Collaborative content dissemination enables vehicles to directly access content from surrounding nodes through Vehicle-to-Everything (V2X) technologies, such as Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. This approach significantly alleviates the downlink traffic burden on cellular network base stations caused by repeated downloading of popular content while addressing security challenges in electric vehicle (EV) charging operations, such as payment fraud and data tampering. A key yet unresolved challenge in content dissemination and EV charging is incentivizing vehicles to participate in collaborative processes voluntarily. Existing incentive mechanisms mainly rely on centralized architectures, which are vulnerable to single-point attacks and trust issues in third-party platforms. To overcome these limitations, we propose the PCD-DB (Popular Content Dissemination using DAG-based Blockchain) scheme, which uses a Directed Acyclic Graph (DAG)-based blockchain to incentivize V2X collaboration for dual applications: improving content dissemination efficiency and ensuring secure EV charging transactions. Our novel framework establishes a decentralized incentive system where vehicles act as content propagators or charging service providers, depending on their service capabilities. We define the propagation and charging capabilities of vehicles and use contract theory to design hierarchical contracts tailored to heterogeneous vehicle roles. Numerical results show that our decentralized incentive mechanism significantly improves the efficiency and profitability of vehicle content dissemination while ensuring the security of electric vehicle charging transactions, outperforming existing benchmark methods.

Effective power line inspection is crucial for power grid maintenance and management. To address the issue of signal loss or signal weakness in remote suburbs and deep mountains, we presented a UAV patrolling system with hybrid communication modules and edge detection capabilities. This system first features the communication ability in different scenarios including the extreme signal loss case by incorporating the BeiDou Navigation Satellite System, short-message communication, the Internet of Things, and edge computing. Next, to accurately and timely detect the power line faults, such as exposed wire, thatch covering, and lead stem falling, under various conditions, an edge detection model employing YOLOv8 is proposed without the help of cloud centers and public communication networks. Finally, experiments are designed on our built UAV patrolling test bed with different use cases. Numerical results show that our proposed scheme could efficiently inspect the power line, especially in signal loss or weak areas, and have a high precision and low latency for line fault detection, compared to the YOLOv8 baseline algorithm.

With the rapid progress made in aerial computing technology and the increased popularity of fifth-generation (5G) networks, uncrewed aerial vehicles (UAVs) have been playing a crucial role in real-time data collection, processing, and transmission. However, due to the diversity in traffic generated by UAVs in various mission scenarios, there is a significant challenge posed in traffic classification. Therefore, a novel traffic classification model is proposed in this article on the basis of the spatial attention-enhanced convolutional neural network (SAE-CNN). This model proves effective in improving classification accuracy and latency, particularly in the context of various 5G services, such as enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and Internet service. Also, a 5G heterogeneous network platform is built to collect UAV-related aerial computing data, with extensive experiments performed to verify the superior performance of the SAE-CNN model compared to other state-of-the-art methods. The experimental results demonstrate that the proposed approach enables effective traffic management and classification for the application of UAV in complex 5G environments.

As a distributed embedded system, vehicular edge computing (VEC) completes various complex Deep neural network (DNN) tasks through network collaboration and communication. However,due to the limited computing power of vehicle processors, vehicles cannot handle increasingly complex DNN tasks. To accurately estimate the execution latency of each layer across different DNN models on heterogeneous devices, we proposed the Extreme Gradient Boosting Tree (XGBoost) algorithm to predict DNN task inference latency. Furthermore, we proposed partitioning and offloading algorithms for both chained DNN tasks and Directed Acyclic Graph (DAG)-type DNN tasks, addressing their unique computational characteristics. For chained DNN tasks, we employ a linear search to determine optimal partitioning points based on predictions from the DNN latency prediction model. For the partitioning and offloading of DAG-type DNN tasks, we construct it as a minimum cut problem under the network flow graph and propose a DNN task partitioning and offloading algorithm based on the highest label pre-stream push (HLPP) algorithm to effectively reduce the cost of task partitioning and offloading. Finally, we used an experimental vehicle equipped with Raspberry and a RSU equipped with Jetson Nano to verify the results. The experiment shows that the DNN latency prediction model based on the XGBoost we proposed can effectively improve the latency prediction accuracy of DNN layer-by-layer execution. At the same time, the division and offloading algorithms for different types of DNN inference tasks can achieve higher task completion rate, lower latency, and lower energy consumption.

In remote or disaster areas, where terrestrial networks are difficult to cover and Terrestrial Edge Computing (TEC) infrastructures are unavailable, solving the computation computational offloading for Internet of Vehicles (IoV) scenarios is challenging. Current terrestrial networks have high data rates, great connectivity, and low delay, but global coverage is limited. Space–Air–Ground Integrated Networks (SAGIN) can improve the coverage limitations of terrestrial networks and enhance disaster resistance. However, the rising complexity and heterogeneity of networks make it difficult to find a robust and intelligent computational offload strategy. Therefore, joint scheduling of space, air, and ground resources is needed to meet the growing demand for services. In light of this, we propose an integrated network framework for Space-Air Auxiliary Vehicle Computation (SA-AVC) and build a system model to support various IoV services in remote areas. Our model aims to maximize delay and fair utility and increase the utilization of satellites and Autonomous aerial vehicles (AAVs). To this end, we propose a Deep Reinforcement Learning algorithm to achieve real-time computational computational offloading decisions. We utilize the Rank-based Prioritization method in Prioritized Experience Replay (PER) to optimize our algorithm. We designed simulation experiments for validation and the results show that our proposed algorithm reduces the average system delay by 17.84%, 58.09%, and 58.32%, and the average variance of the task completion delay will be reduced by 29.41%, 48.74%, and 49.58% compared to the Deep Q Network (DQN), Q-learning and RandomChoose algorithms.

The dangerous driving action seriously affects traffic safety and may cause severe road disasters. Dangerous driving action recognition in Internet of Vehicles (IOV) has been widely exploited to reduce traffic accident risks by discovering and then transmitting the recognition results to autonomous machines or other vehicles. General action recognition models using deep learning networks usually have a large number of parameters and require a significant amount of memory and computational power, which cannot meet the lightweight and real-time requirements of action recognition models deployed on resource-limited onboard devices, e.g., vehicles. As a result, we introduce the broad learning system (BLS) into onboard dangerous driving action recognition tasks, classify actions based on skeleton data, and design the graph convolutional representation (GCR) algorithm and graph convolutional broad learning system (GCBLS) classification model to speed up the recognition process. We verified through ablation experiments that the GCR algorithm can effectively represent skeleton data and improve classification accuracy datasets. In addition, comparative experiments in State Farm and Driver Skeleton datasets show that the GCBLS model has the characteristics of lightweight, real-time, and high accuracy. We also designed a workflow for "noise" caused by poor pose estimations in practical applications, then deployed the proposed workflow on onboard devices, which can run at speeds above 27 FPS with high accuracy, which proves the effectiveness and practicability of our proposed algorithms and models for IOV.

