Low altitude uncrewed aerial vehicles (UAVs) are expected to facilitate the development of aerial-ground integrated intelligent transportation systems and unlocking the potential of the emerging low-altitude economy. However, several critical challenges persist, including the dynamic optimization of network resources and UAV trajectories, limited UAV endurance, and imperfect channel state information (CSI). In this paper, we offer new insights into low-altitude economy networking by exploring intelligent UAV-assisted vehicle-to-everything communication strategies aligned with UAV energy efficiency. Particularly, we formulate an optimization problem of joint channel allocation, power control, and flight altitude adjustment in UAV-assisted vehicular networks. Taking CSI feedback delay into account, our objective is to maximize the vehicle-to-UAV communication sum rate while satisfying the UAV's long-term energy constraint. To this end, we first leverage Lyapunov optimization to decompose the original long-term problem into a series of per-slot deterministic subproblems. We then propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm, which innovatively integrates diffusion models to determine optimal channel allocation, power control, and flight altitude adjustment decisions. Through extensive simulations using real-world vehicle mobility traces, we demonstrate the superior performance of the proposed D3PG algorithm compared to existing benchmark solutions.

Collaborative perception is a vital approach for autonomous systems to understand complex environments by sharing information among multiple agents. However, existing methods often struggle with spatiotemporal misalignment and inefficient feature fusion under dynamic, uncertain conditions. To address these challenges, we propose HGSTA (HyperGraph Spatial-Temporal Awareness), a novel framework that integrates hypergraph computing for multi-scale spatiotemporal feature fusion. By modeling high-order relationships among agents’ data through a hypergraph, HGSTA effectively aligns and combines complementary features from different viewpoints. Extensive experiments on three benchmark datasets demonstrate that HGSTA significantly outperforms current state-of-the-art methods, validating the advantages of our hypergraph-based collaborative perception approach.

With the rapid advancement of artificial intelligence (AI), generative AI (GenAI) has emerged as a transformative tool, enabling customized and personalized AI-generated content (AIGC) services. However, GenAI models with billions of parameters require substantial memory capacity and computational power for deployment and execution, presenting significant challenges to resource-limited edge networks. In this paper, we address the joint model caching and resource allocation problem in GenAI-enabled wireless edge networks. Our objective is to balance the trade-off between delivering high-quality AIGC and minimizing the delay in AI GC service provisioning. To tackle this problem, we employ a deep deterministic policy gradient (DDPG)-based reinforcement learning approach, capable of efficiently determining optimal model caching and resource allocation decisions for AIGC services in response to user mobility and time-varying channel conditions. Numerical results demonstrate that DDPG achieves a higher model hit ratio and provides superior-quality, lower-latency AIGC services compared to other benchmark solutions.

ABSTRACT With the evolution of wireless communication technologies towards the sixth generation (6G) mobile communication system, the space‐air‐ground‐sea integrated network architecture has emerged as a critical development direction for achieving global seamless coverage. Focusing on the unmanned aerial vehicle (UAV)‐to‐ship maritime communication scenario within this network framework, a three‐dimensional (3D) geometry‐based stochastic model is proposed. The model adopts a combined structure of elliptical and cylindrical components to comprehensively characterize multipath propagation mechanisms, including line‐of‐sight, sea surface reflection, as well as single‐bounced and double‐bounced components. By introducing the wave equation of sea surface to establish the 3D motion trajectory model of the ship and integrating it with the 3D rotational motion model of the UAV, the time‐varying propagation distance‐induced channel non‐stationarity is accurately captured. Based on this model, key statistical characteristics such as the space‐time‐frequency correlation function (STF‐CF) and Doppler power spectral density are derived. Furthermore, the impacts of sea surface wind speed, UAV rotation, ship oscillation, and ship size on channel statistical properties and space‐time non‐stationarity are thoroughly analysed. These numerical results provide theoretical foundations for the design and performance optimization of UAV‐assisted communication systems in complex maritime environments.

