With the rapid development of vehicle-to-everything (V2X) technology, service migration has become an important approach to provide low-latency computing services and ensure service continuity for high-speed moving vehicles in vehicular edge computing (VEC), which enables VEC to efficiently support advanced transportation services. However, optimizing service satisfaction for service migration in multi-vehicle heterogeneous VEC networks is challenging, since the complex, multifactorial, and nonlinear dependencies between service satisfaction and quality of service (QoS) metrics is intractable, and the rapidly changing computational loads in edge server results in inefficient utilization of edge resources. In this paper, we propose a service Satisfaction-based Adaptive service Migration and resource Allocation joint Optimization scheme (SAMAO) to improve service migration efficiency and edge resource utilization in VEC. Firstly, we develop an adaptive computation resource allocation algorithm that can adjust resource allocation strategy according to load status of edge servers to improve vehicle service satisfaction. Then, to minimize energy consumption and ensure service satisfaction for vehicles, we propose a utility maximization algorithm to formulate migration decisions based on pre-allocated computation resources on servers. Finally, numerous simulations based on Shanghai Telecom real-world dataset show that SAMAO can achieve significant advantages in terms of average service satisfaction and computation cost.

Traditional computation offloading and resource allocation strategies encounter several issues that lead to poor service experience and resource wastage. The resource allocation scheme lacks the flexibility to adapt to the time-varying offloading demands of User Equipment (UEs). Furthermore, there is an imbalance between UEs seeking better service and Service Providers (SPs) aiming to minimize cost expenditures. In this paper, we propose a knowledge-defined networking-based Multi-Layer Computation Offloading and Resource Allocation strategy optimization (ML-CORA) architecture. Based on the ML-CORA, we design a Multi-Layer Local-Global Collaborative computation offloading and resource allocation strategy optimization (ML2GC) algorithm. The basic level of the ML2GC algorithm expresses and optimizes computation offloading demands from the perspective of UE (local), while the meta level optimizes the resource allocation strategy on demand from the perspective of the SP (global), achieving a collaborative multi-objective optimization for a win-win system between UEs and SPs. The two-layer structure of the ML2GC algorithm outputs continuous and discrete actions respectively, which improves the flexibility and efficiency of the algorithm while effectively balancing the interests of all parties and promoting efficient resource utilization. Simulation results based on the real-world dataset of Shanghai Telecom indicate that the ML2GC algorithm significantly improves both social welfare and resource utilization compared to baseline algorithms.

In large-scale dynamic network environments, optimizing the computation offloading and resource allocation strategy is key to improving resource utilization and meeting the diverse demands of User Equipment (UE). However, traditional strategies for providing personalized computing services face several challenges: dynamic changes in the environment and UE demands, along with the inefficiency and high costs of real-time data collection; the unpredictability of resource status leads to an inability to ensure long-term UE satisfaction. To address these challenges, we propose a Knowledge-Defined Networking (KDN)-based Adaptive Edge Resource Allocation Optimization (KARO) architecture, facilitating real-time data collection and analysis of environmental conditions. Additionally, we implement an environmental resource change perception module in the KARO to assess current and future resource utilization trends. Based on the real-time state and resource urgency, we develop a deep reinforcement learning-based Adaptive Long-term Computation Offloading and Resource Allocation (AL-CORA) strategy optimization algorithm. This algorithm adapts to the environmental resource urgency, autonomously balancing UE satisfaction and task execution cost. Experimental results indicate that AL-CORA effectively improves long-term UE satisfaction and task execution success rates, under the limited computation resource constraints.

Traditional routing algorithms cannot dynamically change network environments due to the limited information for routing decisions. Meanwhile, they are prone to performance bottlenecks in the face of increasingly complex business requirements. Some approaches, such as deep reinforcement learning (DRL) have been proposed to address the routing problems. However, they hardly utilize the information about the network environment fully. The Knowledge Defined Networking (KDN) architecture inspires us to develop new learning mechanisms adapted to the dynamic characteristics of the network topology. In this paper, we propose an effective scheme to solve the routing optimization problem by adding a graph neural network (GNN) structure to DRL, called Message Passing Deep Reinforcement Learning (MPDRL). MPDRL uses the characteristics of GNN to interact with the network topology environment and extracts exploitable knowledge through the message passing process of information between links in the topology. The goal is to achieve the load balance of network traffic and improve network performance. We have conducted experiments on three Internet Service Provider (ISP) network topologies. The evaluation results show that MPDRL obtains better network performance than the baseline algorithms.

The prediction of user trajectories is a fundamental component to support urban traffic management and various advanced transportation applications, such as traffic optimization and location-based services. Trajectory data typically contains multiple behavioral patterns and contexts, including different travel purposes, modes of transportation, time intervals, and geographic regions. These complex factors collectively influence the prediction of user trajectories. However, trajectory prediction models face challenges in effectively distinguishing between these various patterns. In this paper, we propose a novel stack Transformer-based multimodal long-term trajectory prediction (SMTTP) scheme for heterogeneous user behavior patterns. First, a learnable trajectory similarity measure method is proposed to estimate the relative distance between multi-attribute variable-length trajectories. Then, to address the instability of trajectory clustering caused by random initialization, a cluster head initialization algorithm based on high confidence nodes is developed to improve clustering stability and reduce convergence time. In addition, a Transformer-based trajectory prediction model with multi-dimensional feature fusion is proposed to achieve accurate and efficient long-term trajectory prediction. Experimental results on the real telecom dataset in Shanghai, China show that the proposed SMTTP scheme can achieve improved performance in trajectory prediction in terms of prediction error, and also has high accuracy and stability in unsupervised trajectory clustering.

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

Software defined networking (SDN) have the advan-tages of centralized control, global visibility, and programmabil-ity, but these features also bring new security issues, such as Topological Poisoning Attack (TPA), where attackers can attack topology discovery services by stealing host locations or forging link information. Considering the three levels of identity, data package and path, this paper designs a chain authentication defense scheme. The scheme includes authentication mechanism, transaction information storage mechanism, source IP authenti-cation mechanism and smart contract notification mechanism. The received packets are authenticated by digital signature algorithm, and the trusted identity and location information are stored securely. At the same time, an improved block storage structure is designed to avoid data redundancy, and malicious information is processed by smart contract notification and stream rule installation. The experimental results show that the defense scheme designed in this paper can effectively defend against TPA attacks. Compared with the benchmark mechanism, the deployment of this scheme has less impact on controller performance and less impact on the delay of topology discovery in SDN.

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).