Clustering Algorithm in Vehicular Ad-hoc Networks: A Brief Summary

An Intelligent Transportation System (ITS) application requires vehicles to be connected to each other and to roadside units to share information, thus reducing fatalities and improving traffic congestion. Vehicular Ad hoc Networks (VANETs) is one of the main forms of network designed for ITS in which information is broadcasted amongst vehicular nodes. However, the broadcast reliability in VANETs face a number of challenges - dynamic routing being one of the major issues. Clustering, a technique used to group nodes based on certain criteria, has been suggested as a solution to this problem. This paper gives a summary of the core criteria of some of the clustering algorithms issues along with a performance comparison and a development evolution roadmap, in an attempt to understand and differentiate different aspects of the current research and suggest future research insights

Dynamic multiagent method to avoid duplicated information at intersections in VANETs

Vehicular ad hoc networks (VANETs) allow vehicles to contact one another to provide safety and comfort applications. However, mobility is a great challenge in VANETs. High vehicle speed causes topological changes that result in unstable networks. Therefore, most previous studies focused on using clustering techniques in roads to reduce the effect of vehicle mobility and enhance network stability. Vehicles stop moving at intersections, and their mobility does not impact clustering. However, none of previous studies discussed the impact of vehicle stopping at intersections on base stations (BSs). Vehicles that have stopped moving at intersections continue to send the same information to BSs, which causes duplicated information. Hence, this study proposes a new method named dynamic multiagent (DMA) to filter cluster information and prevent duplicated information from being sent to BSs at intersections. The performance of the proposed method was evaluated through simulations during the use of DMA and without-DMA (W-DMA) methods based on real data collected from 10 intersections in Batu Pahat City, Johor, Malaysia. Overall, the proposed DMA method results in a considerable reduction in duplicated information at intersections, with an average percentage of 81% from the W-DMA method

Stable Dynamic Predictive Clustering (SDPC) Protocol for Vehicular Ad hoc Network

Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication, specially, when the number of vehicles increases at any given point. Vehicles also suffer some other problems such as broadcast problem. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either straight road or for intersection. Moreover, the absence of the intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, movement at the intersection etc., results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of all the mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a novel robust and dynamic clustering algorithm stable dynamic predictive clustering (SDPC) for VANET is proposed in this paper. In contrast to previous studies, vehicle relative velocity, vehicle position, vehicle distance, transmission range, and vehicle density are considered in the creation of a cluster, whereas relative distance, movement at the intersection, degree of vehicles are considered to select the cluster head. From the mobility parameters the future road scenario is constructed. The cluster is created, and the cluster head is selected based on the future construction of the road. The performance of SDPC is compared in terms of the average cluster head change rate, the average cluster head duration, the average cluster member duration, and the ratio of clustering overhead in terms of total packet transmission. The simulation result shows SDPC outperforms the existing algorithms and achieved better clustering stability

A Novel Stable Clustering Approach Based On Gaussian Distribution And Relative Velocity In VANETs

Vehicles in Vehicular Ad-hoc Networks (VANETs) are characterized by their high dynamic mobility (velocity). Changing in VANET topology is happened frequently which caused continuous network communication failures. Clustering is one of the solutions applied to reduce the VANET topology changes. Stable clusters are required and Indispensable to control, improve and analyze VANET. In this paper, we introduce a new analytical VANET's clustering approach. This approach aims to enhance the network stability. The new proposed grouping process in this study depends on the vehicles velocities mean and standard deviation. The principle of the normal (Gaussian) distribution is utilized and emerged with the relative velocity to propose two clustering levels. The staying duration of vehicles in a cluster is also calculated and used as an indication. The first level represents a very high stabile cluster. To form this cluster, only the vehicles having velocities within the range of mean ± standard deviation, collected in one cluster (i.e. only 68% of the vehicles allowed to compose this cluster). The cluster head is selected from the vehicles having velocities close to the average cluster velocity. The second level is to create a stable cluster by grouping about 95% of the vehicles. Only the vehicles having velocities within the range of mean ± 2 standard deviation are collected in one cluster. This type of clustering is less stable than the first one. The analytical analysis shows that the stability and the staying duration of vehicles in the first clustering approach are better than their values in the second clustering approach

Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research

Vehicular communication networks received good consideration and focusing on diverse researchers in the latest years. Vehicular Adhoc Networks (VANETs) represents a developed type of an effective communication technology to facilitate the process of information dissemination among vehicles. VANETs established the cornerstone to develop the Intelligent Transport Systems (ITS). The great challenging task in routing the messages in VANETs is related to the different velocities of the moving vehicles on the streets in addition to their sparse distribution. Clustering approach is broadly used to report this challenge. It represents the mechanism of the alliance the vehicles based on certain metrics such as velocity, location, density, direction and lane position. This paper is to investigate and analyze several challenges and their present solutions which based on different developed clustering approaches based on the affinity propagation algorithm. This paper isaim to present a complete taxonomy on vehicles clustering and analyzing the existing submitted proposals in literature based on affinity propagation. Presenting and analyzing the submitted proposals will provide these domain researchers with a good flexibility to select or apply the suitable approach to their future application or research activities. To prepare this paper in a systematic manner, a total of 1444 articles concerning the Affinity Propagation in clustering published in the era of 2008 to 2019 were collected from the reliable publishing sources namely (ScienceDirect, IEEE Xplore, and SCOPUS). Due to their relevance, applicability, generality level and comprehensiveness, only nineteen articles among the collected articles were assigned and eventually analyzed in a systematic review method.A considerable success has been achieved in revealing the essential challenges and necessities for clustering based affinity Propagation in VANETs to guide the researchers in their upcoming investigations. This paper also contributes in dealing with open problems issues, challenges and guidelines for the upcoming investigations

Exploiting vehicular social networks and dynamic clustering to enhance urban mobility management

Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency1916CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP401802/2016-7; 2015/25588-6; 2016/24454-9; 2018/02204-6; 465446/2014-088887.136422/2017-002014/50937-

Real time collision warning system in the context of vehicle-to-vehicle data exchange based on drivings behaviours analysis

Worldwide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error regardless of technological innovations and advancements for vehicle safety. Consequently, there is a need for a reliable-real time warning system that can alert drivers of a potential collision. Vehicle-to-Vehicle (V2V) is an extensive area of ongoing research and development which has started to revolutionize the driving experience. Driving behaviour is a subject of extensive research which gains special attention due to the relationship between speeding behaviour and crashes as drivers who engage in frequent and extreme speeding behaviour are overinvolved in crashes. National Highway Traffic Safety Administration (NHTSA) set guidelines on how different vehicle automation levels may reduce vehicle crashes and how the use of on-board short-range sensors coupled with V2V technologies can help facilitate communication among vehicles. Based on the previous works, it can be seen that the assessment of drivers’ behaviours using their trajectory data is a fresh and open research field. Most studies related to driving behaviours in terms of acceleration�deceleration are evaluated at the laboratory scale using experimental results from actual vehicles. Towards this end, a five-stage methodology for a new collision warning system in the context of V2V based on driving behaviours has been designed. Real-time V2V hardware for data collection purposes was developed. Driving behaviour was analyzed in different timeframes prior obtained from actual driving behaviour in an urban environment collected from OBD-II adapter and GPS data logger of an instrumented vehicle. By measuring the in-vehicle accelerations, it is possible to categorize the driving behaviour into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. When the vehicle is in a risk situation, the system based on NRF24L01+PA/LNA, GPS, and OBD-II will pass a signal to the driver using a dedicated LCD and LED light signal. The driver can instantly decide to make the vehicle in a safe mood, effectively avoid the happening of vehicle accidents. The proposed solution provides two main functions: (1) the detection of the dangerous vehicles involved in the road, and (2) the display of a message informing the driver if it is safe or unsafe to pass. System performance was evaluated to ensure that it achieved the primary objective of improving road safety in the extreme behaviour of the driver in question either the safest (or the least aggressive) and the most unsafe (or the most aggressive). The proposed methodology has retained some advantages for other literature studies because of the simultaneous use of speed, acceleration, and vehicle location. The V2V based on driving behaviour experiments shows the effectiveness of the selected approach predicts behaviour with an accuracy of over 87% in sixty-four real-time scenarios presented its capability to detect behaviour and provide a warning to nearby drivers. The system failed detection only in few times when the receiving vehicle missed data due to high speed during the test as well as the distances between the moving vehicles, the data was not received correctly since the power transmitted, the frequency range of the signals, the antenna relative positions, and the number of in-range vehicles are of interest for the V2V test scenarios. The latter result supports the conclusion that warnings that efficiently and quickly transmit their information may be better when driver are under stress or time pressure

