Clustering is considered as the potential approach for network management in vehicular ad hoc network (VANET). The performance of clustering is often assessed based on the stability of the clusters. Hence, most of the clustering methods aim to establish stable clusters. However, besides the stability of cluster, good link quality must be provided, especially when reliable and high-capacity transmission is demanded. Therefore, this paper proposes a clustering method based on coalitional game theory with the purpose to improve the average of vehicle-to-vehicle (V2V) signal-to-noise ratio (SNR) and channel capacity while maintaining the stability of the cluster. In the proposed method, each vehicle attempts to form a cluster with other vehicles according to coalition value. To attain the purpose of clustering, the value of coalition is formulated based on the V2V SNR, connection lifetime, and speed difference between vehicles. In fast-changing network topology, the higher average of SNR can be achieved but the stability of cluster becomes hard to be maintained. Based on the simulation results, SNR improvement can be adjusted in order to balance with the cluster stability by setting the parameters in the proposed method accordingly. Further simulation results show that the proposed method can obtain a higher average of V2V SNR and channel capacity than other relevant methods.

Vehicular ad hoc network (VANET) is a new form of mobile ad hoc networks comprised of vehicles as the nodes. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are enabled by equipping vehicles with dedicated transceiver device. VANET is provisioned to contribute to intelligent transportation systems by providing a communication link among vehicles to support safety and traffic management purposes as well as infotainment [

Node clustering with the hierarchical topology is considered as the potential approach for network management in VANET. A cluster consists of several vehicle nodes with some similar characteristics which form a communication group. A cluster commonly has one cluster head that acts as the centre of the group and manages the cluster. Other than the cluster head, the ordinary vehicle nodes are called cluster members. Despite the effectiveness of clustering for network management in VANET, clustering has some adversities mainly due to the dynamic environment. In a survey [

Therefore, in this paper, a clustering method for VANET is proposed with the aim to improve the signal-to-noise ratio (SNR) and channel capacity while maintaining the stability of the cluster. The proposed clustering method uses the distributed approach based on coalitional game theory. In general, the coalitional game exhibits the model of interaction between a set of players who attempt to form a group or coalition to reinforce their standing in the game [

As the implementation of the coalitional game in VANET, each vehicle node is considered as a player who attempts to form a coalition (cluster) with other vehicle nodes based on coalitional value. The value of coalition is determined by the revenue and cost. Based on the purpose of the proposed clustering method, the revenue is the link quality or the SNR of V2V connection. Meanwhile, the cost is the variable that decreases the revenue. To obtain the higher SNR, the vehicles need to change the V2V connection frequently. Thus, the stability of the cluster becomes harder to be maintained. For this reason, the cost in the coalitional game is aimed to maintain the stability of the cluster. The cost is formulated based on the connection lifetime and the speed difference between vehicles. The main contributions of this paper and the advantages of the proposed clustering can be described as follows.

A new clustering method for VANET based on coalitional game theory is proposed with the aim to improve SNR and channel capacity. The proposed clustering method also provides the flexibility on balancing between high link quality and stability of the cluster by adjusting some parameters. Thus, the impact of parameters in the proposed method and the guide for adjusting the parameters are also presented in this paper.

The proposed clustering method uses a distributed approach which is favorable due to the flexibility, especially for VANET environment. It is also expected to reduce the workload of the network central, i.e., the cluster head. In this distributed clustering, each vehicle only requires information about the link quality and the speed of neighborhood vehicles. This information is more conveniently provided than the exact position of vehicles. Thus, some assumptions such as the use of GPS can be avoided. The use of GPS is still disputable due to the accuracy problem.

This paper presents the simulation of clustering in VANET using a real-world map and realistic vehicle mobility based on Simulation of Urban Mobility (SUMO). The steps to build the simulation are presented in detail. Simulations of clustering are performed using dedicated program built in MATLAB. Thus, this work can be focused in the physical layer of communication by specifically defining the signal propagation model and the metrics for performance evaluation.

Some research studies related with clustering and game theory in ad hoc networks, especially VANET, are presented in this section. Some technical differences between those research studies and this paper are also presented. The technical differences can be outlined based on the criteria to form a cluster, cluster control (centralized or distributed), cluster head selection procedures, and goal of clustering.

