Currently, communications in the vehicular ad hoc network (VANET) can be established via both Dedicated Short Range Communication (DSRC) and mobile cellular networks. To make use of existing Long Term Evolution (LTE) network in data transmissions, many methods are proposed to manage VANETs. Grouping the vehicles into clusters and organizing the network by clusters are one of the most universal and most efficacious ways. Since the high mobility of vehicles makes VANETs different from other mobile ad hoc networks (MANETs), the previous cluster-based methods for MANETs may have trouble for VANETs. In this paper, we introduce a center-based clustering algorithm to help self-organized VANETs forming stable clusters and decrease the status change frequency of vehicles on highways and two metrics. A novel Cluster Head (CH) selection algorithm is also proposed to reduce the impact of vehicle motion differences. We also introduce two metrics to improve the security of VANETs. A simulation is conducted to compare our mechanism to some other mechanisms. The results show that our mechanism obtains high stability and lower packet loss rate.
As a key component of Intelligent Transportation Systems (ITS), vehicular ad hoc network (VANET) has attracted plenty of researchers from different fields, and massive research efforts have been made.
In VANETs, there are two types of communications [
Generally, communications in VANETs are roughly categorized into two classes according to the adopted radio interfaces. One class of approaches is based on Dedicated Short Range Communication (DSRC). The other class is based on existing cellular technology [
DSRC began to be used for V2V communication from the 90s. It has a shortage in medium range, which is about 300 meters. It is inadequate for large-scale deployment [
To make use of existing mobile cellular networks for data transmissions, many methods are proposed to manage VANETs. However, if VANETs are fully managed by infrastructures, low efficiency will be a big issue, while fully decentralized VANETs must create a lot of overhead. Therefore, VANETs usually combine some centralized parts and decentralized parts. To decrease the overhead via DSRC channels and the probability of LTE channel congestion, VANETs are centralized by cellular-based connections and scheduling. Meanwhile, vehicles may also exchange messages with their neighbors via DSRC. Dividing vehicles into clusters is a common and reasonable approach for VANETs management. In a cluster-based framework, vehicles are signed into clusters. The range of a cluster is smaller or equal to the range of 802.11p, so that vehicles in the same cluster can exchange messages via DSRC. A single eNodeB manages many clusters around it. Within a cluster, at least one vehicle performs as a Cluster Head (CH) to collect information of all Cluster Members (CM) via DSRC and exchanges data with the eNodeB via TLE. This architecture decreases the management overhead while utilizing both DSRC and LTE.
Compared to other MANETs, nodes in VANETs have higher mobility and higher speed. Cluster reforming and CH changing must be much more frequent than other typical MANETs. To decrease the management overhead and increase communication quality, the clustering algorithm for VANETs should be able to form stable clusters. To achieve this goal, in this paper we propose a stable clustering algorithm for VANETs. We propose a novel approach to form and maintain stable clusters for VANETs on highways to avoid continual cluster reforming. A center-based clustering algorithm is used to locate the initial clusters’ centers. In every cluster, a suitable CH is chosen by vehicles’ position, speed, and maximal acceleration. A cluster maintenance algorithm is proposed to keep CMs in its CH’s transmission range.
The rest of the paper is organized as follows. The Related Work briefly reviews the current literature on clustering algorithms in VANETs. The proposed scheme is detailed in the Proposed Scheme. The simulation parameters, simulation results, and analysis are shown in the Performance Evaluation. In the Conclusion, we state the conclusion.
In the literature, clustering is the process to group vehicles in VANETS.
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Road condition affects the speed and direction of vehicles. For example, vehicle’s speed is lower on the bumpy road than a smooth road. Vehicle mobility is determined by human behavior. Take a street connected megapolis and a village as an example. In the morning, most vehicles move from the village (home) to the megapolis (office). In the evening, most vehicles run following the reverse path. Ref. [
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Clustering algorithm groups a set of unlabeled nodes into clusters. In cluster-based VANETs, all vehicles send their information to eNodeB. Then, eNodeB manages the vehicles by clusters. A CH acts as a messenger to help eNodeB and CMs exchange information.
We assume all vehicles are able to communicate via both LTE and DSRC. The size of cluster is smaller or equal to the range of 802.11p, so that vehicles in the same cluster can exchange messages via DSRC. DSRC coverage radius is about 300 meters. LTE coverage radius is about 1 kilometer. Therefore, a single eNodeB manages many clusters around it. Within a cluster, a vehicle acts as a CH to collect information of all CMs via 802.11p and exchanges data with the eNodeB via TLE. Figure
Communications within one cluster.
For cluster in this paper, we have some assumptions: All vehicles have both LTE and 802.11p interfaces All vehicles are equipped with Global Positioning System (GPS) devises. So, they have accurate geolocations All vehicles know their destination, speed, and maximal acceleration
Based on the assumptions, we propose a center detection based clustering algorithm. We group the vehicles in the region where the density of vehicles is higher than other areas into clusters with the help of blob detection method or an improved high-degree algorithm. Some parameters, such as speed and acceleration, are added to the CH selection metric to make the cluster stabler and decrease the CH reselection frequency.
