A Data Download Method from RSUs Using Fog Computing in Connected Vehicles

Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and an RSU has poor connection quality, the efficiency and speed of the data download from the RSU is decreased. This problem affects the quality of the user experience. Therefore, it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed. The proposed method maximizes download speed from an RSU using a machine learning algorithm. To collect and learn from network data, fog computing is used. A fog server is integrated with the RSU to perform computing. If the algorithm recognizes that conditions are not good for mass data download, it will not attempt to download at high speed. Thus, the proposed method can improve the efficiency of high speed downloads. This conclusion was validated using extensive computer simulations.     

Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first is the migration trigger determination algorithm for cross-border migration and service degradation migration, respectively, based on trajectory prediction and traffic awareness to improve the determination accuracy. The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions. The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions. Simulation results show that the proposed migration mechanism can reduce migration times, reduce average migration time, improve average service time and enhance the stability and adaptability of IoV application services.     

A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

The vehicular cloud computing is an emerging technology that changes vehicle communication and underlying traffic management applications. However, cloud computing has disadvantages such as high delay, low privacy and high communication cost, which can not meet the needs of real-time interactive information of Internet of vehicles. Ensuring security and privacy in Internet of Vehicles is also regarded as one of its most important challenges. Therefore, in order to ensure the user information security and improve the real-time of vehicle information interaction, this paper proposes an anonymous authentication scheme based on edge computing. In this scheme, the concept of edge computing is introduced into the Internet of vehicles, which makes full use of the redundant computing power and storage capacity of idle edge equipment. The edge vehicle nodes are determined by simple algorithm of defining distance and resources, and the improved RSA encryption algorithm is used to encrypt the user information. The improved RSA algorithm encrypts the user information by reencrypting the encryption parameters . Compared with the traditional RSA algorithm, it can resist more attacks, so it is used to ensure the security of user information. It can not only protect the privacy of vehicles, but also avoid anonymous abuse. Simulation results show that the proposed scheme has lower computational complexity and communication overhead than the traditional anonymous scheme.     

An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles

A new paradigm of VANET has emerged in recent years: Internet of Vehicles (IoV). These networks are formed on the roads and streets between travellers who have relationships, interactions and common social interests. Users of these networks exchange information of common interest, for example, traffic jams and dangers on the way. They can also exchange files such as multimedia files. IoV is considered as part of the Internet of Things (IoT) where objects are vehicles, which can create a multitude of services dedicated to the intelligent transportation system. The interest is to permit to all connected vehicles to communicate with each other and/or with a central server, through other vehicles. Vehicle to Vehicle (V2V) communication is the main component, because the services encompassed in the IoV are based on the vehicles in question, such as transmitter, relay and receiver. This work is focusing on designing and developing a Quality of Service (QoS) routing scheme dedicated to IoV networks. Especially, we aim to improve the Greedy Traffic Aware Routing (GyTAR) protocol to support QoS in IoV networks. To evaluate the proposed approach in terms of QoS in the context of IoV networks, the performance metrics such as average end-to-end delay and packet delivery ratio are taken into consideration to analyse the network situation.     

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an Integer Linear Program (ILP). This integrates the future estimation metric to predict the future arrivals of the vehicles, searching the optimal routes. Considering the mobile nature of IOV, fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path. The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path. Thus, this work strongly supports its use in applications having stringent Quality of Service (QoS) requirements for the vehicles.     

Connected Vehicles Computation Task Offloading Based on Opportunism inCooperative Edge Computing

The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud. Meanwhile, offloading decision and resource allocation of the user CVs must be ascertained. Furthermore, the joint offloading decision and resource allocation problem is described as a Mixed Integer Nonlinear Programming (MINLP) problem, which optimizes the task response latency of user CVs under various constraints. The original problem is decomposed into two subproblems. First, the Lagrange dual method is used to acquire the best resource allocation with the fixed offloading decision. Then, the satisfaction-driven method based on trial and error (TE) learning is adopted to optimize the offloading decision. Finally, a comprehensive series of experiments are conducted to demonstrate that our suggested scheme is more effective than other comparison schemes.     