Unknown industrial control protocols (ICPs) seriously hamper the device intercommunication and security analysis of the Industrial Internet of Things due to the absence of public specification information. Protocol reverse analysis has emerged as a promising technology to infer their specifications, where the primary step is to extract protocol fields by locating their boundaries in the network packet. Previous works leverage various algorithms, such as sequence alignment, keyword mining, and statistic analysis for field extraction. However, they have limitations in excavating the unique features of ICP fields, leading to inaccuracies in boundary localization. To address this problem, we propose an entropy-based field segmentation method for unknown ICPs. After stacking protocol packets vertically, we calculate the information entropy and information gain ratio of data values at each location in the packet. By analyzing the distribution variations of these entropy features in diverse ICP fields, we derive multiple packet segmentation rules to locate the field boundaries. Extensive comparative experiments demonstrate the superiority of our method for ICP field extraction.

Satellite Terrestrial Networks (STN) are known to enhance the quality of service and to provide a better user experience. However, STNs are primarily utilized as wide-range relays and are characterized by a lack of effective intersatellite collaboration. The communication efficiency and quality of near real-time remote sensing tasks in 6G space-air-ground integrated networks are enhanced by the proposed Orbital Edge Computing-Collaborative Offloading Scheme (OEC-COS), which utilizes Service Function Chains (SFC) and the processing and collaboration capabilities of satellite nodes to allocate near real-time remote sensing tasks to optimal satellite nodes for execution. The remote sensing task offloading problem has been formulated as a delay minimization problem, and the advantages of the OEC-COS scheme in terms of computational resource utilization ratio and latency are validated through simulation experiments by comparing it with average orbit allocation and co-orbit allocation schemes. The proposed OEC-COS scheme achieves the lowest average task computing delay among these methods.

This paper proposes multipath routing for LEO satellite networks with a priority path and improved Bhandari mechanism (PP-IBM). PP-IBM optimizes the Bhandari algorithm and considers delay, jitter, and packet loss rate, introducing the priority mechanism and redefining the priority function. Simulation results show that compared with the other three algorithms, discrete-time dynamic virtual topology routing (DT-DVTR), optimized DT-DVTR, and time-based graph management (TGM), PP-IBM generates a minimum reduction of about 41% in routing overhead, the packet loss rate is only about 0.02%, and the jitter is as low as about 0.1ms.

Metaverse is a visually rich virtual space in which users can interact with each other. By introducing metaverse into vehicular networks, vehicular metaverse can provide users real-time immersive experiences based on augmented technologies. Vehicular edge computing is a desirable approach to support computation-intensive vehicular metaverse services by network resource collaboration. User collaboration needs to reach a consensus on perception information, operation control and so on to realize user autonomy. However, the existing consensus algorithms often require computational proof or frequent communication, making them unsuitable for dynamically changing vehicular edge metaverse with low latency and energy restrictions. In this paper, we have proposed a reputation model maintained in the vehicular edge metaverse to score the vehicles, so the vehicles with a high reputation can be selected to participate in practical Byzantine fault tolerant (PBFT) consensus, which improves the probability of success and credibility of consensus without increasing the number of participating vehicles. Meanwhile, an optimization problem is formulated for each vehicle to allocate its computation and communication resources to reach a PBFT consensus. Also, the optimized communication time interval of each phase in the PBFT consensus can be used as a reference for setting the agreed upper time, which reduces the waiting time of vehicles and the probability of re-consensus. Simulation results have demonstrated that the proposed scheme effectively achieves PBFT information consensus with lower latency and energy consumption, and thus is more scalable and efficient.

Common feature representation in optical and synthetic aperture radar (SAR) image registration is one of the most challenging tasks due to the significant geometric and radiometric differences. This letter proposed aProminent structure-guided feature (PSGF)representation for SAR and optical image registration. Firstly, the prominent structure of the image is highlighted based on windowed inherent variations, which is conducive to identifying more accurate and reliable corresponding points. Secondly, the maximum response index filter banks are proposed to extract structure features with multi-orientation convolution results. Then the structure feature-guided representation generated from this filtering map is quantized in histograms. Finally, the descriptor with radiation invariance is employed for feature matching, enabling automatic image registration with high accuracy. Comparative analysis with state-of-the-art methods on diverse terrain data demonstrates the superiority of the proposed PSGF method for SAR and optical image registration.

Although collaborative 3D perception has successfully improved detection performance by sharing LIDAR information among multiple agents, its impact under adverse weather is under poor investigation. It is non-trivial to reduce the noise effect in the multi-agent system, as each agent may generate defective feature representations with aleatoric uncertainty, and such uncertainty will be further amplified in the collaborative stage due to deterministic collaboration models. To mitigate the negative effects of weather noise on the collaborative framework, we proposed a method called Co-Denoising, which incorporates a two-stage denoising approach within the intermediate collaborative framework. In our method, a sampling-based noise filtering is first performed at each agent to make a coarse denoising. Then, during the collaboration stage, the global feature representations are expanded through Bayesian neural networks to improve the robustness against environmental noise. The extensive experiments on sunny and rainy datasets have indicated the proposed collaborative perception method can significantly reduce performance degradation under adverse weather.

The continuous evolution of cellular networks has resulted in the rapid increase in both mobile applications and devices in the Internet of Vehicles. The introduction of the multi-access edge computing method makes it possible for vehicles in remote areas to offload their computational tasks, which can effectively relieve the computing pressure of local devices and reduce the computational delay as well. Tasks offloading for multi-user is a resource competition problem, especially in dynamic environments, which is difficult to be solved by traditional algorithms. In this article, we propose a two-layer hybrid system with local and edge computing, providing convenient computing and offloading services for vehicle users in dual dynamic scenarios of task generation and vehicle mobility. The delay and queuing situations are considered comprehensively in the formulated optimization problem, which can be solved by the proposed deep deterministic policy gradient-based computation offloading algorithm. The offloading process of the vehicle tasks in dynamic scenarios is transformed into a Markov decision process to obtain the offloading strategy. Simulation results demonstrate the performance advantages of two-tier computing architecture. Compared with random offloading, deep Q network-based offloading, and local computing, the algorithm proposed in this article gains the highest average reward of tasks. Besides that, numerical results also prove that our algorithm has the lowest average delay under different computing capabilities of edge servers.

