1. Introduction

With the rapid development of mobile communication and intelligent transportation technologies, Vehicular Ad hoc NETwork (VANET) has emerged as an important part of the intelligent transportation system (ITS). VANET is a unique type of Mobile Ad hoc NETwork (MANET). The similarity between VANET and MANET is characterized by the movement and self-organization of nodes. However, the primary difference is that the nodes in VANETs are mobile vehicles; they move at much higher speeds and possess substantial power resources, which is an advantage over traditional MANETs. As a newly introduced technology, the goal of VANET research is to develop vehicle-to-vehicle (V2V) communications to enable quick and cost-efficient data sharing for enhanced vehicle safety and comfort [1–3].

Typically, vehicles send and receive messages using an on-board unit (OBU), which is a portable data collector hardware installed on the vehicle. By a combination of information technology and transport infrastructure, VANETs support a wide range of applications including traffic control, road safety, route optimization, multimedia entertainment, and social interactions. With the advancement in the latest vehicular technology, automobile manufacturers and academia are heavily engaged in the blueprint of VANETs. Currently, 10% of moving vehicles on the road are wirelessly connected. By 2020, it is reported that 90% of vehicles will be connected [1, 4, 5].

In VANET systems, each vehicle can exchange messages in V2V communications while acting as a sender, receiver, and router. In addition, these vehicles perform vehicle-to-roadside (V2R) communications with roadside units (RSUs) [6]. The RSU operates as an access point to provide services to moving vehicles at anytime and anywhere while connecting to the backbone network. As Internet access points, RSUs provide the communication connectivity to passing vehicles. In VANET environments, RSUs and OBUs in vehicles repeatedly interact with each other and perform control decisions individually without centralized coordination [1,4–9].

During VANET operations, services can be categorized into three fundamental classes: vehicular cloud service (VCS), vehicular sensing service (VSS), and vehicular routing service (VRS). Vehicular cloud service is a new paradigm that exploits cloud computing to serve VANETs with several computational services [10]. Currently, computing requirements for vehicular applications are increasing rapidly, particularly with the growing interest in embedding a new class of interactive applications and services. Within the communication range of each RSU, the RSU controls the VCS through interactions with the vehicles and provides computation capacity, storage, and data resources. Vehicles requiring resources can obtain the available resources via interconnected RSUs. Therefore, depending on the roadside infrastructure, the VCS provides a significant market opportunity and overcomes challenges, making the technology more cost-effective [10, 11].

With the rapid development of VANETs, VSSs have broad potential for enhancing the public sensing domain. Vehicles with OBUs can be considered as mobile sensors that collect the local information requested by the RSUs. As a server, the RSU pays for the VSS’s participating vehicles according to their sensing qualities. As a type of vehicle-to-infrastructure communication, examples of VSSs are location-based road monitoring, real-time traffic condition reporting, pollution level measurements, and public information sharing. Owning to the mobility of the vehicles, the sensing coverage of location-based services can be expanded, and VSSs will benefit a wide scope of consumers [7, 8, 12].

In VANETs, vehicles are equipped with OBUs that can communicate with each other, also known as VRS. The VRS requires the suitable V2V communication technology, especially the routing method to address various challenges of VANETs. Typically, VANET is considered a subclass of MANET, and MANET-based routing protocols have been extensively studied. However, the VANET has several characteristics that distinguish it from MANET. To obtain adaptive vehicular routing protocols, high mobility, rapid network topology change, and an unlimited power supply must be considered to design an effective VRS algorithm [13–15].

In this study, we propose a new VANET control scheme, by considering the issues of VCS, VRS, and VSS. Based on the joint design of different vehicular operations, our integrated approach can obtain a synergy effect while attaining an appropriate performance balance. However, it is an extremely challenging task to combine the VCS, VRS, and VSS algorithms into a holistic scheme. Therefore, a new control paradigm is required. Currently, game theory has been widely applied to analyze the performance of VANET systems. More specifically, VANET system agents are assumed as game players, and the interaction among players can be formulated as a game model [6].

2. Related Work

There have been considerable researches into the design of VANET control schemes. Cheng et al. [16] investigate the opportunistic spectrum access for cognitive radio vehicular ad hoc networks. In particular, the spectrum access process is modeled as a non-cooperative congestion game. Therefore, the proposed scheme in [16] opportunistically accesses licensed channels in a distributed manner and is designed to achieve a pure Nash equilibrium with high efficiency and fairness. By using the statistics of the channel availability, this approach can exploit the spatial and temporal access opportunities of the licensed spectrum. Through the simulation results, they conform that the proposed approach achieves higher utility and fairness compared to a random access scheme [16].

