle scramble for communication and computational resources challenging. This article intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: 1) high-reliability transmission (HRT) and 2) computational delay minimization (CDM). Specifically, we develop a hybrid MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and nonconvex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the nonsmooth HRT problem. To solve the CDM problem, we transform it into two subproblems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.

The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What’s more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

The gap between the peak-hour Internet and the average level is increasing, which inevitably creates a type of temporary cellular weak coverage when there is a surge in data traffic demand, where any cell-edge device will have a low spectrum efficiency (SE). In this article, we propose a novel incentive architecture based on the dynamic radio frequency charging technology to improve the SE and use the Stackelberg game theory to formulate the problem. In such a model, a small base station (SBS) acts as the leader to offer a desired partition of the resource block obtained by a cell-edge device, while some small energy providers (SEPs) and small virtual access points (SVAPs) that are selected from user equipment act as the followers to make their decisions, respectively, to compete for the free part of such a resource block. Following the potential game rules, all the SEPs compete for a specific free resource part allocated by the SBS, and then, all the SVAPs compete for another nonoverlapping part allocated by the SBS on the basis of the results of the SEPs' potential game. Although our incentive architecture formally has three game stages, it is essentially a two-level Stackelberg game, which is analyzed by using a backward induction method. The theoretical analysis proves the convergence of the above-mentioned game models, and the simulation results demonstrate that the proposed incentive architecture can improve the SE for each cell-edge device.

In this paper, we explore how a rotary‐wing unmanned aerial vehicle (UAV) acts as an aerial millimeter wave (mmWave) base station to provide recharging service and radio access service in a postdisaster area with unknown user distribution. The addressed optimization problem is to find out the optimal path starting and ending at the same recharging point to cover a wider area under limited battery capacity, and it can be transformed to an extended multiarmed bandit (MAB) problem. We propose the two improved path planning algorithms to solve this optimization problem, which can improve the ability to explore the unknown user distribution. Simulation results show that, in terms of the total number of served user equipment (UE), the number of visited grids, the amount of data, the average throughput, and the battery capacity utilization level, one of our algorithms is superior to its corresponding comparison algorithm, while our other algorithm is superior to its corresponding comparison algorithm in terms of the number of visited grids.

Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context‐awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

Geographic routing is a research hotspot of the Internet of Vehicles (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between the vehicle and position but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features and vehicle association potential features and propose a routing algorithm based on vehicle position (RAVP) analysis, which can obtain the more accurate vehicle prediction trajectory. Then, the vehicle distance is obtained based on the vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end-to-end delay, and routing overhead ratio.

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link.

The ultradensification deploying for cellular networks is a direct and effective method for the improvement of network capacity. However, the benefit is achieved at the cost of network infrastructure investment and operating overheads, especially when there is big gap between peak‐hour Internet traffic and average one. Therefore, we put forward the concept of virtual cellular coverage area, where wireless terminals with high‐end configuration are motivated to enhance cellular coverage quality by both providing RF energy compensation and rewarding free traffic access to Internet. This problem is formulated as the Stackelberg game based on three‐party circular decision, where a Macro BS (MBS) acts as the leader to offer a charging power to Energy Transferring Relays (ETRs), and the ETRs and their associating Virtual Access Points (VAPs) act as the followers to make their decisions, respectively. According to the feedback from the followers, the leader may readjust its strategy. The circular decision is repeated until the powers converge. Also, the better response algorithm for each game player is proposed to iteratively achieve the Stackelberg‐Nash Equilibrium (SNE). Theoretical analysis proves the convergence of the proposed game scheme, and simulation results demonstrate its effectiveness.

The schemes detecting and mitigating routing misbehavior in other wireless networks are not suitable for Wireless Mesh Network (WMN), whereas the researchers do not pay much attention to WMN's routing misbehavior. We present a new scheme for WMN in this paper. Differing from existing schemes, mobile client nodes do not add new functions, whereas mesh routers detect and mitigate routing misbehavior in WMN. Mesh routers obtain routing misbehavior information from source route field of packets based on Dynamic Source Routing (DSR). This scheme do not increase additional routing overhead, whereas it only increase computing overhead of mesh routers without power constraint. Simulation results are presented to evaluate the performance of the proposed scheme, which show the desirable feature of detecting and mitigating routing misbehavior.