Transparent Access to Heterogeneous IoT Based on Virtual Resources

The Internet of Things (IoT) inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily life. Edge computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected devices. According to the purpose of the environment including privacy level, domain functionality, network scale and service quality, various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge computing. However, for providing user-friendly service scenarios based on the transparent access to heterogeneous devices in edge computing, a consistent interface shall be provided to deliver services from edge computing to clients. In this paper, we propose transparent computing based on virtual resources to access heterogeneous IoT devices without considering the underlying network configuration at the edge of the networks. For supporting transparent access to different edge computing environments through a consistent interface, the virtual resource of edge gateway is proposed to bridge the Internet and devices which are deployed on the edge of the network. The proposed edge gateway exposes the services of the Internet of Things devices to the Internet using virtual resources that represent the resources of physical devices. The virtual resources provide a consistent interface to enable clients to access devices in edge computing without considering underlying protocols. The virtual resource is generated by the resource directory in the edge gateway through the registration of a device. Based on the device registration, the device information is stored in the gateway to link virtual resources and devices for translating messages according to the destination protocols and identifying physical devices that are represented by virtual resources. Moreover, through collaboration with the service provider, the function of device discovery and monitoring is provided to clients.     

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

The content-centric networking (CCN) architecture allows access to the content through name, instead of the physical location where the content is stored, which makes it a more robust and flexible content-based architecture. Nevertheless, in CCN, the broadcast nature of vehicles on the Internet of Vehicles (IoV) results in latency and network congestion. The IoV-based content distribution is an emerging concept in which all the vehicles are connected via the internet. Due to the high mobility of vehicles, however, IoV applications have different network requirements that differ from those of many other networks, posing new challenges. Considering this, a novel strategy mediator framework is presented in this paper for managing the network resources efficiently. Software-defined network (SDN) controller is deployed for improving the routing flexibility and facilitating in the inter-interoperability of heterogeneous devices within the network. Due to the limited memory of edge devices, the delectable bloom filters are used for caching and storage. Finally, the proposed scheme is compared with the existing variants for validating its effectiveness.     

Intelligent vehicle clusters (Inside Com2React project)

The intelligent vehicle clusters concept has been introduced and developed in the Com2React project to overcome some of the limitations of the former REACT research project. Cluster of vehicles together with the regional control center constitute a hierarchical traffic information and communication structure where vehicles on the local level are mainly self-organised but then additionally controlled by the traffic management system on the regional level. The main challenge was to secure the achievements and benefits of the REACT applications and to find a new balance for efficient data processing, communication and utilisation between the different entities in the overall system. The aim here is to give a brief review and analysis of the most important features and benefits of this concept.     

A Capacity Improving Scheme in Multi-RSUs Deployed V2I

The communication reliability and system capacity are two of the key performance indicators for Internet of Vehicles (IoV). Existing studies have proposed a variety of technologies to improve reliability and other performance, such as channel selection and power allocation in Vehicle-to-Infrastructure (V2I). However, these researches are mostly applied in a single roadside unit (RSU) scenario without considering inter-cell interference (ICI) of multi-RSUs. In this paper, considering the distribution characteristics of multi-RSUs deployment and corresponding ICI, we propose a reliable uplink transmission scheme to maximize the total capacity and decrease the interference of multi-RSUs (mRSU-DI) in condition of the uplink interruption performance. In the proposed mRSU-DI scheme, ICI is depressed by dynamic channel and power allocation algorithm. A heuristic algorithm based on penalty function is proposed to obtain the optimal power allocation solution of the model. In addition, we realize the scheme in both given conditions of channel state information (CSI) and channel state distribution, respectively. The results show that the proposed scheme can both improve the system capacity and guarantee the reliable transmission in both premises.     

Service Scheduling Based on Edge Computing for Power Distribution IoT

With the growing amounts of multi-micro grids, electric vehicles, smart home, smart cities connected to the Power Distribution Internet of Things (PD-IoT) system, greater computing resource and communication bandwidth are required for power distribution. It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence. This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT. The architecture, components and functional requirements of the PD-IoT with edge computing platform are proposed. Then, the structure of the service scheduling system is presented. Further, a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method. The validity of the method is evaluated by simulation tests. Results indicate that the mean load balancing ratio is reduced by 99.16% and the optimized offloading links can be acquired within 1.8 iterations. Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.     