The paper [17] focuses on how to stimulate message forwarding in VANETs. To implement this mechanism, it is crucial to make sure that vehicles have incentives to forward messages for others. Especially, this study adopts the coalitional game theory to solve the forwarding cooperation problem and rigorously analyzing it in VANETs. The main goal is to ensure that whenever a message needs to be forwarded in a VANET, all involved vehicles gets their incentives to form a grand coalition. In addition, this approach is extended to taking the limited storage space of each node. Experiments on testbed trace data verify that the proposed method is effective for the stimulating cooperation of message forwarding in VANETs [17].

In [18], the main focus is to achieve a higher throughput by selecting the best data rate and the best network or channel based on the cognitive radio VANET mechanism. By changing wireless channels and data rate in heterogeneous wireless networks, the scheme in [18] is designed as a game theoretic model to achieve a higher throughput for vehicular users. In addition, a new idea adopted to find optimal number of APs for given VANET scenarios [18]. Even though the papers [16–18] have considered some game theory-based VANET control algorithms, they are designed as one-sided protocols while focusing a specific control issue. Therefore, the system interaction process is not fully investigated.

Recently, the vehicular microcloud technique has been considered one of the solutions to address the challenges and issues of VANETs. It is a new hybrid technology that has a remarkable impact on traffic management and road safety. By instantly clustering, cars help aggregating collected data that are transferred to some backend [19, 20]. The paper [19] introduces the concept of vehicular microcloud as a virtual edge server for efficient connections between cars and backend infrastructure. To realize such virtual edge servers, map-based clustering technique is needed to cope with the dynamicity of vehicular networks. This approach can optimize the data processing and aggregation before sending them to a backend [19].

F. Hagenauer et al. propose an idea that vehicular microcloud clusters of parked cars act as RSUs by investigating the virtual vehicular network infrastructure [20]. Especially, they focus on two control questions: (i) the selection of gateway nodes to connect moving cars to the cluster and (ii) a mechanism for seamless handover between gateways. For the first question, they select only a subset of all parked cars as gateways. This gateways selection helps significantly to reduce the channel load. For the second question, they enable driving cars to find better suited gateways while allowing the driving car to maintain connections [20]. The ideas in [19, 20] are very interesting. However, they can be used for special situations. Therefore, it is difficult to apply these ideas in general VANET operation scenarios.

During VANETs’ operations, there are two fundamental techniques to disseminate data for vehicular applications: Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Recently, Kim proposes novel V2I and V2V algorithms for the effective VANET management [6]. For V2I communications, a novel vertical game model is developed as an incentive-based crowdsensing algorithm. For V2V communications, a new horizontal game model is designed to implement a learning-based intervehicle routing protocol. Under dynamically changing VANET environments, the developed scheme in [6] is formulated as a dual-plane vertical-horizontal game model. By considering the real-world VANET environments, this approach imitates the interactive sequential game process by using self-monitoring and distributed learning techniques. It is suitable to ensure the system practicality while effectively maximizing the expected benefits [6].

In this paper, we develop a novel control scheme for vehicular network services. Our proposed scheme also includes sensing and routing algorithms for the operations of VANETs. Therefore, our proposed scheme may look similar to the method in [6]. First of all, the resemblance can be summarized as follows: (i) in the vehicular routing algorithm, link status and path cost estimation methods and each vehicle’s utility function are designed in the same manner; (ii) in the crowdsensing algorithm, utility functions of vehicle and RSU are defined similarly; and (iii) in the learning process, strategy selection probabilities are estimated based on the Boltzmann distribution equation. While both schemes have some similarities, there are several key differences. First, the scheme in [6] is designed as a dual-plane game model, but our proposed scheme is extended as a triple-plane game model. Second, a new cloud computing algorithm is developed to share the cloud resource in the RSU while increasing the quality of service. Third, to reduce computation complexity, entropy-based routing route decision mechanism is replaced by an online route decision mechanism. Fourth, the scheme in [6] is modeled as a non-cooperative game approach. However, our proposed scheme adopts a dynamic bargaining solution. Therefore, in our proposed sensing algorithm, each individual vehicle decides his strategy as a cooperative game manner. Fifth, our proposed scheme fully considers cloud computing, sensing, and routing payoffs and calculates the bargaining powers to respond to current VANET situations. Most of all, the main difference between the scheme in [6] and our proposed scheme is a control paradigm. Simply, in the paper [6], V2I and V2V algorithms are combined merely based on the competitive approach. However, in this paper, sensing, cloud computing, and routing algorithms work together in a collaborative fashion and act cooperatively with each, other based on the interactive feedback process.

The Multiobjective Vehicular Interaction Game (MVIG) scheme [21] proposes a new game theoretic scheme to control the on-demand service provision in a vehicular cloud. Based on a game theoretic approach, this scheme can balance the overall game while enhancing vehicles’ service costs. The game system in the MVIG scheme differs from other conventional models, as it allows vehicles to prioritize their preferences. In addition, a quality-of-experience framework is also developed to provision various types of services in a vehicular cloud; it is a simple but an effective model to determine vehicle preferences while ensuring fair game treatment. The simulation results show that the MVIG scheme outperforms other conventional models [21].