Mobile Crowdsensing (MCS) is a promising paradigm where embedded sensor are exploited for collecting and sharing environmental data. In IoV, participating vehicles sense the environment, collect data and transmit the data to the edge server for processing to provide real-time services. However, real-time services not only pose energy challenges for massive data transmission and analysis, but also demand dynamic multi-timeslot optimisation for MCS systems. Additionally, sensing data often require continuous updates to prevent the provision of obsolete services. To address this, we introduce the concept of Urgency of Information (UoI) to characterize the freshness of sensing data. Diverging from the linear growth trend of Age of Information (AoI), UoI enables dynamic adjustment of the decay rate of data freshness based on traffic complexity. In this paper, we propose a dynamic multi-timeslot MCS system for IoV, under constraints of UoI, which intelligently leverages the spatial correlation of perception to update data and minimizes network energy consumption while ensuring compliance with UoI constraints. Then we propose a Joint Data Collection and Resource Allocation (JDCRA) algorithm to obtain the solution based on convex optimization. To the best of our knowledge, this is the first work to jointly optimize multi-road data collection and resource allocation, taking into account UoI metrics. We evaluate JDCRA by experiments on SUMO in real-world scenarios. Experimental results show that JDCRA outperforms state-of-the-art methods in terms of energy consumption and UoI violation probability, and obtains solutions that consumes only 7.43% more energy than the optimal solution in polynomial complexity.

Mobile edge computing technology has facilitated the deployment of high computational algorithms on Marine Internet of things (MIoT) devices that equipped with limited computing resources. However, the dynamic changes in the network environment, the strict requirements of system latency and energy consumption of mobile devices, restrict the task execution in MIoT. This paper proposes an offloading framework in a marine mobile edge computing scenario, which is assisted by sea buoys and unmanned aerial vehicles (UAVs). Aiming to achieve long-term system optimization goals under resource-constrained conditions, with task partitioning, user scheduling, and resource allocation joint optimization under the constraints of UAV residual battery life, system latency, and energy consumption. This paper specifically introduces a Network Partition Point Reservation Algorithm (NPPR) that reduces the solution space of the problem through preprocessing. Subsequently, the paper presents a multiagent deep deterministic policy gradient (MADDPG) algorithm, enhanced with standardization and adaptive learning rate decay (SA-MADDPG), to address task heterogeneity and environmental dynamics. Simulation results demonstrate that, compared to existing algorithms, the SA-MADDPG algorithm proposed in this paper reduces system latency and energy consumption by 34.7% and 61.2%, respectively.

The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions.

This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely,i)exhaustive searching,ii)unique index-based stochastic searching with risk-aware filter constraint,iii)geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.

The Vehicular Fog Computing (VFC) provides low-latency computing service to support emerging intelligent transportation applications in Internet of Vehicles (IoV). Multi-node cooperative VFC can utilize Connected and Autonomous Vehicles (CAVs) to implement cooperative intelligence. Due to the mobility of vehicles, service migration is necessary when task offloading service providers change. This paper proposes an Asynchronous Task Offloading Scheme (ATO-S) that allows each CAV to choose an independent optimization period and provides robust task offloading services under unknown vehicle mobility probability distribution. To the best of our knowledge, this is the first work to investigate asynchronous and robust multi-node cooperative task offloading in dynamic VFC-enhanced IoV scenarios. Furthermore, we formulate the long-term energy consumption minimization problem of VFC and transfer it into each time slot problem by Lyapunov optimization. Then we design Asynchronous Task Offloading Algorithm (ATO-A) to jointly optimizing CAVs matching, communication and computation resource allocation, and transmission power based on multiple mathematical techniques and hybrid heuristic algorithm. Extensive simulations based on real-world traffic scenario are conducted by varying multiple crucial parameters. Simulation results demonstrate the energy efficiency and task queue stability achieved by ATO-A, and service robustness achieved by ATO-S, in comparison with benchmark solutions.

Artificial Intelligence Internet of Things (AIoT) is an emerging technology within the Internet of Things (IoT), bringing an increasing demand for intelligent task offloading. Multi-node cooperative Vehicular Fog Computing (VFC) offers efficient and low-latency data processing to meet this requirement. However, due to the limited resource of fog nodes and latency-sensitive and computing-intensive task requirements, how to efficiently improve Quality of Service (QoS) and reduce energy consumption is an important issue in VFC. In this paper, we propose QECLO, a novel QoS-aware task offloading method. Unlike most previous studies, our goal is to improve QoS while minimizing energy consumption thus avoiding the overallocation of resource and high energy consumption caused by the one-sided pursuit of high QoS for few high-priority tasks. We solve the computation resource allocation subproblem by convex optimization and then optimize the communication resource and power allocation by an improved heuristic algorithm based on Decision Tree (DT). Moreover, we evaluate proposed approach through traffic scenario simulations. The experimental results show that our proposed approach outperforms existing methods in terms of energy consumption and latency.