Autonomous vehicle platooning has tremendous potential to relieve the burden of Vehicular Edge Computing (VEC) by sharing resources with nearby vehicles. Therefore, fleet formation and resource orchestration within vehicle platoons have recently ignited significant research interest. However, most fleet formation works focus on the intra-platoon configuration and information exchange, but few consider trajectory matching and joining willingness. Likewise, in multi-platoon scenarios, static resource orchestration for a single platoon no longer meets the demand from dynamic resource scheduling. To tackle these problems, we proposed the SFO scheme, an adaptive taskScheduling based on incentive fleetFormation and metrizable resourceOrchestration. First, we design a fleetFormation algorithm based onTrajectory matching andJoining willingness (FTJ) to ensure the stable underlying architecture. Second, we use theWeightedSum ofEnergyConsumption (WSEC) as the performance metric for resource orchestration and formulate the time-average WSEC minimization problem. Third, anAdaptive taskScheduling underPartitionableApplications and variableResources (ASPAR) is proposed for an asymptotic optimal solution in reaction to the changeable backlog of the timeout queue. Finally, our numerical results demonstrate that our approach is superior to other latest and classic works in energy consumption and execution latency.

In existing research on cluster head selection schemes in the Internet of Vehicles (IoV), designing a stable cluster structure poses a significant challenge. Choosing a centrally-located cluster head that can respond rapidly is crucial for meeting various requirements. To address the aforementioned challenges, this paper introduces a machine learning-based IoV cluster head selection scheme (HSCS). We introduce a new metric termed N-cycle Average Virtual Cluster Delay (XTn) for appropriate cluster head selection. To accommodate the high dynamism of vehicles, a machine learning model is integrated to predict cluster head selection metrics across different periods, and a set of cluster head selection guidelines is formulated. Experimental results demonstrate that our proposed HSCS ensures a relatively low average intra-cluster delay while maintaining a longer cluster head retention time, and it exhibits commendable robustness.

Federated learning has been combined with graph learning for modeling spatial-temporal data while maintaining data confidentiality and safety. However, there are still several issues: 1) In practical usage, some clients may be unable to participate in the model inference due to poor network signal, malicious attacks, etc. 2) In the communication process, the uploaded information is easily disturbed by noise. The performance of the graph model will be seriously affected by its low robustness. Additionally, the assumption of identical distribution between the training and testing domain does not hold in practical scenarios, resulting in overfitting and poor generalization ability of the trained models. 3) The relations that exist among clients may change dynamically over time and manually constructing the graph structure of clients may not accurately represent the relations among clients. In this paper, we address all the above limitations by proposing a robust hierarchical split-federated graph model named DCSFG. Specifically, DCSFG combines split-federated learning and spatial-temporal graph model to better capture the spatial-temporal dependencies. We propose a Dropclient method and introduce the uncertainty estimation to enhance the robustness and generlization ability of the model. We also design a dual-sub-decoders structure for clients so that they can perform predictions locally and independently when they are unable to participate in the inference process. A novel hierarchical graph message passing structure is proposed to enable each client to perceive the global and local information. The extensive experimental results demonstrate the effectiveness of DCSFG.

Utilizing the stable underlying and cloud-native functions of vehicle platoons allows for flexible resource provisioning in environments with limited infrastructure, particularly for dynamic and compute-intensive applications. To maximize this potential, we propose the creation of a trading market to encourage interactions between service supporters (vehicle platoons) and requesters (task vehicles). Current trading decisions based on game and negotiations can lead to unpredicted handover costs and increased communication overhead in dynamic environments. Moreover, existing research tends to overlook a mutually beneficial trading philosophy by focusing on either the service supporters’ profitability or the user experience of resource-restrained requesters. Addressing these issues, we introduce a multi-objective optimization problem to model environmental dynamics and uncertainty, aiming to maximize both platoons’ and task vehicles’ long-term utilities while maintaining a satisfactory service access ratio. To tackle the problem within acceptable time frames, we develop a global-local training architecture, incorporating a hybrid action space and prioritized sampling into a multi-agent reinforcement learning algorithm that utilizes a twin delayed deep deterministic gradient (GL-HPMATD3). This approach facilitates consensus in the trading market on key issues, including service request selection, resource allocation, and trading pricing. Through extensive experimentation and comparison, we demonstrate our mechanism’s superior performance in convergence, service access ratio, player utility, execution latency, and trading pricing relative to several state-of-the-art and baseline methods.

The low earth orbit (LEO) satellite constellation is regarded as an effective complement to the terrestrial communication system due to its seamless coverage and ultra-low latency. Unfortunately, the highly dynamic traffic volume, as well as the inherent nature of dynamic topology changes caused by frequent link handover and uncertain hardware failures, pose severe challenges in the design of reliable routing. However, most existing reliable routing approaches with distributed schemes only focus on information exchange between adjacent nodes, which makes them fail to perceive real-time global network changes and make optimal decisions. In this paper, we propose a software defined networking (SDN) based intelligent satellite routing (SISR) method to increase the adaptivity and reliability during the packet transmission process. With the facilitation of SDN, we manage the network in a hierarchical and centralized paradigm, and further implement a more refined form of reinforcement learning (RL) to enhance the fault-tolerant ability of satellite network routing. Experimental results show that our solution can reduce latency and packet loss ratio by more than 42% and 29% compared to baselines.

Massive flood damage has garnered a lot of social attention. Due to the tension between the strong demand for generalized models and the constrained capabilities of edge devices for hydrological surveillance, this study proposes an accessible distributed hydrological surveillance (HS) and computing system with integrated end-edge-cloud (iEEC) architecture to address the issue. In order to increase the inference efficiency of the edge servers (ES), we first develop a HS model with multiple exits, aiming to exploit its network structure and inference strategy. Then, using a collaborative scheduling algorithm, we construct the iEEC pathway to decide whether to undertake edge inference or cloud invocation. With a prototype system and a simulation tool, we eventually performed a numerical analysis of the system at various scales. The accuracy reached 94.3 %, the speed reached 30.3 frames per second (FPS), and it can better handle the occurrence of hard instances compared to state-of-the-art (SOTA) approaches.

Flood damage is a devastating natural disaster that requires effective hydro-surveillance (HS) systems. However, the limited capabilities of edge servers (ES) make it challenging to develop such systems. Our study proposes an accessible, distributed HS and computing system to address flood damage. To increase inference efficiency on ES, we develop a HS model and combine it with a collaborative scheduling algorithm to construct an integrated end-edge-cloud (iEEC) computing pathway. Our system achieves 94.3% accuracy and a speed of 30.3 frames per second (FPS), outperforming state-of-the-art (SOTA) approaches, and can handle hard instances. The prototype system and simulation tool demonstrate the effectiveness of our approach at various scales.

