Vehicle road cooperation (VRC) services closely related to personal safety impose stringent requirements on reliability and real-time performance. However, the growing trend of vehicle to network (V2N) connections has significantly intensified the threat of distributed Denial of Service (DDoS) to the end-cloud communication links. To address this challenge, this article introduces a comprehensive DDoS defense architecture for VRC, named in-network DDoS-oriented Balancer, Inspector, and Filter (IDBIF). Leveraging programmable switches, IDBIF adopts an in-network mode to dynamically mitigate volumetric DDoS traffic in real-time, which coordinates three data plane functions for traffic processing: 1) load balancing; 2) packet inspection; and 3) traffic filtering. Our work faces the starved data plane resources and establishes the problem model for high reliability requirements of VRC services when subjected to DDoS attacks. Furthermore, we developed a multiagent hierarchical structure policy gradient (MAHSPG) algorithm that combines the inherent characteristics of DDoS attacks to tackle the runtime deployment of high-dimensional and heterogeneous defense function chains. Simulation experiment results show that the proposed approach alleviates the data plane resource bottleneck and improves the DDoS defense effect by 24.80%, highlighting its advantages in ensuring reliable VRC services.

Large-scale models, also referred to as pretrained models, have attracted significant attention due to their outstanding performance and robust generalization. However, the high demands for data quality, stringent data privacy and security requirements, and limited computational and communication resources have imposed restrictions on the further development of large-scale models. Federated Learning (FL) is a popular machine learning framework that can effectively address this issue. However, when collaboratively training large-scale models using FL, local training and the transmission of large-scale parameters impose significant computational and communication burdens on mobile devices. This paper proposes a light-weight and high-efficiency federated learning framework (FedLH) for large-scale models. This framework divides large-scale models into semantic block-based submodels, allowing clients to transmit these submodels to the server for heterogeneous aggregation. This approach enables both communication and computational efficiency. With the proposed framework, each device can learn personalized, structured sparse models that can efficiently run on terminal devices. Experimental results demonstrate that FedLH outperforms other baseline algorithms by significantly reducing the number of training parameters and transmitted data. It also exhibits strong generalization and scalability.

The advancement of information and telecommunication technology has resulted in lots of computing power service providers (CSPs) sharing resources. This effectively improves the utilization of computing resources and provides opportunities for users to find best network services. However, with the increase of similar or identical services, traditional service provision mechanisms become more complicated and face more challenges. To provide on-demand services for users and trusted service sharing platform for CSPs, we propose a blockchain-based distributed network service provision (DNSP) mechanism. Based on blockchain technology, we design a distributed network service (BBDNS) architecture and introduce trusted QoS model for users and profit model for CSPs. Meanwhile, we develop a bi-objective optimization problem, called BP-MATCH, to balance benefits between users and CSPs. Then, we define DNSP mechanism based on smart contracts. Furthermore, to solve BP-MATCH, we design Kuhn-Munkres based service matching algorithm (KM-SMA) and ant colony optimization-based service matching algorithm (ACO-SMA). Finally, we conduct a simulation experiment based on the generated dataset. Simulation results show that the proposed algorithm can obtain the optimal service matching decision under certain conditions, and DNSP mechanism can guarantee the efficiency of service decisions while providing trusted distributed service.

As the future trend, more and more vehicles access to the Internet of Vehicles, which means that a huge number of tasks of the vehicle terminals need to be transformed and completed on the network. Edge computing makes the tasks executed on the edge nodes near the terminal, but some vehicle terminals are at a relatively idle state and these additional computing resources are not utilized, causing great waste of resources. What is more, it is hard to highly and comprehensively satisfy the high real-time requirements of some tasks. In order to execute these tasks efficiently, we propose a dynamic edge–end computing collaboration architecture for urban IoV. In this architecture, edge nodes and vehicle terminals can cooperate with each other, which means tasks can be allocated more dynamically and flexibly. We evaluate the completion of the task by considering task latency and overhead, task transmission model, task priority, as well as edge node and vehicle terminal’s capacity when defining task comprehensive utility. Then weformulate the task allocation as an optimization problem and propose an improved quantum particle swarm optimization algorithm to solve the problem. Simulation results show that the proposed strategy have better task allocation utility than other strategies, which can effectively solve the multi task allocation problem.