Face anti-spoofing (FAS) is crucial for enhancing the security of face recognition systems. Although previous multi-frame face anti-spoofing methods utilized consecutive images for decision fusion, they lacked the extraction and detection of facial behavior, which is an essential attribute to distinguish real faces from presentation attacks (PAs). Besides, people tend to display unconscious facial behaviors at specific moments, other intervals are often redundant for classification. To address these issues, in this paper, we propose the frame-level-attention-based facial behavior mining for FAS (FBM-FA). An adaptive key-frame mining module is introduced to locate potential peak frames of facial behavior in a video. Moreover, a two-stream feature extraction network is designed to extract temporal features between facial behaviors and single facial behavior representations from position information. Comprehensive experiments are performed on four datasets including OULU-NPU, 3DMAD, CASIA-FASD, and REPLAY-ATTACK. Experiments demonstrate that our method can achieve perfect detection performance on 2D and 3D datasets.

Autonomous driving research has predominantly focused on LiDAR and vision-based methodologies. While LiDAR excels in accuracy and robustness, its high cost is prohibitive; vision-based systems, alternatively, are more economical but limited in scope and precision. To overcome these limitations, this paper presents a cross-modal scene recognition algorithm integrating semantic information to facilitate a seamless positional transformation between vision devices and LiDAR maps. The core objective is to enable precise initial localization within LiDAR point cloud maps, thereby establishing a consistent linkage between visual perception and spatial mapping. The algorithm utilizes a cross-modal interaction network to synergize features from both modalities, significantly narrowing the semantic gap. Further enhancing this framework, graph neural networks are employed to deepen the semantic understanding and improve alignment between disparate modal scenes. This method demonstrates remarkable efficiency in decoding complex environmental contexts and elevating match precision. Validated on the KITTI dataset, the algorithm achieved a commendable average F1 score of 0.815, affirming its value in advancing autonomous navigation systems with more accurate and reliable scene recognition capabilities.

In the development of Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), traditional management strategies that separate communication, sensing, and computation resources, as well as the uneven scheduling of resources, are increasingly unable to meet the needs of complex scenarios such as higher-level autonomous driving and integrated vehicle-road-cloud systems. This paper proposes a Quality of Service (QoS) tripartite evolutionary game model based on task-driven Integrated sensing, communication, and computation (ISCC). The dynamic evolution of resource allocation is analyzed by constructing a resource game platform centered on task requirements. Additionally, a joint utility function involving multi-node competition, dependency, and cooperation as QoS indicators has also been developed to achieve a unified representation of performance metrics. Subsequently, task-driven analysis effectively guides resource allocation strategies, ensuring optimization of resource utilization and precise alignment with task requirements. This paper offers new methodological support for designing and optimizing future intelligent transportation systems.

Active reconfigurable intelligent surface (RIS) acts as an enhancement of signal transmitted by the base station (BS) to overcome the effects of multiplicative fading and provide communication services for users. In this paper, we investigate an active RIS-assisted integrated sensing and communication (ISAC) system in which the BS senses the target in the presence of interference and establishes communication with multiple users assisted by the RIS. The optimization problem is formulated to jointly design the BS’s sensing transceiver beamforming, communication beamforming, and the RIS’s reflection coefficients to maximize the sensing SINR while satisfying the communication SINR requirements of users. An algorithm based on the ideas of quadratic transformation (QT), Karush-Kuhn-Tucker (KKT) conditions, semidefinite relaxation (SDR), and alternating optimization (AO) is designed to transform and solve the non-convex problem optimally. Simulation results verify the effectiveness and superiority of active RIS-assisted ISAC systems in improving sensing performance with communication SINR constraints.

Abstract Beam tracking is commonly employed in millimetre wave (mmWave) based vehicle‐to‐vehicle (V2V) networks to align the beams towards the intended targets and compensate for the path loss of mmWave signals. To mitigate the high latency issue arising from the tracking processes, integrated sensing and communication (ISAC) technology leverages the echo signal to sense the motion parameters of the target, achieving low‐latency beam tracking without requiring pilot and uplink feedback. Existing studies mainly focus on utilizing ISAC for beam alignment to track the target, without integrating beam tracking with resource allocation. In this paper, we propose the ISAC beam tracking based on deep reinforcement learning (IBTD) algorithm to address this problem. Specifically, we introduce the concept of packet age to measure communication performance. To achieve accurate beam tracking and optimize the transmit power, we integrate the sensing results, such as the position and velocity of the target vehicle, along with the buffer pool status information, with deep reinforcement learning (DRL) to select an appropriate policy. Furthermore, we consider the effect of inter‐vehicle distance and incorporate the changing of tracking targets into the DRL‐based policy. Simulation results demonstrate that the proposed IBTD algorithm achieves lower packet age and transmit power consumption compared to the baseline algorithms.

