Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multi-user, multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations.

The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a promising paradigm to provide high-performance vehicle services by offloading the applications to edge servers. However, it is still a challenge to efficiently utilize the available resources of vehicle nodes. In this article, we introduce mobile edge computing technology to vehicular ad hoc network to build a vehicular edge computing system, which provides a wide range of reliable services by utilizing the computing resources of vehicles on the road. Then, we study the computation offloading decision problem in this system and propose a novel multi-objective vehicular edge computing task scheduling algorithm which jointly optimizes the allocation of communication and computing resources. Extensive performance evaluation demonstrates that the proposed algorithm can effectively shorten the task execution time and has high reliability.

Due to the complexity and fluctuation of the wireless network statuses in the high-speed railway scenario, the existing works of the network selection problem in heterogeneous wireless networks face two major challenges, that is, the imprecise statuses and mobility. In this paper, we propose FSNS, a novel dynamic imprecise-aware network selection approach to solve the problems. The imprecise statuses are inferred by fuzzy rules and the status-awareness feature of the status monitor module enables a dynamic network selection. Through the status monitor and fuzzy processing modules, FSNS is formulated as utility functions for meeting quality-of-service (QoS) requirements. What is more, we carry out plenty of simulation experiments and compare FSNS approach with an enhanced TOPSIS method and fuzzy MADM (FMADM) scheme through simulation experiments of two types of services. The results indicate that FSNS outperforms both TOPSIS and FMADM for a good performance improvement and a preferable decision to keep relative stability and reduce abnormal selections. The conclusion of experimental results has some extent pragmatic value since the network statuses of the simulation are complex and fluctuate by real-world data from high-speed railways.

Accessing Internet services in high-speed mobile scenario is an increasing demand for passengers and vendors. Owing to the bandwidth limitation of a single wireless network, researchers attempt to utilize the heterogeneous wireless networks along tracks to achieve multipath parallel transmission. These multipath transmission schemes usually depend on accurate estimation of network quality to achieve high performance. However, due to the unpredictability of wireless networks in high-speed mobile scenario, current multipath transmission schemes perform poorly. In this paper, firstly, we make quantitative analysis for the unpredictability of wireless networks. With lots of results of real experiments, we make quantitative analysis for the estimation error of classical algorithms in different scenarios. Secondly, aiming at the unpredictability of wireless networks, we propose a multipath transmission algorithm named Receiver Adaptive Incremental Delay (RAID) that can aggregate bandwidth for heterogeneous networks independent of accurate network quality estimation. Thirdly, we deploy the RAID algorithm into a real system. Abundant of real experiments and simulations prove that our proposed algorithm has a better performance than the Earliest Completion First (ECF) algorithm and the Weighted Round Robin (WRR) algorithm in high-speed mobile scenario.

Nowadays, multipath transmission scheme in heterogeneous vehicular networks has become an emerging topic. It is a great challenge to overcome the unreliability of heterogeneous wireless network in vehicle-to-ground multipath communication. Many multipath transmission schemes were proposed. However, most schemes do not consider the unreliability of wireless networks and are difficult to deploy in vehicle-to-ground communications. Even though part of the multipath transmission schemes consider the unreliability of wireless networks, their overhead is too large to be deployed in vehicle-to-ground multipath communication. Therefore, we propose a BigNum Network Coding (BNNC) scheme for vehicle-to-ground multipath communication. Compared with the Opportunistic Routing (OR) scheme, BNNC’s network resource overhead is smaller. Compared with other network coding schemes, it is a better trade-off between coding flexibility and codec efficiency. In this paper, we propose a brand-new mathematical model for network coding which can effectively improve the reliability of the vehicular networks. Secondly, based on the mathematical model, we design BNNC multipath transmission scheme. Compared with the current network coding scheme, the BNNC scheme considers coding flexibility and codec efficiency while ensuring multipath transmission reliability. Thirdly, we compare BNNC scheme with many current multipath transmission schemes through lots of simulations and real tests. The results show that the BNNC scheme is significantly superior to the other network coding schemes in terms of computational performance. And in terms of the network performance, the BNNC scheme can overcome the unreliability of wireless networks in multipath transmission and it has lower overhead than OR scheme.

Greater demands are being placed on the access bandwidth, stability, and delay of network because of the quickening rhythm of life and work, especially in mobile scenario. In order to obtain a stable network with low latency and high bandwidth in mobile scenario, taking advantage of the wireless heterogeneous network in parallel is a good choice. Nowadays, people are increasingly concerned about the network quality under the mobile scenario. Some scholars have done the relevant measurements. However, all of those measurements mainly investigate part of the network parameters or part of mobile scenarios. In this paper, we make the following contributions. Firstly, in high‐speed mobile scenario, the wireless network qualities of different vendors are measured synthetically. Secondly, we analyze the benefits of taking advantage of the different vendors. Thirdly, we deploy the replication link mechanism in high‐speed mobile scenario and propose an algorithm to remove the duplicate packet in high‐speed mobile scenario. And the algorithm can also be used in another multipath schedule algorithm to improve the reliability.