As a new network technology, mobile-edge computing (MEC) combined with the Internet of Vehicles (IoV) can effectively improve the efficiency of task computing and offloading. However, the power of edge computing will be severely limited to the areas with poor MEC server coverage. Furthermore, there are a number of peripheral vehicles with temporarily idle computing resources on the road, so how to put the resources of these vehicles into use becomes the primary issue to be considered. In this article, a distributed multihop task offloading decision model for task execution efficiency is developed, which mainly consists of two parts: 1) a candidate vehicle selection mechanism for screening the neighboring vehicles that can participate in offloading and 2) a task offloading decision algorithm for obtaining the task offloading solution. Considering the impact of different hop and wireless communication ranges on communication ranges on task completion in a generic scenario, we introduce the hop count$k$and select the neighboring vehicles in the$k$-hop wireless communication range as the candidate vehicles. Then, the problem of offloading is modeled as a generalized allocation model with constraints which is solved by the greedy algorithm and discrete bat algorithm, respectively. The results show that compared with the scheme in which the task vehicle randomly selects the neighboring vehicles to offload and the scheme that all tasks are completed locally, the offloading scheme in which all tasks are completed under the greedy algorithm or bat-based algorithm has advantages in time delay performance in terms of different task number, task required computation power, and task size environment. Besides, this article also explores the influence of hop count$k$on the results when selecting candidate vehicles from the neighboring vehicles within the range of$k$hop. The results show that the increase of$k$will also increase the number of candidate vehicles, which makes the time delay lower. Under the parameters set in this article, the time delay required for the greedy algorithm offloading scheme to complete all tasks is a lower bound on the time delay of the bat algorithm scheme. The greedy algorithm scheme reduces latency by 0.2–2.4 s compared to the scheme where tasks are all completed locally, and it reduces latency by 0.16–2.3 s compared to the random offloading scheme.

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.

With the increasing on-board demand for intelligent connected vehicles (ICVs), the fifth-generation (5G) wireless systems are being massively utilized in vehicular networks. As an essential component, content retrieval in the ICV provides a basis for vehicle-to-vehicle or vehicle-to-infrastructure data interaction for many applications. However, content access is still subject to performance degradation due to congested communication channels, diverse requests patterns, and intermittent network connectivity. To mitigate these issues, in-network caching in 5G-enabled ICV has been leveraged to benefit content access by allowing edge nodes to store content for data generators. In this paper, we propose an in-network caching scheme to support various provisions of data sharing in the ICVs by exploring the advantages of information-centric networks (ICN). We first divide each on-board service into several content units. Then, we place these units at the ICV and small cell base stations (SBSs) to reduce the content retrieval delay, further model the proposed system as an integer nonlinear program (INLP) and attain the optimal QoE (Quality of Experience) by placing content units at appropriate cache entities. Finally, we verify the effectiveness and correctness of our proposed model through extensive simulations.

The autonomous vehicle platoon has the potential to cope with the stress caused by the resource-constrained vehicles‘ demand for processing power and the spread-out deployment of MEC-BS. In this poster, we focus on a multi-platoons scenario for task scheduling. Our objective is to minimize the overall energy consumption subject to the long-term latency constraint. To characterize stochastic properties and deal with coupling between variables, we propose a dynamic task scheduling algorithm based on Lyapunov optimization (LDTS). We theoretically and empirically evaluate the performance of the proposed algorithm, which is illustrated to be significantly better than state-of-the-art and other benchmark approaches in terms of execution latency and energy consumption.

Accurate annotation of construction disturbance outline greatly influences the evaluation quality in interpretation application. While the loss functions used in semantic segmentation nowadays are difficult to reflect the contour information. Therefore, this paper proposes a RA U-Net(Residual Attention U-Net) construction disturbance interpretation. The model uses an exactly defined Contour Loss(CL) as the loss function. This function could reflect the local segmentation information of each element by extracting and transferring contour features into the weight matrix. Moreover, this paper modifies the CL function to make it suitable for construction disturbance interpretation. The experiments on M-nih Massachusetts Building Dataset and Construction Disturbance Dataset labeled manually show the capability of the proposed function in remote sensing interpretation. The proposed model has been used for the Soil and Water Conservation project supervised by the local government. And the IoU increased 1.85% maximum than binary cross-entropy(BCE) on Mnih Massachusetts Building Dataset.

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme.

Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.

In recent years, vehicular named data networking (VNDN) has quickly ascended to the spotlight and gained enormous popularity, which has emerged as a candidate to support various applications of vehicular communications. VNDN has the potential improve the data dissemination efficiency by mitigating the performance degradation from Internet Protocol (IP) addressing, unstable connectivity and diversified service requirements. With the number of connected vehicles increasing rapidly, the traffic burden of the base station (BS) also grows. As an effective edge computing paradigm, in-vehicle caching can significantly relieve the pressure of the BS. However, the design of a fair caching strategy is still challenging due to the selfish nature of individuals. In this paper, to address the above issues, a popularity-incentive caching scheme (PICS) is proposed in VNDN, where the BS will reward vehicles who execute cache offloading and content sharing with others. To balance the conflict of interest between the BS and vehicles, a Stackelberg game is modeled with rational utilities envisioned. Next, we propose the solution of this game model and evaluate the influence of different weight parameters. Finally, simulation results validate the effectiveness of PICS.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider’s and users’ perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider’s cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

The rapid advancement of intelligent vehicles is deemed crucial to the emergence of diverse compute-intensive applications of assisted driving, which consist of automatic driving, speed recognition, hybrid sensing data fusion, etc. Nevertheless, resources-constraint vehicles with high mobility cannot always meet the computing and communication demands when the above applications occur. Additionally, considering the expensive and inflexible deployment of edge servers, offloading application tasks to “Edge” in the vehicular networks is not always working well. To effectively mitigate the above issues, the complicated application tasks are motivated to offload to the vehicle platoon, where the vehicles travel synchronously in a string with small headway. Benefiting from the stable connectivity, adjustable mobility, and reasonable charge, the task vehicle would like to process the task by leveraging the idle resources of each platoon member (PM). To make more effective use of the resources on the mobile edge platoon cloud (MEPC), we investigate the resource allocation strategy based on the task vehicle’s service pricing strategy in this work. We first formulate the interactions between MEPC and task vehicle as a Stackelberg game to study the joint utility maximization of the MEPC and task vehicle. Then the Stackelberg Equilibrium (SE) for the proposed game is characterized and proved. The proposed algorithm Hook-Jeeves-based Stackelberg game (HJSG) can reach the SE. Finally, we introduce the consortium blockchain to ensure the security and privacy of service transactions. The entire system helps enhance task processing efficiency, protect transaction data, and improve service experience. Experimental results over numerical simulation based on practical scenarios demonstrate that compared with Multi-round Stackelberg Game (MRSG), uniform pricing, and the local computation strategy, the proposed HJSG algorithm can attain less execution time and faster convergence performance.