The metric to form a cluster used by most of the clustering methods is the distance between vehicles such as in [

Most of the clustering methods including this research perform clustering in a distributed manner. However, clustering can also be performed using a centralized approach with the aid of infrastructures, such as in [

Cluster head selection in some clustering methods uses the same metrics as in cluster formation, while other methods use more specific metrics for cluster head selection. The specific metric mostly used for cluster head selection is the node connectivity, such as in [

The goal of most clustering methods is to form the stable cluster [

This research proposes the coalitional game theory as the approach for clustering in VANET. Game theory is known as the potential approach for wireless communication and expected to give contribution in VANET development. In [

In this paper, the coalitional game theory is selected due to flexibility to formulate the rule in forming a coalition. Moreover, the concepts of coalition and clustering are compatible. The coalitional game theory has been used in clustering for mobile ad hoc networks (MANETs) [

Research in this paper utilizes the trace data from real-world map. The usage of trace data has become an alternative for simulating VANET environment, and it has an advantage due to the proximity with the real condition. Another related work in VANET using the trace data was presented in [

This paper proposes a multihop clustering method. In multihop clustering, the stability of connection between vehicles is the key factor which determines the stability of the cluster. Therefore, the establishment of V2V connection must be arranged appropriately to attain the longer connection duration and the better link quality. Furthermore, the formation and maintenance of the cluster rely on the established V2V connection. In this proposed method, coalitional game theory is employed to improve the mechanism of V2V connection establishment due to the relevance of the cluster formation model and coalition formation model. The vehicles aim to form cluster by establishing V2V connection with other vehicles which can give mutually good link quality and connection lifetime. This concept is similar to the concept of coalition formation game, where each player selects other players to form group which can give mutual benefit to the group members. Therefore, the coalitional game is considered as the potential approach to be implemented in VANET clustering.

The problem of clustering investigated in this paper can be described as an optimization problem as follows. There are two objectives of the optimization, namely, to improve the SNR of V2V connection and to establish the stable clusters. However, each of those objectives is a trade-off for the other objective. This is due to the dynamic environment in VANET, thus the fluctuation of SNR is high and the vehicles tend to change connection frequently in order to obtain the higher value of SNR. Meanwhile, the frequent change of connection disrupts the stability of cluster. Therefore, this is a complex optimization problem. However, as mentioned above, the coalitional game can be the appropriate approach to model this optimization problem. Here, the variables related with SNR value such as transmission distance and power are included in the revenue of coalition. Meanwhile, the variables influencing the stability of V2V connection as well as the stability of cluster such as the speed difference and connection lifetime are included in the cost of coalition. Finally, the vehicles will decide whether to maintain current connection or change the connection based on the value of coalition which is the difference between revenue and cost of coalition. Therefore, the objective of optimization is directed to maximize the value of coalition. Furthermore, the details about the proposed clustering method including the implementation of coalitional game approach in the clustering process are described as follows. The role of coalitional game approach in the clustering process can be seen in Figure

Procedure in the proposed clustering method.

Coalitional game theory approach is used for establishment of vehicle-to-vehicle (V2V) connection. Therefore, the proposed clustering approach is called coalitional game clustering (CGC). In the coalitional game, players select any other player for cooperating or forming a coalition based on the value they expect to get. The value of the coalition has two elements, i.e., revenue and cost. High value is implied by the high revenue and low cost. The implementation of this approach in V2V connection establishment is described as follows.

The main purpose of this clustering approach is to obtain a higher transmission link quality. Since this research is done in the physical layer of communication, the transmission link quality observed is the signal-to-noise ratio (SNR) and the channel capacity obtained from the establishment of a V2V connection. Channel capacity is proportional to the SNR based on the Shannon–Hartley theorem. Therefore, the proposed revenue function is based on SNR as given by

Cost is considered as the amount or price that reduces the revenue. In this proposed clustering approach, the cost is formulated to increase the lifetime of a V2V connection. In other words, it is aimed to maintain the stability of clusters. In this CGC, the cost consists of two elements as follows.