Adaptive Beacon Broadcast in Opportunistic Routing for VANETs

Broadcast of beacon messages including geographic coordinates, node speeds, and directions are among the most commonly used methods in routing protocols of VANETs to obtain neighboring positions. Broadcast of periodic beacon messages in fixed time intervals will reduce network performance due to increased channel load and contention. In this paper, an adaptive update strategy for sending beacon messages according to the VANETs’ characteristics (position, speed, and direction) and the nature of broadcast wireless channel in an opportunistic routing strategy is studied. It is based on two rules: 1) an estimation of the lifetime of the links between vehicles’ beacon messages are sent after the expiration of the estimated time to inform their local topology and 2) if the forwarding set of consecutively received data packets is changed, a beacon message is sent to maintain the accuracy of the topology. The simulation results show that the proposed strategy significantly reduces the cost of routing and improves network performance in terms of packet-delivery ratios, average end-to-end delay, and routing overhead

Enhanced stability of cluster-based location service mechanism for urban vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) are gaining tremendous research interest in developing an Intelligent Transportation System (ITS) for smart cities. The position of vehicles plays a significant role in ITS applications and services such as public emergency, vehicles tracking, resource discovery, traffic monitoring and position-based routing. The location service is used to keep up-to-date records of current positions of vehicles. A review of previous literatures, found various locationbased service mechanisms have been proposed to manage the position of vehicles. The cluster-based location service mechanisms have achieved growing attention due to their advantages such as scalability, reliability and reduced communication overhead. However, the performance of the cluster-based location service mechanism depends on the stability of the cluster, and the stability of the cluster depends on the stability of the Cluster Head (CH), Cluster Member (CM) and cluster maintenance. In the existing cluster-based location service schemes, the issue of CH instability arises due to the non-optimal cluster formation range and unreliable communication link with Road Side Unit (RSU). The non-optimal cluster formation range causes CH instability due to lack of uniqueness of Centroid Vehicle (CV), uncertainty of participating vehicles in the CH election process and unreliability of the Cluster Head Election Value (CHEV). Also, the unreliable link with RSU does not guarantee that CH is stable with respect to its CMs and RSU simultaneously. The issue of CM instability in the existing cluster-based location service schemes occurs due to using instantaneous speed of the CH and fixed CM affiliation threshold values. The instantaneous speed causes the CM to switch the clusters frequently and fixed CM affiliation threshold values increase isolated vehicles. The frequent switching of isolated vehicles augment the CM instability. Moreover, the inefficient cluster maintenance due to non-optimal cluster merging and cluster splitting also contributes to cluster instability. The merging conditions such as fixed merging threshold time and uncertain movement of overlapping CHs within merging threshold time cause the cluster instability. Furthermore, the unnecessary clustering during cluster splitting around the intersection due to CH election parameters also increases cluster instability. Therefore, to address the aforementioned cluster instability issues, Enhanced Stability of Cluster-based Location Service (ESCLS) mechanism was proposed for urban VANETs. The proposed ESCLS mechanism consists of three complementary schemes which are Reliable Cluster Head Election (RCHE), Dynamic Cumulative Cluster Member Affiliation (DCCMA) and Optimized Cluster Maintenance (OCM). Firstly, the aim of the RCHE scheme was to enhance the stability of the CH through optimizing the cluster formation range and by considering communication link reliability with the RSU. Secondly, the DCCMA scheme focussed on improving the stability of the CMs by considering the Cumulative Moving Average Speed (CMAS) of the CH and dynamic CM affiliation threshold values, and finally, the OCM scheme enhanced the cluster stability by improving cluster merging conditions and reducing unnecessary clustering in cluster splitting. The results of the simulation verified the improved performance of the ESCLS in terms of increasing the location query success rate by 34%, and decreasing the query response delay and localization error by 24% and 35% respectively as compared to the existing cluster-based location service schemes such as HCBLS, CBLS and MoGLS. In conclusion, it is proven that ESCLS is a suitable location service mechanism for a wide range of position-based applications of VANETs that require timely and accurate vehicle locations