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

With the rapid development of intelligent transportation, various computation-intensive applications have e-merged to improve the safety, efficiency, and comfort on the road. However, due to the mobility and resource dynamics, it is still a challenge for the resource-constrained vehicles to timely process computation-intensive tasks. Fortunately, the computation offloading in the Internet of Vehicles (IoV) greatly eases the contradiction between resource constraints and computing requirements. In this paper, we first present a collaborative computing architecture based on Edge-Cloud (EC) and Mobile-Edge-Platooning-Cloud (MEPC). Then, considering the priority of the Delay-Sensitive Tasks (DSTs), preemptive scheduling is introduced to deal with the hybrid tasks, comprised of DSTs and Delay-Tolerant Tasks (DTTs). Finally, a computation offloading problem based on the collaborative EC-MEPC architecture is established by jointly optimizing the decision-making and resource allocation issue. To solve the above problem, a distributed computation offloading and resource allocation algorithm is designed to achieve the optimal solution. Simulation results show that the proposed collaborative computing architecture and the distributed algorithm can effectively improve the delay and energy consumption performance of this system.

Semantic segmentation could not distinguish the spot's contour, which has both the construction disturbance region and original physiognomy. This paper proposed a semantic segmentation network named Residual Attention U-Net (RA U-Net) in the appliance of construction disturbance interpretation. With Inception-v3 as the backbone, the proposed model used the residual attention module replaced the skip connection in U-Net and Conditional Random Field (CRF) as the post-processing. Regarding natural landform as noise, the residual attention module could retain the natural landform. Then, CRF was used to fine-grained outline. The experiment on Standford Background Dataset proved the capability of the proposed model in semantic segmentation. It shows a good performance in the construction disturbance interpretation dataset labelled by ourselves. Moreover, it has been used for the Soil and Water Conservation project held by the local government.

Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices.

Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.

As the application of the industrial Internet of Things (IIoT) becomes more widespread, the IIoT is being combined with social networks. Nodes in the network can be users, machines, and so on. Using the sensing detection technology of the IIoT, industrial machines can realize real-time informatization, which is convenient for users to perform remote management. Nodes can communicate with each other and make ratings. These ratings can be modeled as directed weighted edges between nodes and form directed weighted networks (DWNs). The edge weight represents the “strength” of relationship and the direction of edge points from the edge generator to the edge receiver. Predicting edge weights in DWNs is critical to predicting unknown ratings or recovering lost data. In this article, we propose a directed edge weight prediction model (DEWP) using decision tree ensembles. It extends the local similarity indices to DWNs and extracts a series of similarity indices between nodes as features of each edge. These features are used to construct a blended regression model of random forest, gradient boost decision tree, extreme gradient boosting, and light gradient boosting machine. The proposed algorithm was evaluated experimentally with the Bitcoin OTC and Bitcoin Alpha datasets by removing 10% to 90% of edges in the original network. Compared with other classical algorithms, DEWP has higher prediction accuracy and robustness.

An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.

In Internet of Vehicles (IoV), accurate traffic flow prediction is helpful for analyzing road condition and then timely feedback traffic information to managers as well as travelers. Traditional traffic flow predictions are generally suffering from the performance degradation by over-fitting and manual intervening, which cannot support large-scale and high-dimensional urban road network data. To address this issue, in this paper, a traffic flow prediction framework for urban road network based on deep learning is proposed. Firstly, the feature engineering is introduced to extract the features from a large volume of traffic dataset, with the anomaly nodes eliminated. Next, the big traffic dataset is compressed through the spectral clustering compression scheme. Finally, we designed a hybrid traffic flow prediction scheme based on LSTM (Long Short Term Memory) and Sparse Auto-Encoder (SAE). Experimental results show that our proposed model is superior to other models with an average prediction accuracy approaching 97.7%.

Cooperative content dissemination allows vehicles to directly retrieve content from each other by vehicle-to-vehicle (V2V) communications. It can release the downlink traffic pressure caused by repetitive of popular content downloads. A critical but open issue is how to motivate vehicles to participate in content dissemination. Most of the existing incentive mechanisms are proposed under a centralized architecture that suffers from the single point attack and trustless third-platform. In this paper, we propose a decentralized incentive mechanism for cooperative content dissemination in vehicular networks. By introducing the Directed Acyclic Graph (DAG) based blockchain technology, a decentralized architecture is proposed, which can improve incentive efficiency and reliability by removing the third-platform. Next, using the contract theory, the vehicles are divided into different types according to their route feature, and a series of contracts are designed for different types of vehicles to provide suitable incentives, as well as maximize the content generators' profit. Simulation results demonstrate the effectiveness and efficiency of our solution.

Internet of Vehicles (IoV) have been attracting increasing interests in recent years, targeting to support services between vehicles or vehicles and infrastructures involving driving safety, traffic and infotainment. However, the dynamic nature of IoV make it quite challenging by applying traditional TCP/IP protocol stack, considering the complex signaling and addressing procedure of TCP/IP as well as the strict End-to-End service requirements in vehicular environment. To address these issues, an information centric data dissemination scheme (IDDS) in IoV is proposed. At first, the framework of our IDDS is introduced with the PDU (Protocol Data Unit) type, data structure and protocol signaling procedure given. After that, to increase the data delivery ratio and reduce the incurred latency, a defer time based forwarder selection scheme is presented, with which the “Interest” and “Reply” packets could be exchanged on the link with better transmission performance, thus mitigating the negative impact of high mobility of vehicles. Simulation results show that IDDS could outperforms some traditional strategies in terms of average hop count and content acquisition delay, thus significantly increasing the data dissemination efficiency in vehicular information-centric networks.

In this paper, a Generative Adversarial Network(GAN) is proposed for data augmentation of remote sensing images abstracted from Jiangsu province in China, i.e., D-sGAN(Deeply-supervised GAN). At First, to modulate the layer activations, a down-sampling scheme is designed based on the segmentation map. Then, the architecture of the generator is UNet++ with the proposed down-sampling module. Next, the generator of this net is deeply supervised by the discriminator using deep Convolutional Neural Network(CNN). This paper further proved that the proposed down-sampling module and the dense connection characteristics of UNet++ are significantly beneficial to the retention of semantic information of remote sensing images. Numerical results demonstrated that the images generated by D-sGAN could be used to improve accuracy of the segmentation network, with a better Fully Convolutional Networks Score(FCN-Score) compared to the GoGAN, SimGAN and CycleGAN models.

With the increasing demand for high speed and low latency services on the Internet of Vehicles, researches on wireless networks in intelligent connected vehicles (ICVs) with communication and caching capability have attracted much attention. Content retrieving in ICVs is subject to performance degradation as a result of channel fading and intermittent network connectivity. The emerging fifth-generation (5G) networks are promising in supporting the needs of data transmission and alleviating the communication problems in ICVs. Specifically, to improve the users' quality of experience (QoE) and reduce the access delay of content retrieval, it helps to leverage in-network caching in on-board units and small cell base stations (SBSs). In this paper, we propose a cooperative caching scheme based on content popularity and transmission power restriction for inhomogeneous ICV, which pre-caches content files at SBSs to significantly reduce content retrieval delay. In specific, we model the proposed system as a cache management problem and attain optimal QoE by allocating proper transmission power for each content file. Using extensive simulations, we demonstrate that the proposed solution can effectively provide service for ICVs with high QoE in different scenarios.