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose a solution, the vehicular safety support system (V3S), which exchanges BSMs reliably even when many vehicles are on the road. The V3S uses a clustering scheme to decrease network traffic by reducing the amount of data exchanged between a vehicle and the roadside unit (RSU). In addition, the V3S reduces the collision rate of wireless network packets by broadcasting the vehicle's BSM in an allocated timeslot using the time division multiple access (TDMA) MAC protocol. The V3S also deals with insufficient bandwidth for dedicated short-range communications (DSRC) by changing DSRC channels according to traffic flow. In evaluating the packet error rate for stable BSM packet delivery, the V3S demonstrates an excellent packet error rate of less than 1%, compared to the 802.11p with its packet error rate of 82%.     

Trusted virtual machine monitor-based group signature architecture

Group communication is an important technique for many network computing applications. In group communication, a member in a group sends a message to others normally by multicast. Group signature guarantees the integrity of the exchanged data and provides source authentication. In a virtual machine (VMs) based computing system, a virtual machine monitor (VMM) allows applications to run in different VMs strongly isolated from each other. A trusted VMM (TVMM) based platform can provide stronger security protection for group signature systems than traditional computing platforms can. The authors first introduce a TVMM-based group signature architecture and a TVMM security protection mechanism for group signature components. Then, the authors propose a group signature scheme using the function of message checking based on the discrete logarithm problem. Finally, the authors prove the correctness of the group signature scheme and analyse its security in virtual computing environments.     

Inter-vehicle mobile ad hoc network for road transport systems

Access to information services while on the move is becoming increasingly prevalent within transport systems. Whereas Internet access is now common place in trains, it still remains a challenge for vehicles, particularly when travelling through high speed motorways. Motorway vehicles equipped with wireless communication nodes form an ad hoc network have been examined by which data can be exchanged among them without the need for a pre-installed infrastructure. The main challenge with such an infrastructure-less network is developing communications and protocols that can deliver robust and reliable ad hoc communications between vehicles, when the relative speed between vehicles that can be extremely high under opposite traffic conditions. To address this opposite direction effect, a solution has been presented by minimising the effect of opposite traffic on routing packets. Firstly, a router direction index is introduced to enhance the performance of ad hoc on demand distance vector protocol in updating its routing table and secondly, a new queue priority mechanism is proposed which is based on cross-layer collaboration. Simulations were performed for an ad hoc network consisting of 200 vehicles driving with speeds between 90 and 120 km/h on a two-way motorway for different traffic loads sent through a Gateway adjacent to the motorway. The results obtained demonstrate a performance increase in the average data goodput and less routing overhead for the proposed solution     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles (IoV) usually have linear and nonlinear composite characteristics. In order to accurately describe the dynamic change trend of such loads, this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV. Firstly, a chaotic analysis algorithm is implemented to process the load-time series, while some learning samples of load prediction are constructed. Secondly, a support vector machine (SVM) is used to establish a load prediction model, and an improved artificial bee colony (IABC) function is designed to enhance the learning ability of the SVM. Finally, a CloudSim simulation platform is created to select the per-minute CPU load history data in the mobile cloud computing system, which is composed of 50 vehicles as the data set; and a comparison experiment is conducted by using a grey model, a back propagation neural network, a radial basis function (RBF) neural network and a RBF kernel function of SVM. As shown in the experimental results, the prediction accuracy of the method proposed in this study is significantly higher than other models, with a significantly reduced real-time prediction error for resource loading in mobile cloud environments. Compared with single-prediction models, the prediction method proposed can build up multidimensional time series in capturing complex load time series, fit and describe the load change trends, approximate the load time variability more precisely, and deliver strong generalization ability to load prediction models for mobile cloud computing resources.     

Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model, thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service. On this basis, a statistical method is designed, which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions. Finally, this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai. The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements, and provide better privacy protection and service for the users of the IoV.     

分布式虚拟环境动态共享状态管理策略

分布式虚拟环境的动态共享状态是所有关于虚拟环境的不断变化的信息,而维护动态共享状态是建立分布式虚拟环境的重要环节。提出并比较了维护共享状态的三种策略:集中式信息存储策略、高频率状态再生策略和 DR 技术策略。集中式信息存储策略提供了很高的一致性,然而代价是高的带宽开销、减慢的吞吐量,以及各主机间的依赖性增强;高频率状态再生策略牺牲了数据的绝对一致性,但能保证数据的最终一致性并且减少了各主机间的依赖性;DR 技术策略提供的数据一致性很差,但节省了带宽并增强了各主机的自主权,可支持大量的用户。     

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.