Marine Internet of Things (MIoT) has emerged as a prominent technology for the future development of marine applications, in which edge equipment provides a valuable method for information collection and processing on smart mobile devices (SMDs). However, the deployment of edge equipment may result in high latency due to inefficient computing offloading schemes. In this paper, we propose an optimal offloading scheme based on a dynamic unmanned air vehicle (UAV) assisted marine mobile edge computing (MMEC) environment in which latency-sensitive computing tasks can be partially offloaded autonomously. Specifically, we consider a time-varying scenario where the UAV hovers over multiple maritime mobile unmanned surface vessels (USVs) and provides MIoT services over communication periods. Our objective is to minimize the overall task execution time through joint optimization of user scheduling variables, UAV motion trajectory, and resource allocation while considering energy consumption and spatial constraints, thereby achieving enhanced quality of service. Considering the non-convexity of this optimization problem, we propose an advanced Twin Delayed Deep Deterministic policy gradient (ATD3) algorithm and examine the convergence and optimality of different parameter factors. Simulation results demonstrate that the proposed algorithm is superior to the baseline scheme regarding convergence speed, adaptability, and task execution time.

Vehicular Clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as Directed Acyclic Graphs (DAGs) consisting of interdependent vertices/subtasks and directed edges. However, efficient scheduling of DAG tasks over VCs presents significant challenges, mainly due to the dynamic service provisioning of vehicles within VCs and non-Euclidean representation of DAG tasks’ topologies. In this paper, we propose a Graph neural network-Augmented Deep Reinforcement Learning scheme (GA-DRL) for the timely scheduling of DAG tasks over dynamic VCs. In doing so, we first model the VC-assisted DAG task scheduling as a Markov decision process. We then adopt a multi-head Graph ATtention network (GAT) to extract the features of DAG subtasks. Our developed GAT enables a two-way aggregation of the topological information in a DAG task by simultaneously considering predecessors and successors of each subtask. We further introduce non-uniform DAG neighborhood sampling through codifying the scheduling priority of different subtasks, which makes our developed GAT generalizable to completely unseen DAG task topologies. Finally, we augment GAT into a double deep Q-network learning module to conduct subtask-to-vehicle assignment according to the extracted features of subtasks, while considering the dynamics and heterogeneity of the vehicles in VCs. Through simulating various DAG tasks under real-world movement traces of vehicles, we demonstrate that GA-DRL outperforms existing benchmarks in terms of DAG task completion time.

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing. We formulate an optimization problem to jointly optimize the mapping between task components and vehicles, and transmission powers of UAVs, while addressing the trade-off between i) completion time of tasks, ii) energy consumption of UAVs, and iii) data exchange cost among vehicles. We show that this problem is a mixed-integer non-linear programming, and thus NP-hard. We subsequently reveal that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology. Accordingly, we propose a decoupling approach by segregating template searching from transmission power allocation, where atemplatedenotes a mapping between task components and vehicles. For template search, we introduce a low-complexity algorithm for isomorphic subgraphs extraction. For power allocation, we develop an algorithm using$p$-norm and convex optimization techniques. Extensive simulations demonstrate that our approach outperforms baseline methods in various network settings.

Most existing face anti-spoofing methods get excellent results in the intra-dataset experiments. However, in the cross-dataset experiments, the performance may decline sharply due to the change of lighting condition, spoofing type, camera and other factors. To solve this issue, many researchers have proposed domain generalization methods for face anti-spoofing task. However, most approaches simply take full face image as input, thus the model may focus on a fixed area of the face. So these methods still have poor generalization ability to local spoofing attacks (as shown in Fig. 1). In this paper, we propose a dynamic attention based domain generalization method for face anti-spoofing. Specifically, the proposed method takes both of the full face and randomly selected local region as inputs. Moreover, we discover that the performance of the network changes with the position of local input. To handle this problem, we utilize the designed Gaussian function to fit the changing pattern of performance, and apply it into weighted cross-entropy loss function. Moreover, we introduce a Feature Decoupling Module (FDM) that can decouple features into domain-invariant and domain-specific features. In order to decouple correctly, we propose a Style Loss function to constrain the domain-invariant features of different samples aggregated, while domain-specific features separated. Extensive experiments are conducted to show the superior generalization ability of the proposed method.