The fifth-generation (5G) mobile communication systems support the millimeter-wave (mmWave) communications, which enable content dissemination for enhanced V2X (eV2X) services. However, the current content dissemination architectures face various problems, such as limited radio coverage of mmWave communications and differentiated Quality-of-Experience (QoE) requirements of users. To address these, we propose a regional-centralized content dissemination (RC-CD) scheme for eV2X services. The RC-CD can deal with the requests from distributed vehicles by the regional-centralized service architecture. By introducing vehicle-to-vehicle and vehicle-to-infrastructure mmWave communications, the content dissemination services are expanded to the areas that the mmWave base stations cannot cover. According to different QoE requirements, the requests of eV2X services are categorized into two groups: 1) elastic requests and 2) inelastic requests. Our algorithm considers the channel characteristics of mmWave and the different QoE requirements of requests to jointly optimize the waiting time of elastic requests and the failure ratio of inelastic requests. It also adopts an efficient heuristic approximate algorithm to solve the NP-completed optimization problem. The performance of our proposed scheme is evaluated by simulations in a realistic city layout. The results show that our scheme can provide an efficient architecture for eV2X content dissemination that supports different types of requests, provides a better QoE for the users, and achieves broader service coverage.

Vehicular named data networking (VNDN) has recently emerged as a novel paradigm to facilitate content-centric data sharing for Internet of Vehicles. However, an information holder can spread fake data to clients for malicious purposes, which may affect the driving decision of the recipient, or even worse, cause traffic congestion and accidents. In this article, we build a data-sharing system that consists of a double-layer blockchain. The nodes at the bottom layer request for service by announcing their requirements in the NDN paradigm. For the upper layer, the nodes submit their demands and supplies to the nearest roadside unit for further matching. We model the balance between the demand and supply as a matching game. To encourage nodes to provide positive services, a reputation management mechanism that combines negative and positive transaction records is proposed. Simulation results verify the validity of our system, and the data-sharing mechanism fosters a secure information interaction in the VNDN.

To improve the urban traffic condition and reduce accidents, we propose a platoon-driving model for autonomous vehicles in a free-flow traffic state in this article. This model allows vehicles with successful path matching to be grouped in a platoon and led by the platoon head (PH). In addition, a PH selection scheme is introduced to provide an incentive for vehicles to be PHs and maintain the dynamic update of platoons. Next, a smart contract is employed to enable the payment based on a blockchain between the PH and platoon members (PMs), avoiding the malicious and false payments. The numerical results show that the platoon model is superior to the individual driving model in terms of fuel consumption. The comparison between carpooling and noncarpooling modes within the platoon shows that our model has a better performance in terms of PH revenue and PM's service charge.

The development of the smart ocean requires that various features of the ocean be explored and understood. The Underwater Internet of Things (UIoT), an extension of the Internet of Things (IoT) to the underwater environment, constitutes powerful technology for achieving the smart ocean. This article provides an overview of the UIoT with emphasis on current advances, future system architecture, applications, challenges, and open issues. The UIoT is enabled by the most recent developments in autonomous underwater vehicles, smart sensors, underwater communication technologies, and underwater routing protocols. In the coming years, the UIoT is expected to bridge diverse technologies for sensing the ocean, allowing it to become a smart network of interconnected underwater objects that has self-learning and intelligent computing capabilities. This article first provides a horizontal overview of the UIoT. Then, we present a five-layer system architecture for the future UIoT, which consists of a sensing, communication, networking, fusion, and application layer. Finally, we suggest the current challenges and the future UIoT research trends, in which cloud computing, fog computing, and artificial intelligence are combined.

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety.

In Vehicular Ad Hoc Networks (VANETs), the unreliable wireless environment and highly dynamic topology make multi-content distribution inefficient due to the large amount of content requests from vehicles. This gives rise to the need for new content distribution schemes in VANETs. Current researches on content distribution for VANETs generally focus on the single type of content. In this paper, we consider both elastic (with no hard delay requirement) and inelastic (with a hard deadline) contents and propose a joint content distribution scheme in VANETs. We model the content distribution as an optimization problem which jointly minimizes the waiting delay of elastic requests and the failure ratio of inelastic requests. Then, we propose an efficient clonal selection-based approximate algorithm to solve the optimization problem. The performance of our scheme is evaluated by simulation using realistic vehicular traces. Simulation results show that our proposed scheme has better performance than previous solutions.

Due to the limited computing resources of swarm of drones, it is difficult to handle computation-intensive tasks locally, hence the cloud based computation offloading is widely adopted. However, for the business which requires low latency and high reliability, the cloud-based solution is not suitable, because of the slow response time caused by long distance data transmission. Therefore, to solve the problem mentioned above, in this paper, we introduce fog computing into swarm of drones (FCSD). Focusing on the latency and reliability sensitive business scenarios, the latency and reliability is constructed as the constraints of the optimization problem. And in order to enhance the practicality of the FCSD system, we formulate the energy consumption of FCSD as the optimization target function, to decrease the energy consumption as far as possible, under the premise of satisfying the latency and reliability requirements of the task. Furthermore, a heuristic algorithm based on genetic algorithm is designed to perform optimal task allocation in FCSD system. The simulation results validate that the proposed fog based computation offloading with the heuristic algorithm can complete the computing task effectively with the minimal energy consumption under the requirements of latency and reliability.

Industrial Internet of Things (IIoT) is a revolution which is changing the visage of industry in a profound manner. However, it brings many opportunities as well as many puzzles and challenges. Facing with billions of programmable IIoT devices, the traditional IIoT architecture based on cloud computing is no longer suitable, therefore, Mobile Edge Computing (MEC) has been seen as the promising technology to support IIoT business in 5G era. However, the existing mainstream MEC framework exposes numerous problems when supporting IIoT, such as complex development, low development reuse rate, poor software maintainability and mobility, poor flexibility, etc. Therefore, in order to solve the problems mentioned above, in this paper, we propose IIoT-MEC, a novel MEC framework specially for IIoT. We use Docker container to slice computing and storage resources of MEC server into numerous resource blocks (RBs). Based on the concept of virtualization, some RBs for “Device Function Virtualization (DFV)” are used to map physical devices into virtual devices and present in a set of normalized APIs, which shield the hardware development of diverse IIoT devices, so as to simplify the IIoT development into software development only. Some RBs are used to support the operation of IIoT services, in the form of distributed computing. On these basis, a flexible object-oriented IIoT development architecture is constructed. IIoT-MEC can overcome the drawbacks of the existing MEC framework in supporting IIoT. And the implementation procedure of IIoT-MEC is demonstrated with an application example. We also discuss how the IIoT-MEC would be used and what we need to do in future research.

