This paper focuses on the routing algorithm for the communications between vehicles and places in urban vehicular ad hoc networks. As one of the basic transportation facilities in an urban setting, buses periodically run along their fixed routes and cover many city streets. The trajectory of bus lines can be seen as a submap of a city. Based on the characters of bus networks, we propose a bus-trajectory-based street-centric (BTSC) routing algorithm, which uses buses as the main relay to deliver messages. In BTSC, we build a routing graph based on the trajectories of bus lines by analyzing the probability of bus appearing on every street. We propose two novel concepts, i.e., the probability of street consistency and the probability of path consistency, which are used as metrics to determine routing paths for message delivery. This aims to choose the best path with higher density of busses and lower probability of transmission direction deviating from the routing path. In order to improve the bus forwarding opportunity, we design a bus-based forwarding strategy with ant colony optimization to find a reliable and a steady multihop link between two relay buses in order to decrease the end-to-end delay. The BTSC makes improvements in the selection of routing paths and a strategy of message forwarding. Simulation results show that our proposed routing algorithm has a better performance in terms of the transmission ratio, transmission delay, and adaptability to different networks.

Generally, a service request must specify its required virtual network functions (VNFs) and their specific order, which is known as the service function chain (SFC). When mapping SFCs, network providers face many challenges due to the requirements of maintaining the correct order and satisfying other constraints of VNFs. Furthermore, SFC orchestration becomes a more difficult problem when considered in multi-domain networks, because the confidentiality of the topology information of each domain must be considered. In this paper, we study the problem of SFC orchestration across multiple domains. We first use the full mesh aggregation approach to construct an abstracted network to guide the orchestration process, and then propose two efficient methods for SFC partitioning. Based on the SFC partitioning results, we also propose two heuristic algorithms for deploying the sub-chains in multiple domains. Moreover, when the partitioning results cannot be mapped completely, a feedback mechanism is used to repartition the SFC and improve the success ratio of orchestrating the SFC. Finally, to save bandwidth resources, we further improve our heuristic algorithms by migrating the deployment position of VNFs. The simulation results demonstrate that our proposed algorithm achieves better performance compared to existing solutions.

With the popularization and development of cloud computing, network function virtualization and service function chain (SFC) provisioning have attracted increasing attention from researchers. Excellent and reliable network service is important for network development. Moreover, as the number of network users increases, network service construction costs become very high. Therefore, an efficient algorithm is necessary to provide an SFC with excellent performance and low resource costs. In this paper, we re-examine the problem of optimizing the deployment of an SFC to provide users with excellent and resource-saving network service. We propose a heuristic, closed-loop feedback (CLF) algorithm to find the shortest path to map an SFC. To solve the problem, we introduce and integrate a restricted Boltzmann machine and cross entropy to improve the performance of CLF. Simulation results demonstrate the excellent performance of CLF. The communication delay is reduced by approximately 20%, the accept ratio improves by approximately 15%, and the algorithm running time decreases by approximately 20%. In addition, the resource utilization ratio increases by approximately 15%, and the resource fragmentation ratio decreases by approximately 50%.

Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.

A large number of plug-in hybrid electric vehicles (PHEVs) have high mobility but a small battery capacity; thus, these vehicles urgently need to make charging and discharging decisions in real time. Our study proposes a new architecture based on fog computing for an Internet of Vehicles energy trading system, which we call a vehicle-mounted energy fog. This architecture includes a fog computing energy center (FCEC), which manages local energy trading and reduces the peak load energy trading for an external public energy company. We model the optimization problems for energy trading under two different types of FCECs: 1) a non-profit-driven FCEC whose goal is solely to benefit the PHEV charging and discharging operations and 2) a profit-driven FCEC whose goal is to maximize its own profits while still guaranteeing that each PHEV achieves a nonnegative utility. We also propose efficient algorithms for these two types of FCECs to seek optimal pricing and make supply-demand decisions. Simulation results show that our proposed algorithms are superior to existing algorithms in terms of the convergence rate, the final objective value and the evenness of the Pareto solution set. Specifically, the evenness of the Pareto solution set is improved by 23% compared to the results of the existing algorithm.