In our proposed algorithm, in the initialization stage of cluster formation, vehicles send beacon messages to the eNodeB. The beacon message of one vehicle contains the vehicle’s ID
Direction type is decided by the angle from the current position to the destination. For vehicle
The clustering algorithm is described in Algorithm
After receiving the beacon messages, the system analyzes vehicles’ position information and detects the centers of the ranges where the vehicle density is higher than in other areas. If the vehicle quantity or the vehicle density is not very large, an improved Highest-Degree Algorithm is applied. Several vehicles which have more neighbors in their transmit range are detected. We improve the original Highest-Degree Algorithm to make sure the distance between any two vehicles we detected is larger than the DRSC range. The positions of detected vehicles will be the centers we use in the clustering algorithm. Otherwise, when the vehicle quantity and the vehicle density are very large, to decrease the computing complexity and analyze time, the system draws dots on the map to indicate vehicles. Then, we can carry out the blob detection. The blob detection is able to detect the regions where the gray pixel value is greater. Thus, we can use the blob detection algorithm, e.g., [
All vehicles whose distances to the center are not larger than the range of DSRC are labeled as one cluster. Then, the system selects one nearest intersection for every center among all intersections that meet the following conditions: The distance from it to the points in The intersection is not in any cluster’s region
Vehicles near those selected intersections are grouped into clusters. Then, eNodeB uses the same way to select intersections near the selected intersections and groups vehicles. After iterations, ungrouped vehicles are grouped into clusters. The distance between two vehicles in the same cluster is not larger than the range of DRSC. To further decrease computing complexity, in line 8 of clustering algorithm, a vehicle or infrastructure located in the center or intersection can broadcast a request to invite neighbors to join the cluster. In line 37, the chosen vehicle
Compared to other MANETs, VANETs have lower stability, because of the high mobility of vehicles. Although we divide the vehicles with the help of direction vector
For a vehicle
The relative mobility metric
The unpredictability and mobility of traffic make the cluster lifetime temporary. It is infeasible to reform clusters in real time or very frequently. To minimize the frequency and overhead of cluster reforming, we propose a cluster maintenance algorithm. Algorithm
To further improve the VANETs security and availability, a novel security mechanism is proposed to detect malicious nodes.
In clustered networks, the availability and security of CHs are incredibly crucial. CHs help the servers to collect and transmit messages to CMs. If an attacker wants the access to other vehicles’ private information, it should acts as a CH. The most common and most executable method for an attacker to be selected as a CH is launching a Sybil attack. In a Sybil attack, the vehicle controlled by a malicious attacker presents multiple identities (vehicles), and all of the vehicles have similar directions, positions, speeds, and maximal acceleration. Hence, these vehicles must have higher relative mobility metrics and higher probabilities to be selected as CH.
To protect the CMs’ privacy, we introduce a trajectory similarity metric
A denial-of-service attack (DoS attack) is another common attack in VANETs. In DoS attack, the attacker floods the CH or server to make the network services unavailable. The connections of authenticated vehicles to the network are temporarily broken. Therefore, the legitimate requests of server and authenticated vehicles cannot be actioned.
To protect the network availability, we introduce an activity similarity metric
We perform the simulation with the help of Veins LTE. Veins LTE is a simulator developed on Veins [
In our experiment, vehicles run on a real map of Washington, DC, USA, obtained from OpenStreetMap [
We compare our proposed clustering algorithm, Center-Based Stable Clustering Algorithm (CBSC), with a K-Means-Based method (KMB) and SCalE algorithm [
The goal of this paper is to propose a stable clustering algorithm for VANETs. To check whether a clustering algorithm can solve the high mobility of vehicles on the highways, the cluster stability should be evaluated. The metrics we use to show the performance of clustering algorithm are as follows:
In the experimentation, we compare the four metrics of the three methods with different vehicle numbers, transmission ranges, or highway speed limits. Figures
Average CH lifetime versus N and R.
Average CH lifetime versus R and v.
Average CM lifetime versus N and R.
Average CH lifetime versus R and v.
Average number of reaffiliation times per vehicle versus N and R.
Average number of reaffiliation times per vehicle versus R and v.
Packet loss rate versus N and R.
Packet loss rate versus R and v.
Figures
The average CM lifetime values produced by KMB, SCalE, and the CBSC methods are shown in Figures
Figures
The results of simulation illustrate that clusters under CBSC are the stablest in the three algorithms. They have the longest average CM lifetime and lowest average number of reaffiliation times per vehicle. Although SCalE performs slightly better than CBSC on the CH lifetime experiment, it produces a much shorter average CM lifetime. Besides, the number of CMs is much larger than the CHs in one system. Therefore, we consider that CBSC has higher stability than SCalE.
The basic function of VANETs is supporting communication between separated vehicles and infrastructures. To test the performance of data dissemination in VANETs, we do experiment on packet loss rate with different methods. Packet loss means a packet fails to arrive at its destination. A high packet loss rate decreases the data dissemination efficiency and may cause network congestion. Therefore, an efficient data dissemination mechanism should have a low packet loss rate. In our experiment, all vehicles exchange data with eNodeB every three seconds. That means, in every three seconds, eNodeB sends data to all vehicles once, and each vehicle sends data to eNodeB once. Like the scene we described in the previous section, eNodeB communicates with the nodes in its record via CHs, and vehicles which are CMs send data to their CHs first. Figures
To decrease the management overhead and increase the quality of communications, we try to make the clusters in VANETs as stable as possible while keeping the network performance acceptable. In this paper, we propose a stable clustering algorithm for VANETs on highways, which utilizes direction vector, the centers of vehicle denser areas, and intersections to group less quantity of more stable clusters. To reduce the impact of vehicle type and drivers’ driving habits, we propose a novel CH selection algorithm and cluster maintenance algorithm, which use the relative mobility metric to reduce the influence of vehicle’s distance, velocity, and maximal acceleration. To protect the vehicles’ privacy and the network availability, we introduce two mechanisms to detect malicious attacker. In the simulation experiment, our algorithm’s performance ranks up against the other two algorithms (KMB and SCalE) on both stability and package delivery rate. In the future, we would like to further improve the algorithm for the complex urban environment.
No data were used to support this study.
The authors declare that they have no conflicts of interest.