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively.

As one of the key services for non-safety applications in Vehicular Ad-hoc Networks (VANETs), the Popular Content Distribution (PCD) has become a hot issue in recent years. In popular content distribution, the On-Board Units (OBUs) passing the Area of Interest (AoI) receive popular content broadcast by the RoadSide Units (RSUs). However, due to the high speed of OBUs, limited bandwidth, and unstable wireless connections, only a portion of the popular content can be received by OBUs. To address this issue, in this paper, a cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed. The scheme allows the OBUs to cooperate with their neighbors to provide the missing popular content. In addition, a coalition graph game algorithm is designed for optimizing the cooperative behaviors among OBUs. The performance of our CVCG scheme is evaluated by different metrics compared to other three content distribution schemes. The numerical results show that the proposed CVCG scheme could outperform the three schemes in terms of the number of iterations for 99 percent finished PCD, the average content completion percentage, and the number of completed OBUs.

Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.

Internet of Vehicles (IoV) has drawn more and more attention. However, due to high vehicle movement and frequent topology changing, it is quite challenging to design an efficient routing protocol in the complex urban IoV. In this paper, we propose a Connectivity aware Transmission quality guaranteed Geographic Routing in urban IoV (CTGR). First, we give the connectivity model in case of disconnected link and the transmission quality (TQ) model when the network is connected, respectively. Then, using the two models, with the assistance of road weight evaluation scheme (RWE), each road segment can be assigned with a suitable weight. Based on the weight information, the road segment can be dynamically selected one by one to comprise the optimized routing path, avoiding the local maximum and data congestion. An improved next hop selection strategy is further proposed to forward the packet along the selected road segment, guaranteeing fast and reliable packet transmission. Simulation results show that our proposed protocol achieves higher packet delivery ratio and lower transmission delay compared with the existing protocols.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more interest. Designing one efficient routing protocol is one of the most important issues for urban VANETs. However, fast node movement, dynamic topology changes and complicated channel environments make it quite challenging. In this paper, a Delay-aware and Backbone-based Geographic Routing (DBGR) protocol for urban VANETs is proposed. This protocol comprehensively exploits the real-time traffic information in case of link connection and the historical traffic information when the link is disconnected to make a route selection for packet forwarding. Based on the current traffic condition, using the road weight evaluation scheme (RWE), each road segment can be assigned with an appropriate weight associated with the corresponding transmission delay, by which the weight matrix of the network topology can be built. Using the matrix, the optimized route with the minimum delay can be selected. Simulation results show that the proposed protocol outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more attention. However, due to the fast movement of vehicles and dynamic topology change, designing an efficient routing protocol in the complex urban environment is quite challenging. In this paper, an Intersection-based Geographic Routing with Transmission Quality guaranteed in Urban VANETs (IGRTQ) is proposed. As a selection guidance of the best route, each road segment is assigned with a weight based on the collected information related to the delay and connectivity of each road segment. Based on the weight information, the road segment can be dynamically selected one by one to form the optimized routing path, avoiding the local maximum and data congestion. An improved greedy strategy is further proposed to forward the packet along the selected road segment, ensuring the fast and reliable packet transmission. Simulation results show that the proposed protocol provides higher packet delivery ratio and lower end-to-end delay compared to the existing protocols.

Recently, the industrial Internet of Things (IIoT) has drawn high attention in academia and industry in the context of industry 4.0. In the IIoT, smart IoT devices are adopted to improve production efficiency. But, these devices will generate huge amounts of production data, which need to be processed effectively. To support IIoT services efficiently, cloud computing is usually considered as one of the possible solutions. However, the IIoT services still suffer from the high-latency and unreliable links problem between cloud and IIoT terminals. To combat these issues, fog computing is a promising solution which extends computing and storage to the network edge. In this paper, we are motivated to integrate the fog computing to the cloud-based IIoT to build a cloud-fog integrated IIoT (CF-IIoT) network. To achieve the ultra-low service response latency, we introduce the distributed computing to the CF-IIoT network and propose leveraging the real-coded genetic algorithm for constrained optimization problem(RCGA-CO) algorithm to optimize the load balancing problem of the distributed cloud-fog network. Most importantly, considering the unreliable situation in the CF-IIoT (e.g., fog nodes damage, wireless links outage), we propose a task reallocation and retransmission mechanism to reduce the average service latency of the CF-IIoT network architecture. The performance evaluation results validate that the RCGA-CO-based CF-IIoT and our proposed mechanism can provide ultra-low latency service in IIoT scenario.

In recent years, the rapid improvement of sensor and wireless communication technologies powerfully impels the development of advanced cooperative driving systems, generating the demands to form the Internet of Vehicles (IoV). With the assistance of cooperative communication among vehicles, the road safety can be greatly enhanced in the IoV. In this paper, we propose a cooperative driving scheme for vehicles at intersections in the IoV. First, the driver’s intention is modeled by the BP neural network trained with driving dataset. Then, the identified intention is used as the control matrix of the Kalman filter model, by which the vehicle trajectory can be predicted. Finally, by collecting the information of vehicles’ trajectories at the intersections, we develop a collision probability evaluation model to reflect the conflict level among vehicles at intersections. Through obtained collision probability, the driver or the autonomous control unit can determine the next step to avoid the possible collisions. Numerical results show that our proposed scheme has high accuracy in terms of driver’s intention identification, trajectory prediction and collision probability evaluation.

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.

Vehicular Ad Hoc Networks (VANETs) have been paid significant increasing attentions. However, the inherent characteristics of VANETs, such as the dynamic network topology, uneven distribution of vehicles, frequent disconnections etc., bring great challenges for designing an efficient routing protocol in urban environment. In this paper, we first introduce a new routing metric called link transmission quality (LTQ), which exploits the effect of links' position along the routing path and also takes both transmission cost and forwarding reliability into consideration, to evaluate the performance of a multi-hop link. Next, with the help of back-bone nodes selected based on specified criterions, road segments are assigned different weights according to the value of calculated LTQ. Finally, a novel geographic routing protocol based on LTQ in urban VANETs, named as LTQGR, is proposed, where the routing path with the lowest aggregated weights is selected for packets transmission. When data packets are forwarded along the optimized route, one forwarding strategy is presented to guarantee the efficient transmission. Simulation results show that our proposed LTQGR outperforms GPSR and another ETX-based protocol HLAR in terms of the average transmission delay, packet delivery ratio and routing overheads.

