Query: vehicular edge computing network

title: Reputation Based Miner Node Selection in Blockchain Based Vehicular Edge Computing
abstract: Vehicular edge computing (VEC) is one of the most efficient ways to have high functionality in resource constrained connected vehicles. As the edge nodes in VEC holds paramount importance and highly sensitive data, we need to make sure the information exchange remains tamper free. Although blockchain provides immutable and verifiable data storage solutions, the nodes that enable blockchain might be under attack such as a denial of service from various malicious actors. One way to nullify these types of attacks is to find the nodes under attack and remove them from the blockchain consensus process to ensure the proper functioning of the system, i.e. miner node selection. Similarly, in order to provide the decision on which node is to be removed from the network, we need a smart decision making technique such as an artificial neural network (ANN) The ANN is used to calculate the reputation of the miner nodes. In this article, we propose a reputation based miner node selection (RbMNS) in blockchain based vehicular edge computing (BloVEC) We have used ANN and hyperledger fabric (HF) to implement the RbMNS and blockchain model in the system. We obtained the average transactional latency of 128 ms/109 ms with/without our implementation, respectively.

title: Intent Based Network for Data Dissemination in Software Defined Vehicular Edge Computing
abstract: With the surge in the demand for online services and multimedia applications, the traffic on the underlying network infrastructure has escalated (multi folded) in recent years. To meet the strict latency requirements, Software defined Networking (SDN) provides flexible network control (and possible intelligence) that can act as an enabler for application oriented service industry. However, the crippling gap between the business needs and the network delivery potential necessitates the underlying network to constantly (and consistently) adapt, protect, and inform across all strands of the service oriented landscape. Intent based network has emerged as a recent solution to the cover the above gap by capturing business intent and thereafter activating and assuring it networkwide. Motivated from these facts, in this article, an Intent based network control framework has been designed over the SDN architecture for data dissemination in the vehicular edge computing ecosystem. In this framework, a tensor based mechanism is used to reduce the dimensionality of the incoming elephant like traffic and then classifying the specific attribute data traffic according to the defined priority requirement of the underlying applications. Here, the network policies are configured using the intent based controller according to the application requirement and then forwarded to the SDN controller to enable intelligent data dissemination (through an optimal route) at the data plane. Convolution Neural Network is used to train the flow table to allocate the route dynamically for the classified traffic queues. The proposed framework has been evaluated through extensive simulations and the results supports the claims in terms of the quality of service requirements.

title: Adaptive Digital Twin and Multi agent Deep Reinforcement Learning for Vehicular Edge Computing and Networks
abstract: Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multi agent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic data sets demonstrate the efficiency of our proposed schemes.

title: Slicing Based Offloading in Vehicular Edge Computing
abstract: Vehicular edge computing (VEC) provides an environment for offloading tasks from vehicles. Indeed, the advantage through VEC is to push power computational and storage capacities at the edge nodes near the vehicles to handle the enormous resources required by some applications. On the other hand, in order to manage efficiently these resources, it would be necessary to partition them into several parts, each dedicated to a specific service. Thus, integrating network slicing in VEC appears to be relevant. Therefore, in this paper we study the task offloading problem from vehicles to wireless 5G new generation nodes (gNBs) and road side units (RSUs) hosting sliced edge computing servers. We formulate the problem as an integer linear programming problem and propose a new algorithm, which follows a centralized control strategy to holistically view and manage the whole network, and the sliced edge nodes. In addition, it follows network function virtualization framework to separate the logical network from the physical resources. The simulation results show that, in terms of acceptance ratio, the proposed algorithm provides very close results to the optimal solution, and when compared to state of art algorithm, integrating slicing is better when there is enough resources on the hosting nodes, but it still guarantees the differentiation among services.

title: Reverse Offloading for Latency Minimization in Vehicular Edge Computing
abstract: The safety of autonomous driving can be improved with the support of Cooperative Vehicle Infrastructure System (CVIS) and Vehicular Edge Computing (VEC) which benefit greatly from crowdsensing of CVIS and accurate decision in a short deadline of VEC. In the CVIS, the vehicles will upload the crowdsensing data to the VEC server for data fusion and tasks generating. However, with the ever increasing number of vehicles, the VEC server cannot undertake massive computation intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework to fully utilize the computation resource of vehicles to relieve the burden of the VEC server in a multi vehicle mobile edge network. First, the system latency minimization problem is formulated as a mixed integer nonlinear programming problem by optimizing reverse offloading decisions and the communication and computation resources allocation. Next, the original problem is transformed into an equivalent weighted sum optimization problem, which can be decoupled as two subproblems, i.e. resource allocation and decision selection subproblems. The closed form expressions for the optimal resource allocation are derived by the dual decomposition method in a distributed fashion. Moreover, a low complexity greedy based efficient searching (GES) algorithm is proposed to obtain the reverse offloading decision strategies. Simulation results show that the proposed algorithm can significantly improve the performance compared with other baseline schemes.