The Prefetching-Based Vehicular Data Dissemination (PVDD) scheme [22] devises a vehicle route-based data prefetching approach while improving the data transmission accessibility under dynamic wireless connectivity and limited data storage environments. To put it more concretely, the PVDD scheme develops two control algorithms to determine how to prefetch a data set from a data center to roadside wireless access points. Based on the greedy approach, the first control algorithm is developed to solve the dissemination problem. Based on the online learning manner, the second control algorithm gradually learns the success rate of unknown network connectivity and determines an optimal binary decision matrix at each iteration. Finally, this study proves that the first control algorithm could find a suboptimal solution in polynomial time, and the optimal solution of the second control algorithm converges to a globally optimal solution in a certain number of iterations using regret analysis [22].

The Cooperative Relaying Vehicular Cloud (CRVC) scheme [23] proposes a novel cooperative vehicular relaying algorithm over a long-term evolution-advanced (LTE-A) network. To maximize the vehicular network capacity, this scheme uses vehicles for cooperative relaying nodes in broadband cellular networks. With new functionalities, the CRVC scheme can (i) reduce power consumption, (ii) provide a higher throughput, (iii) lower operational expenditures, (iv) ensure a more flexibility, and (v) increase a covering area. In a heavily populated urban area, the CRVC scheme is useful owning to a large number of relaying vehicles. Finally, the performance improvement is demonstrated through the simulation analysis [23]. In this paper, we compare the performance of our proposed scheme with the existing MVIG [21], PVDD [22], CRVC [23] schemes.

3. The Proposed VCS, VRS, and VSS Algorithms

In this section, the proposed VCS, VRS, and VSS algorithms are presented in detail. Based on the learning algorithm and game-based approach, these algorithms form a new triple-plane bargaining game model to adapt the fast changing VANET environments.

4. Performance Evaluation

4.2. Results and Discussion

Figure 1 compares the cloud service success ratio of each scheme. In this study, the cloud service success ratio represents the rate of cloud services that was completed successfully. This is a key performance evaluation factor in the VCS operation. As shown in Figure 1, the cloud service success ratios of all schemes are similar to each other; however, the proposed scheme adopts an interactive environmental feedback mechanism, and the RSUs in our scheme adaptively adjust their VCS costs. This approach can improve the VCS performance than the existing MVIG, PVDD, and CRVC schemes. Therefore, we outperformed the existing methods from low to high vehicle distribution intensities.

Figure 2 compares the normalized dissemination throughput in VANETs. Typically, the network throughput is measured as bits per second of network access. In this study, the dissemination throughput is defined as the ratio of data amount successfully received in destination vehicles to the total generated data amount in the source vehicles. The throughput improvement achieved by the proposed scheme is a result of our $$ game paradigm. During the VRS operations, each vehicle in the proposed scheme can select the most efficient routing path with real-time adaptability and self-flexibility. Hence, we attained a higher dissemination throughput compared to other existing approaches that are designed as lopsided and one-way methods and do not effectively adapt to the dynamic and diversified VANET conditions.

The crowdsensing success probability, which is shown in Figure 3, represents the efficiency of the VANET system. In the proposed scheme, we employed the learning-based triple-plane game model to perform control decisions in a distributed online manner. According to the interactive operations of the VCS, VSS, and VRS, our game-based approach can improve the crowdsensing success probability more effectively than the other schemes. The simulation results shown in Figures 1 to 3 demonstrate that the proposed scheme can attain an appropriate performance balance; in contrast, the MVIG [21], PVDD [22], and CRVC [23] schemes cannot offer this outcome under widely different and diversified VANET situations.

5. Summary and Conclusions

The VANET using vehicle-based sensory technology is becoming more popular. It can provide vehicular sensing, routing, and clouding services for 5G network applications. Therefore, the design of next-generation VANET management schemes is important to satisfy the new demands. Herein, we focused on the paradigm of a learning algorithm and game theory to design the VCS, VRS, and VSS algorithms. By combining the VCS, VRS, and VSS algorithms, a new triple-plane bargaining game model was developed to provide an appropriate performance balance. During the VANET operations, the RSUs learned their strategies better under dynamic VANET environments, and the vehicles considered the mutual-interaction relationships of their strategies. As game players, they considered the obtained information to adapt to the dynamics of the VANET environment and performed control decisions intelligently by self-adaptation. According to the unique features of VANETs, our joint design approach is suitable to provide satisfactory services under incomplete information environments. In the future, we would like to consider privacy issues such as the differential privacy during the VANET operation. Furthermore, we will investigate probabilistic algorithms to estimate the service quality of sensing, routing, and clouding services. In addition, we plan to investigate smart city applications, where the different sensory information of a given area can be combined to provide a complete view of the smart city development.

Conflicts of Interest

The authors declare that they have no conflicts of interest.