Connection lifetime (

where

Speed difference (

The two cost elements in (

The reason for using max operator is to allow each element to stand out depending on the condition. For example, when the speed difference is above the threshold (

The value obtained by vehicle

Based on the above formulation,

Once the vehicles select the paired vehicle to establish V2V connections, the clusters are formed. Since V2V connection establishment is arranged in a distributed manner, the formed clusters could be dynamic. The topology of clusters could be multihops and overlapping clusters are possible. Figure

Structure of clusters using CGC.

One of the cost elements in CGC is the speed difference (

In CGC, each vehicle selects another nearby vehicle to establish a V2V connection. In this case, a vehicle can be selected by more than one vehicle. Therefore, in CGC, the electability of vehicles is used as one of the criteria to select the head of a cluster. The vehicle with the highest electability among the cluster members can become the cluster head (CH). Another criterion to select the cluster head is the speed of the vehicle. When there are two or more vehicles with the same electability, then the vehicle with the slower speed becomes the cluster head. Another alternative can be used, i.e., by selecting the vehicle with smaller speed difference to the cluster. However, it requires more computation and adds the transmission overhead. In spite of this, it is not a big problem since the main factor to select the cluster head is the electability, and the additional parameter is used only when there are two or more equal cluster head candidates. Due to the dynamic environment of VANET, the electability of a vehicle can also fluctuate. Therefore, a procedure is added in cluster head selection to increase the lifetime of a cluster head. A vehicle can maintain the state as cluster head as long as there are at least two vehicles connected (has electability at least 2), even though there is another vehicle in the cluster with higher electability. Algorithm

In this system model, the environment of VANET is in toll road with the longest route has length 9 kilometers. The road is traced from the real map, i.e., city toll at Semarang, Indonesia. The longest route starts from (−6.954628, 110.451622) at the north to (−7.026183, 110.432788) at the south. In the middle of the main route around (−6.973131, 110.450006), there is a tollgate where vehicles from either north or south direction must pass through the gate, and thus, the flow of vehicles is slower around this point. The road has 3 lanes for each direction; meanwhile, the tollgate has 4 lanes for each direction. As shown in Figure

Map of the road for VANET simulation.

The traced coordinates of the roads from Google Maps [

The mobility of vehicles is simulated using an open source, microscopic and continuous road traffic simulator, namely, Simulation of Urban Mobility (SUMO) [

Vehicle speed attributes.

Vehicle | Maximum speed (m/s) | Speed factor | Speed deviation |
---|---|---|---|

Car | 40 | 0.75 | 0.2 |

Coach | 30 | 0.375 | 0.1 |

Trailer | 20 | 0.5 | 0.3 |

Distribution of vehicle based on the type of vehicle.

Distribution of vehicle based on the route.

Apart from the maximum speed and speed deviation, the flow of vehicle is also influenced by the lane speed limits. The speed factor as in Table

Screenshot of vehicle mobility simulation in SUMO.

The propagation of the signal from the transmitter vehicle to receiver vehicle is defined by path loss and fading. Path loss defines the attenuation of the signal proportional to the distance between the transmitter and receiver. Fading meanwhile defines the variation either gain or attenuation of the signal due to the surrounding environment. Based on the path loss characterization for vehicular communication in [

In wireless communication, especially when the reused spectrum is used, the interference becomes a generic issue. However, this work has not dealt with the channel allocation. Therefore, the interference is not modeled specifically. For the sake of simplicity, the interference is represented by using additional noise with Gaussian distribution; mean 20 dB; standard deviation of 5 dB. In addition to interference, the communication channel has noise, namely, white noise that has equal intensity at different frequencies. The noises are summed and then used to calculate the SNR (Γ) of V2V connection. For the known SNR value in watts and the bandwidth of channel (

The simulation of vehicle mobility and VANET clustering is performed separately. The vehicle mobility is simulated in SUMO, and the result of the simulation is an extensible markup language (XML) file with information about vehicle ID, speed, coordinate position, and lane position at every second. Meanwhile, the simulations of VANET clustering are performed in MATLAB. A dedicated program is compiled to simulate the proposed CGC in MATLAB. As the input of simulation, the output file from SUMO is converted from XML to MATLAB data file (.mat). In addition to the vehicle position file, random numbers to represent fading (

The simulation in SUMO runs for 800 seconds. However, the output file of simulation only records the information during

The performance of clustering methods are observed and evaluated using the following metrics:

Number of clusters denotes the number of clusters formed at every unit of time during simulations. Usually, the fewer number of clusters is more desired since the fewer number of clusters is expected to increase the transmission efficiency such as reducing the overhead and communication between clusters.