title: Computation Resource Configuration for Vehicular Edge Computing: A Fluid Model Based Approach
abstract: In this paper, the computation resource configuration for vehicular edge computing is studied. Unlike most of existing work in the literature, we focus on the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long term management profit of the network operator (i.e. the road side unit of the vehicular network) under the randomness of vehicular traffics and task processing. To describe the heterogeneities among different vehicles in terms of their edge computing service requirements and associated service payments, a multi class management framework is employed. By considering the steady state nature of the system with dynamic evolutions, a novel fluid model is developed, which facilitates the formulation of the corresponding resource optimization problem. Based on this, we propose a simple but efficient solution, called threshold based computation resource configuration scheme (TCRCS) Theoretical analyses and simulations evaluate the performance of the proposed solution and demonstrate its superiority compared to counterparts.

title: Imitation Learning Enabled Vehicular Edge Computing: Toward Online Task Scheduling
abstract: Vehicular edge computing (VEC) is a promising paradigm to enable information sharing and acquisition among vehicles. Since information freshness has significantly influence on task scheduling, an emerging metric, named the age of information (AoI) has been utilized to evaluate it. Recent research generally focuses on AoI minimization but pays little attention to information personality. However, different impacts caused by distinct information may be posed on user decisions. This article first briefly introduces the state of the art of imitation learning in wireless networks. After that, an imitation learning based online task scheduling scheme is designed with the support of VEC. It intends to minimize the average age of critical information (AoCI) referring to the age of information that has significant impacts on vehicle decisions. Performance evaluations show that the proposed scheme outperforms other algorithms from several aspects. At last, we discuss several potential research challenges and open issues for artificial intelligence in the Internet of Vehicles.

title: AI Empowered Content Caching in Vehicular Edge Computing: Opportunities and Challenges
abstract: Vehicular networks are an indispensable component of future autonomous and intelligent transport systems. Today, many vehicular networking applications are emerging, and therefore, efficient data computation, storage, and retrieval solutions are needed. Vehicular edge computing (VEC) is a promising technique that uses roadside units to act as edge servers for caching and task offloading purposes. We present a task based architecture of content caching in VEC, where three major tasks are identified, namely, content popularity prediction, content placement in the cache, and content retrieval from the cache. We present an overview of how artificial intelligence techniques such as regression and deep Q learning can improve the efficiency of these tasks. We also highlight related future research opportunities in areas such as collaborative data sharing for improved caching, efficient sub channel allocation for content retrieval in C V2X, and secure caching.

title: Blockchain Enabled Intelligent Vehicular Edge Computing
abstract: Smart vehicles are expected to be equipped with high dimensional, resource intensive applications, including platoon control, augmented reality supported gaming, AI based pedestrian detection, fuel scheduling, and so on, catering to diverse user preferences and enhancing safety and efficiency. These applications pose unique challenges for resource constrained vehicles due to their intense computation requirements, whereas vehicular edge computing (VEC) networks, consisting of roadside units (RSUs) and MEC servers, contain the capability of providing cloud like computing experience at vehicular edges while meeting performance requirements in terms of latency and throughput. Moreover, the development of intelligent VEC (IVEC) infrastructure is accelerated due to rapid advancement of AI algorithms in recent years. However, IVEC is prone to attacks, including fake computation feedback, unfair or biased resource allocation in a VEC server, and so on, due to its centralized governance and black box computation (edge computation works like a black box for end users) To combat such security vulnerabilities, we propose a blockchain based decentralized architecture to enhance transparency in IVEC resource management and leverage edge consumers (e.g. vehicles) with a computation verification option. Additionally, we address the unbalanced load distribution issue and propose a secure IVEC federation model for balancing loads. We also outline the main challenges and provide a brief description of promising research directions to draw the attention of concerned stakeholders and parties in both the blockchain and edge computing domains.

title: Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing
abstract: The vehicular networks constructed by interconnected vehicles and transportation infrastructure are vulnerable to cyber intrusions due to the expanded use of software and the introduction of wireless interfaces. Intrusion detection systems (IDSs) can be customized efficiently in response to this increased attack surface. There has been significant progress in detecting malicious attack traffic using machine learning approaches. However, existing IDSs require network devices with powerful computing capabilities to continuously train and update complex network models, which reduces the efficiency and defense capability of intrusion detection systems due to limited resources and untimely model updates. This work proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices (e.g. connected vehicles and roadside units (RSUs) Distributed federated based approach reduces resource utilization of the central server while assuring security and privacy. To ensure the security of the aggregation model, blockchain is used for the storage and sharing of the training models. This work analyzes common attacks and shows that the proposed scheme achieves cooperative privacy preservation for vehicles while reducing communication overhead and computation cost.