Fog computing is an energy-efficient and cost-effective paradigm to help alleviate the pressure of resource-constrained mobile devices (MDs) running computation-intensive applications. In this article, we investigate the joint task partitioning and power control problem in a fog computing network with multiple MDs and fog devices (FDs), where each MD has to complete a periodic computation task under the constraints of delay and energy consumption. Each task can be partitioned into multiple subtasks and offloaded to the FDs according to the task partition strategy and transmission power strategy to reduce task execution delay and energy consumption. To this end, we present a multiagent deep deterministic policy gradient (MADDPG)-based task offloading algorithm for MDs to maximize the long-term system utility including the execution delay and energy consumption. Each MD inputs the local information, e.g., the task requirements, the available communication, and computation resources of the FDs, the computation resources, and the battery level of the MD into a distributed actor network to generate a task offloading policy, while a centralized critic network is used to update the weights of the actor networks to improve offloading performance. Numerical simulation results demonstrate the effectiveness of the proposed scheme in improving the system utility, reducing the average execution delay as well as the average energy consumption.

Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes.

In this paper, we investigate the multi-task federated learning in mobile edge computing (MEC) networks where a central server assigns different federated learning tasks to different MEC servers and select feasible clients to participate in the federated learning training process. The problem is formulated as a joint client selection and task assignment problem to maximize the total utility of all tasks, subject to the trained model quality and total training latency. Since the above-mentioned problem is NP-Hard, it poses challenges to obtain the optimal solution within polynomial time, the problem is transformed into a many-to-one-to-one 3D matching problem. To further reduce the computation while ensuring the matching stability, we first adopt the spectral clustering algorithm to cluster the clients into multiple client clusters. Then we reformulate the problem as a 3-Partite weighted hypergraph total weight maximization problem. Finally, we propose a greedy and local search (GLS) based algorithm to resolve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm as compared with baseline schemes.

Visual Simultaneous Localization and Mapping (SLAM) is widely employed in modern mobile service robots, which help robots to capture images indoors for estimating their pose. However, as part of visual SLAM, the typical visual odometry (VO) and visual-inertial odometry (VIO) systems only work in static environments. In many scenarios, they have to work in high-dynamic environments, which brings challenges to previous visual SLAM. In this paper, we proposed a novel monocular VIO for the challenging dynamic environments. Our method can make the robot locate accurately and robustly. Based on VINS-Mono, our system constructs a dynamic objects and feature points detection module. This module combines semantic object detection, multi-sensor-aided, and the geometric 3D vision constraints to remove the dynamic feature points. According to our experiments, the results demonstrate our system outperforms SOTA monocular VIO systems in accuracy and robustness, especially in high-dynamic indoor environments.

Driven by the tremendous in vehicular networks computation-intensive application demands, the incorporation of mobile edge computing (MEC) and vehicular cloud is convinced as a promising paradigm to fulfill computation offloading requirements. However, the changing vehicular communication topology (CVCT) poses a significant challenge for offloading directed acyclic graph (DAG) model application. Due to the precedence and connection constraint between different sub-jobs, the successful offloading of DAG-enabled apllication will be disturbed even interrupted without considering CVCT. To address this problem, we propose a topology-aware dynamic computaion offloading mechanism and adopt simulated annealing algorithm (TASA) to jointly optimize the energy consumption and completion time under dynamic environment, while guaranteeing the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method in overcoming CVCT’s influence.

Internet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.

Mobile edge computing (MEC) has emerged as one of the key technical aspects of the fifth-generation (5G) networks. The integration of MEC with resource-constrained unmanned aerial vehicles (UAVs) greatly enables flexible resource provisioning for supporting dynamic and computation-intensive UAV applications. Existing resource trading could facilitate this paradigm with proper incentives, which, however, may often incur unexpected negotiation latency and energy consumption, trading failures and unfair pricing, due to the unpredictable nature of the resource trading process. Motivated by these challenges, an efficient futures-enabled resource trading mechanism for edge computing-assisted UAV network is proposed, where a mutually beneficial and risk-tolerable forward contract is devised to promote resource trading between an MEC server (seller) and a UAV (buyer) with multiple tasks. Two key problems i.e. futures contract design before trading, and transmission power optimization during trading are studied. By analyzing historical statistics associated with future resource supply, demand, and air-to-ground communication quality, the contract design is formulated as a multi-objective optimization problem aiming to maximize both the seller’s and the buyer’s expected utilities, while estimating their acceptable risk tolerance. Accordingly, we propose an efficient bilateral negotiation scheme to help players reach a trading consensus on the amount of resources and the relevant price. For the power optimization problem, we develop a practical algorithm that enables the buyer to determine its optimal transmission power via convex optimization techniques. Comprehensive simulations demonstrate that the proposed mechanism offers mutually beneficial utilities to players, while achieving commendable performance on trading failures and fairness, negotiation latency and cost, comparing with baseline methods.