The traditional centralized resource scheduling method leads to trust issues among multiple subjects carrying microservices. At the same time, in the process of service provision, single-point failure problems also occur from time to time. In order to realize the trusted provision of services, we build a blockchain-based distributed intelligent service trusted provision architecture, which uses smart contracts to realize the on-chain registration of resource information and automatic orchestration of microservices. In order to break through the bottleneck of blockchain efficiency and improve scalability, we use sharding technology to expand the blockchain. And the Raft-practical Byzantine fault-tolerance two-level consensus mechanism combining the Boneh–Lynn–Sacham (BLS) threshold signature (B-RBFT) is designed for blockchain sharding, which greatly improves throughput and reduces consensus delay while taking security into account. To meet higher Quality-of-Service (QoS) requirements, we design the microservice orchestration algorithm based on the improved double deep$Q$network (DDQN) to support microservice deployment and migration. In particular, to make the neural network converge faster, we improve the traditional DDQN framework by using double replay buffers and weighted target values. Simulation results show that our proposed algorithm has advantages in convergence speed, resource usage cost, delay, and load balancing.

The 6G wireless network aims to forge a new spectrum, high technical standards of high time and phase synchronization accuracy, and 100% geographic coverage to connect trillions of devices flexibly and efficiently in the future. However, as connectivity increases and applications become novel, it is a challenge to ensure the privacy and security of networks and applications. Blockchain is seen as a promising technology that can improve efficiency, reduce costs, mitigate security, and privacy threats, and establish a trusted data-sharing environment. This article presents a trusted framework based on blockchain technology from the perspective of how to build a trusted software-defined content delivery network. As the peer node of the blockchain, the software-defined network (SDN) controller establishes trust between different regions and a wide range of participants, realizing peer autonomy and flexible business orchestration. The two main purposes of the architecture are to enhance the security of network communications and establish trust relationships between entities in different domains. It includes trusted communication based on routing sandbox, service choreography based on blockchain, proxy server selection strategy based on model predictive control (MPC), and optimization consensus based on practical Byzantine fault tolerance. Some simulation experiments verify the effectiveness of the theoretical method.

IEEE 802.11ax, as a new generation of wireless local area network (WLAN) standard, has great development potential for multi-service access in industrial WLAN with its characteristics of low-cost, quick-deployment and high-efficiency. However, it still faces the problem of quick and efficient access point (AP) seamless handover for multi-service communications due to terminal mobility and channel degradation. To solve the problem, combining the characteristics of orthogonal frequency division multiple access (OFDMA) technology and multi-user multiple-input multiple-output (MU-MIMO) technology, this paper firstly proposes an edge computing based IEEE 802.11ax multi-service bearer mechanism by dynamically allocating channel resource units (RUs). Secondly, a multi-service AP seamless handover mechanism based on edge computing is proposed, which consists of a channel scanning delay optimization method based on virtual AP sentinel, a handover trigger judgment algorithm considering mobility and RU reallocation, a multi-objective optimized AP handover model considering load balancing, transmission rate optimization and handover cost minimization with service attributes. Finally, we propose a multi-service AP seamless handover algorithm based on improved particle swarm optimization (PSO) to get optimized handover making-decision. Simulation results show that the proposed handover mechanism can reduce the handover delay significantly while realizing seamless handover accurately in multi-service industrial WLAN.

