In the last decade, cooperative vehicular network has been one of the most studied areas for developing the intelligent transportation systems (ITS). It is considered as  an  important  approach  to  share  the  periodic  traffic situations over vehicular ad hoc networks (VANETs) to improve efficiency and safety over the road. However, there are a number of issues in exchanging traffic data over high mobility of VANET, such as broadcast storms, hidden nodes and network instability.



This paper proposes a new model to detect the traffic conditions  using  clustering  traffic  situations  that  are gathered from the nodes (vehicles) in VANET. The model designs new principles of multi-level clustering to detect the traffic condition for road users. Our model (a) divides the situations of vehicles into clusters, (b) designs a set of metrics to get the correlations among vehicles and (c) detects the traffic condition in certain areas. These metrics are simulated using the network simulator environment (NS-3) to study the effectiveness of the model

Abufanas, A

Peytchev, E

English

Ayman Abufanas

Evtim Peytchev

Crossref

Detecting Traffic Conditions Model Based On Clustering Nodes Situations In VANET

Nottingham Trent Institutional Repository (IRep)

DETECTING TRAFFIC CONDITIONS MODEL BASED ON CLUSTERINGNODES SITUATIONS IN VANETAyman Abufanas and Evtim PeytchevSchool of Science and TechnologyyNottingham Trent UniversityClifton Lane, Nottingham, NG11 8NS, UKE.mail: {ayman.abufanas2009 , evtim.peytchev}@ntu.ac.ukKEYWORDSVehicular Ad-Hoc Network; Traffic Condition Detec-tion; Clustering; Network Simulation; ITS; NS-3; MobileProgramming.ABSTRACTIn the last decade, cooperative vehicular network hasbeen one of the most studied areas for developing theintelligent transportation systems (ITS). It is consideredas an important approach to share the periodic trafficsituations over vehicular ad hoc networks (VANETs) toimprove efficiency and safety over the road. However,there are a number of issues in exchanging traffic dataover high mobility of VANET, such as broadcast storms,hidden nodes and network instability.This paper proposes a new model to detect the trafficconditions using clustering traffic situations that aregathered from the nodes (vehicles) in VANET. The modeldesigns new principles of multi-level clustering to detectthe traffic condition for road users. Our model (a) dividesthe situations of vehicles into clusters, (b) designs a setof metrics to get the correlations among vehicles and (c)detects the traffic condition in certain areas. These metricsare simulated using the network simulator environment(NS-3) to study the effectiveness of the model.INTRODUCTIONVEHICULAR ad hoc network is considered as newtechnology that has recently developed in termsof improving road safety, traffic knowledge and touristinformation service. The issues of traffic control andmanagement have become significantly important in orderto utilize full capacity of all roads. In addition, the lackconsideration of travel safety may also increase number oftraffic accidents. For instance, approximately six millionaccidents are recorded annually in the United States aloneand the same study shows that in the 2007, thousandsof people have died and were injured in China due tocars crashes (Zhou et al. 2010).Therefore, much effort has been placed to develop ITSapplications to reduce the problems to avoid traffic jamsor dangerous areas on their current routes. For instance,Bishop (2005) states that the combination of ITS andtelecommunication systems and transport informationsystems is able to provide reliable forecast informationto travellers to enhance safety and efficiency of the routes.These efforts are under-utilised so far in term of trafficinformation systems where every vehicle can be presentedas a single node in an ad-hoc network (Thomas et al.2004).Figure 1: VANET concept (Khan 2010)In fact, several recent relevant ITS applications in thecurrent time require fixed infrastructure. Particularly, theactivity of information exchange between vehicles andinfrastructure is defined as Vehicle-to-Infrastructure (V2I)communications. Although, it provides several solutionsfor any transport challenges, it is arguable that V2Iarchitecture has faced several restrictions such as thecost of sufficient infrastructure and reliable connection(Thomas et al. 2004).According to these circumstances, researchers andcommercial projects tend to focus on a different archi-tecture of ITS applications, known as the vehicle-to-vehicle (V2V) communications, which focus on directmessage delivery between nodes (vehicles). Figure 1illustrates the architecture of vanet including these typesof