Average size of the cluster represents the average number of members in a cluster. The higher number of cluster members is more desired since in the worst case, a cluster can only have one or two members. However, sometimes the number of cluster members should be limited. For example, if the number of channels is limited, then the fewer number of members can reduce the channel congestion. Regardless of this opinion, it is assumed that the higher number of cluster members is better.

Cluster head change rate denotes the number of cluster head changes per second. Cluster head change can be in the existing clusters or the newly formed clusters. The fewer number of this metric is desirable since it implies a more stable cluster.

Clustering coverage defines the percentage of vehicles that join any clusters. Normally, 100% coverage is better. However, if the clustering method allows a single vehicle to form a cluster, then the coverage can be 100%.

Average V2V SNR denotes the average of SNR from all V2V connections established at the time.

Average channel capacity denotes the average channel capacity based on the V2V SNR. Channel capacity denotes the maximum bit rate that the channel can support.

The results of simulations are organized into two subsections. The first subsection presents the results of simulations by alternating the value of parameters in CGC, namely, connection time threshold (

Figures

Number of clusters based on the variation of

Average cluster size based on the variation of

Cluster head change rate based on the variation of

Clustering coverage based on the variation of

Average V2V SNR based on the variation of

Average channel capacity based on the variation of

Figure

The results of simulations using LID, DBC, MDC, and the proposed CGC are presented here. For the balance between connection link quality and cluster stability, the parameters of CGC are defined as follows: _{t}) or the maximum radius of cluster from the cluster head is 250 m. The beacon interval (BI) and merge interval (MI) are 0.5 s and 5 s, respectively. The maximum duration of temporary cluster head (_{CHt}) and unclustered node (_{UN}) are 5 s and 3 s, respectively.

Figures

Cluster structure based on the number of clusters and cluster size. (a) Average number of clusters and (b) average size of cluster.

Figure

Cluster head change rate (a) and clustering coverage (b).

The coverage of clustering is shown in Figure

The average of V2V SNR and channel capacity are presented by Figures

Link quality based on V2V SNR and channel capacity. (a) Average V2V SNR and (b) average channel capacity.

A distributed clustering method for VANET based on coalitional game theory, namely, CGC is proposed in this paper. Each vehicle attempts to form a cluster with other vehicles according to the concept of coalition value. Since the purpose of clustering is to improve the V2V SNR while maintaining the stability of the cluster, the coalition value is formulated based on this purpose. The value of coalition is defined by the revenue (V2V SNR) and the cost (connection lifetime and speed difference). In fast-changing network topology, the higher average of SNR can be obtained but the stability of the cluster becomes hard to be maintained. Based on the simulation results, SNR improvement can be adjusted in order to balance with the cluster stability by setting the parameters in CGC accordingly. Further simulation results show that CGC can obtain a higher average of V2V SNR and channel capacity than the other relevant methods.

This study is based on the datasets generated by the SUMO software upon vehicle mobility model we had constructed, which are available from the corresponding author upon request.

There are no other persons who satisfied the criteria for authorship but are not listed. The authors further confirm that the order of authors listed in the manuscript has been approved by all of them.

The authors declare that they have no conflicts of interest.

SS contributed mainly to the defining problem formulation, mobility model, and system modeling as well as discussion parts. SA contributed for implementing the simulation and graphical production while RA contributed for preparing the mobility models dataset using the SUMO software. The authors also confirm that the manuscript has been read and approved by all named authors.

This work was supported by the Ministry of Research, Technology, and Higher Education, Indonesia, under the PUPT grant no. 751/UN1–P.III/LT/DIT-LIT/2016 for the years 2016–2018, and was conducted in the Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.