The rapid development of the Internet of Things has put forward higher requirements for the processing capacity of the network. The adoption of cloud edge collaboration technology can make full use of computing resources and improve the processing capacity of the network. However, in the cloud edge collaboration technology, how to design a collaborative assignment strategy among different devices to minimize the system cost is still a challenging work. In this paper, a task collaborative assignment algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Firstly, the task collaborative assignment framework of cloud edge collaboration is constructed. Secondly, the problem of task assignment strategy was transformed into a function optimization problem with the objective of minimizing the time delay and energy consumption cost. To solve this problem, a task assignment algorithm combining the improved genetic algorithm and simulated annealing algorithm was proposed, and the optimal task assignment strategy was obtained. Finally, the simulation results show that compared with the traditional cloud computing, the proposed method can improve the system efficiency by more than 25%.

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.

Blockchain has broad application prospects in Smart City, and the technical characteristics of the blockchain itself can solve the problems of mistrust of network resource production relations and unfair distribution of revenue. However, most of the devices in Smart City are mobile devices with insufficient resources. The demand for computing power of the mining process cannot be met. For this, we introduce mobile edge computing, deploy edge servers on the edge side, and provide resources for mobile devices. We build an edge network resource allocation model for mobile blockchain to realize the effective application of blockchain technology in mobile environments. The resources required by the mining process can be obtained from neighboring resource sharing devices or edge servers. The resource allocation between adjacent devices can be modeled as a two-way auction model, and the Bayesian- Nash equilibrium is solved to determine the optimal price, while considering the trusted value of the device; the process of the mobile device acquiring resources from the edge server can be modeled as a two-stage Stackelberg game. Finally, simulation experiments show that this mechanism achieves a higher personal utility than an existing model that only considers requesting resources from an edge server.

With the development of wireless local area networks and intelligent transportation technologies, the Internet of Vehicles is considered to be an effective method to alleviate the severe situation of the current transportation system. The vehicles in the Internet of Vehicles system build the Vehicular Ad Hoc Networks through wireless communication technology and dynamically provide different services through the real-time driving information broadcast by the vehicles. Vehicle drivers can control the distance, planning the driving route, between vehicles according to the current traffic environment, which improves the overall safety and efficiency of the traffic system. Due to the particularity of the Internet of Vehicles system service, vehicles need to broadcast their location information frequently. Attackers can collect and analyze vehicle broadcast information to steal privacy and even directionally track the owner through the driving trajectory, bringing serious security risks. This paper proposes a blockchain-based privacy protection system for the Internet of Vehicles. The system combines the blockchain with the Internet of Vehicles system to design a safe and efficient two-way authentication and key agreement algorithm through encryption and signature algorithm, which also solves the central dependency problem of the traditional Internet of Vehicles system.

Data collection communications system for electric power communication network is an important entity network in electric power system. It's significant to provide a reasonable routing mechanism for data collection system. The major risk of original routing mechanism is the burst congestion. In the data collection network of smart grid, that some nodes may suffer from more congestion than others even become the bottleneck of network become the new source of risk. In order to overcome these problems, this paper proposes a routing mechanism to realize data balance. This mechanism firstly abstracts a multigate network model of data collection system. Then, it proposes a routing measurement model to balance data traffic named data balance measurement model (DBMM). Subsequently it proposes a routing algorithm based on DBMM, named RA-DBMM. The RA-DBMM handles the data traffic to achieve data balance by modifying the routing decision measurement function. So the queue length of queuing data and buffer capacity of every nodes are taken into consideration. Simulation illustrates that the proposed RA-DBMM has biggish enhancement in minishing the congestion, reducing loss ratio, overcoming bottleneck and increasing throughput of network.

Because of the advantages of multi-hop communication, self-organizing, self-healing and reliability, wireless mesh network becomes an ideal choice for data collection. However, wireless mesh network for data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occurs in emergence, some mesh nodes (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion, especially in smart grid. For the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on data collection tree. Simulation data show that the new algorithm can create a balanced data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

One of the key technologies of smart grid is an efficient, reliable and secure two-way communication system for meter data collection. Because of the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop communication technology becomes an ideal choice for smart grid meter data collection. However, forming wireless mesh network with advanced electricity devices (smart meters) which have the communication capabilities for meter data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occur in emergence, some smart meters (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. With the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on meter data collection tree. Simulation data show that the new algorithm can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.