Mobile edge computing (MEC) enabled user-centric ultra-dense network (UDN) is a promising solution to the energy constrained mobile users with delay-sensitive and computation intensive applications. Due to the high density of access points and MEC servers in UDN, both task offloading decision, power control, communication and computation resource allocation need to be addressed. To this end, we consider the joint problem of task offloading, uplink transmission power control, communication and computation resource allocation in a UDN, where the task of each user can be partitioned into several subtasks and offloaded to different access points. To handle the continuous action space of task partitioning and power control, we propose a multi-agent deep deterministic policy gradient (MADDPG) approach to solve this problem. Simulation results reveal the effectiveness of the proposed method.

Vehicular cloud computing has emerged as a promising paradigm for fulfilling user requirements in computation-intensive tasks in modern driving environments. In this paper, a novel framework of multi-task offloading over vehicular clouds (VCs) is introduced where tasks and VCs along with their internal connections are modeled as undirected weighted graphs. Aiming to achieve a trade-off between minimizing task completion time and data exchange costs, task components are efficiently mapped to available virtual machines in the related VCs. The problem is formulated as a non-linear integer programming problem, mainly under constraints of limited contact between vehicles as well as available resources, and addressed considering different problem sizes. In small size scenarios with a couple of tasks and service providers in a VC, we determine optimal solutions; in larger size cases, a connection-restricted random-matching-based subgraph isomorphism algorithm is proposed that presents low computational complexity. Evaluation of the proposed algorithms against greedy-based baseline methods is conducted via extensive simulations.

With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds (VCs) formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy onboard workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for VCs that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over VCs is formulated as a nonlinear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize the job completion time and data exchange cost. The problem is addressed for two scenarios: 1) low-traffic and 2) rush-hour scenarios. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree-based subgraph isomorphism extraction. The evaluation of the performance of both optimal and proposed randomized algorithms with two greedy-based baseline methods is carried out through extensive simulations.

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey-Clarke-Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.

In cognitive radio networks, cooperative spectrum sensing(SS) between cognitive users can improve the detection performance, reduce the testing time. However, a large amount of data interaction restrict its application and increase the consumption of resources. For the demand of cooperative SS, this paper improved the compression algorithm based energy detection which in the early stage of the work, the spectrum data is divided into segments of different characteristic and respectively compressed. The presented algorithm further improved the compression ratio compared to the original algorithm. Verified by the experiment of satellite signals, the experimental results show that the compression performance of this algorithm will be increased several times compared to JPEG, JPEG2000 and detection-based compression algorithm.

Device-to-device (D2D) communication, which is defined as a direct communication between two mobile users without traversing the Base Station (BS) or the core network to offload the increasing traffic to the user equipments, is one of the key technologies in the fifth generation (5G) of wireless communication systems. Discovery and communication are the basic two features to fulfill the need for the D2D communication. However, Most of existing D2D studies focused on the communication issues always assume that the discovery is completed. In this paper, we propose two strategies of device discovery and access scheme for the 5G cellular networks. Then the performance analysis based on two dimensional discrete time Markov process model is provided. In addition, we present numerical simulation on the Matlab platform. The simulation results demonstrate the viability of the proposed scheme.

Abstract A large number of new data‐consuming applications are emerging, and many of them involve mobile users. In the next generation of wireless communication systems, device‐to‐device (D2D) communication is introduced as a new paradigm to offload the increasing traffic to the user equipment. Before the traffic transmission, D2D discovery and access procedure is the first important step which needs to be completed. In this paper, our goal is to design a device discovery and access scheme for the fifth generation cellular networks. We first present two types of device discovery and access procedures. Then we provide performance analysis based on the Markov process model. In addition, we present numerical simulation on the Vienna Matlab platform. The simulation results demonstrate the viability of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.