Vehicular Ad-hoc Networks(VANET) has been considered as an important and popular research domain in future Intelligent Transportation Systems(ITS). To support VANET applications, some emerging technologies, such as cloud computing, fog computing, Software Define Network(SDN) are introduced in VANET. Although supported by these technologies, VANET still suffers with some problems, especially reliability issue. Due to the potentially processing nodes(communication links) failure in realistic environment, VANET safety applications, which have strict real time requirement, may fail and cause catastrophic consequence. In this paper, we propose a novel reliability model with delay constraint and fault-tolerant mechanism based on retransmission in hybrid SDN/Fog computing VANET(HSFV) architecture. With the global knowledge of SDN and low delay of fog computing, a novel fault-tolerant reliability optimization particle swarm optimization(FPSO-RO) algorithm is introduced into HSFV architecture. The simulation results indicate that the proposed algorithm could effectively evaluate and improve reliability in real-time VANET systems.

In VANETs, information exchange plays an important role to make vehicles aware to each other and provide references for safety or non-safety related applications. At intersections, vehicles are forced to maintain a lower mobility thereby have more time to exchange on-road or off-road context information. However, if an effective control scheme is not applied to this specific scenario, a packet congestion is more likely occurring especially when the vehicle density is high and channel load is heavy. In this paper, to solve the aforementioned problem, a congestion control scheme based on non-cooperative bargaining game is proposed. Numerical results show that our proposed Joint Power and Rate Adjustment congestion control scheme (JPRA) can greatly alleviate the possible packets collisions thus reducing the average experienced delay in queue and increasing the number of successfully delivered packets on the receiver side.

In this paper, we propose a novel concept of virtual resource chain for Software Defined Campus WLANs (SD-WLANs). A typical SD-WLAN is composed of three layers, i.e., infrastructure, access control and application layers. Firstly, the network control plane is decoupled from the forwarding plane, and soft-defined access points (SD-APs) merely execute the forwarding rules according to the instructions from access controllers (ACs). Secondly, with a global view of the network, AC could abstract, encapsulate, and virtualize the physical resources (e.g., computing, storage, and radio resources). Finally, by binding all the resources between the infrastructure and access control layers, isolated virtual resource chains are built up and simultaneously mapped to northbound interface (NBI) to accommodate various services at the application layer. Benefiting from the virtual resource chain, a user-oriented load balance scheme is presented in this paper. In SD-WLANs, the load of APs could be balanced according to the user's demands, and our proposed user-oriented load balance scheme could be easily invoked only by coding at the application layer. Experiment results demonstrate that our scheme is able to distribute mobile stations among all the APs and increase the average system throughput, and thus to increase the flexibility and availability of networks.

Based on the analysis to the phenomenon and methods of denial-of-sleep attacking in wireless sensor network, a scheme is proposed employing fake schedule switch with RSSI measurement aid. The sensor nodes can reduce and weaken the harm from collision, exhaustion and broadcast attack and on the contrary make the attackers lose their energy quickly so as to die. Simulation results show that at a bit price of energy and delay, network health can be guaranteed and packets drop ratio has been decreased compare with original scenario without our scheme.

Based on the analysis of the node selfishness and the drawback of min-hop selection method as the unique routing selection criteria in ad hoc networks, a non-intrusive multi-metric ad hoc routing protocol-NIMR is presented. By computing and storing the node fame, NIMR greatly attenuates the influence of node selfishness; By crossing-design between MAC layer and network layer, we employ a non-intrusive real-time available bandwidth measurement scheme to solve the congestion problem. Finally, we propose a new metric as the routing selection criteria, which combines fame, available bandwidth and minimum hops by weight. Simulation results show, Compared with DSR, fairness between nodes is improved and congestion control and load balance is implemented using NIMR without additionally increasing the network load. At the same time average life-span and end-to-end throughput is increased, average end-to-end delay is decreased using NIMR.

In this paper, we present a nonintrusive and time-based mechanism to estimate the available bandwidth of a node with the nodes collision and backoff stage taken into account. We first give a new definition of available bandwidth in IEEE802.11b network to reflect the collision and contention between nodes. Then, the investigated node obtains the NAV information of its neighbors within the measurement duration through CSMA/CA mechanism and stores the information into its routing buffers. Subsequently, we obtain the available bandwidth of the investigated node by calculate the ratio of its channel accessible time to the measurement duration, which should subtract the sum of NAV values of its neighbors and the backoff time itself from the measurement duration. Simulation shows the proposed scheme is accurate and can completely reflect the collision and contention between nodes

The IEEE 802.11e is an extension of the IEEE 802.11 to provide Quality of Service (QoS) for applications requiring real time services. However, when used with the multi-rate scheme, they suffer unfairness problem. On the other hand, the traditional contention window size (CW) update method actually depends on the collision times of the investigated station, which would possibly causes more contentions especially under heavy load conditions. In this paper, we propose a scheme, which utilizes the collision times to estimate contention degree before transmission, turn TXOP, and at the same time, solves the unfairness problem in the IEEE 802.11e networks employing the multi-rate scheme.

One of the principal disadvantages of orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Carrier frequency offset can be divided into two parts: an integer one and a fractional one. The integer frequency offset has no effect on the orthogonality among the subcarriers, however causes a circular shift of the received data symbols, resulting in a BER of 0.5. The maximum likelihood (ML) estimation algorithm of the integer frequency offset is derived under the assumption that the channel impairments only consist of additive noise. Simulation results show that it can perform well even in a time-dispersive channel. Its performance is assessed and compared with the conventional method by computer simulations for the additive white Gaussian noise (AWGN) channel and a multipath fading channel.

In this paper, a new maximum likelihood (ML) estimator for the integer frequency offset estimation of OFDM systems is proposed and compared with the conventional SC estimator. Both estimators exploit the differential information between two consecutive blocks of OFDM data symbols in the frequency domain. The reason why the proposed ML estimator has better performance than the conventional SC method is analyzed. The mean and variance of the proposed and conventional estimators are discussed, and the estimation error probability is given. The computer simulation results are in good agreement with the analytical study.

An efficient estimator for integer frequency offset estimation of OFDM systems is derived, which is based on maximum likelihood (ML) technique and exploits the differential information between two consecutive blocks of OFDM data symbols in frequency domain. The reason why the proposed ML estimator has better performance than the conventional method is analyzed. How to select the differential sequence is also studied. By computer simulations, the effects of the various parameters such as length of cyclic prefix, value of integer frequency offset and differential PN sequence on the performances of the two estimators are evaluated, and the performance of the ML estimator is compared with that of the conventional method for the additive white Gaussian noise (AWGN) channel and a multipath fading channel. The simulation results are in good agreement with the analytical study.

This paper presents a robust MDPSK modulation classifier based on cumulants to frequency offset. The feature proposed in the algorithm is invariant with respect to constellation scale, rotation, the shift and the carrier frequency offset between transmitter and receiver. The invariant property is proved in theory. Through computer simulation the performance is evaluated and the results show that the improved classification algorithm is high in performance and valuable in practice.