communications. Currently, VANET-based wirelessnetworks are a natural fit to intelligent informationtransportation system (ITS).The rest of paper is structured as follows. Section IIdiscusses related work of the proposed model. SectionProceedings 29th European Conference on Modelling and Simulation ©ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors) ISBN: 978-0-9932440-0-1 / ISBN: 978-0-9932440-1-8 (CD)III demonstrates our model and methodologies that areapplied. Then section IV generally concludes the paperand briefly introduces the future work of the project.RELATED WORKDesigning traffic monitoring dissemination approachis still one of the open research challenges beinginvestigated in VANET. Researchers often consider thatit is significant to detect the specific traffic conditionsover roads or junctions in vehicular networks (Praba &Ranichitra 2013). On one hand, it is widely known thatthe more vehicles are on the road, more reliable andfaster for information delivery in vehicular networks. Onthe other hand, the higher the density of vehicles, themore probability that traffic congestion or accidents mayhappen in the area. Therefore, the number of vehicles areon the road is a double-edged sword in order to achievea high level of stability of networks.The big challenge for reliable and fast data deliveryis blind vehicles. Floating car data is a technology thatgathers road status data from vehicles. FLOAT is a systemof driving alongside the sections and recording the trafficconditions when road users travel (Bishop 2005). Thisscheme assumes that cars can be considered as a movingsensor that runs on a network. In addition, floating cardata technology (FCD) has become widely used in manyalgorithms for solving the challenges of fixed monitoringsystems (Li et al. 2012). Thus traffic information of FCDmay improve drivers decisions for their route choices.Manvi et al. (2009) introduced a cognitive modelwhich performs pull and push processes on relevantand important data collected in VANETs and also theyproposed a critical data retrieve agent called CIPLA.Sometimes, vehicles are interested to recollect latestweather information from other vehicles, such as fog,rain, etc. However, CIPLA seems to be complex andmessages that forwarded by this system are also sentslowly, consequently, the delay may tend to be biggerand the performance might be low.Vision car detection based system was presented forFCD, which helps to improve the context of Vehicularnetworks (Llorca et al. 2010). Packets are transmittedbased on GPRS/UMTS communication which also usesa central unit to mix the expanded FCD. However,GPRS/UMTS is responsible for the delivering the packets,which it means that their model depends on an existedinfrastructure such as stations, road side units (RSU)etc. Therefore, Carry messages then forward is quiteappropriate mode with low mobility and acceptable delaynetworks (Li et al. 2012).Therefore, Zhao & Cao (2008) focused on V2Vcommunication and they introduced the idea of ”carryingthen forwarding” packets, which means that packets willbe temporarily kept in node’s buffer until it obtainsa connection with other node that enters the network.Then the message will be forwarded to that node.They proposed vehicles assisted data delivery algorithms(VADD) which use dense roads to deliver the packet toobtain faster delivery. However, the authors didnt focuson a method of detecting the traffic situation whether itis dense or not.Gamati et al. (2011) proposed algorithm for trafficdetection, named (TCDA) in V2V communication. Theiridea is based on using the optimum number of messagesthat provide sufficient indication to detect the conditionsabout the roads. They obtain good result comparedwith Flooding and AODV protocol. A serious questionwith TCDA model, however, is the idea of optimumnumber could be varied depending on weather andtraffic situation, such as fog, snow, etc. They obtainedgood results compared with flooding protocol. Moreover,the algorithm did not clearly consider the issue of theboundaries of area that is covered to estimate the trafficcondition. Moreover, other unconsidered parameters mayaffect on the reliability of detecting the conditions, suchas speed, direction.The proposed model is designed to take a placeon an application layer in VANET. Therefore, theexisting routing protocols are applied for exchangingand delivering packets among nodes in the network. Itwas decided to use Ahoc On Demand Distance Vectorrouting protocol (AODV) to achieve the routing suite inthe model.AODV is considered as a reactive routing protocol thatenables dynamic multi-hop routing among participatingnodes to establish and maintain a network (Perkinset al. 2003). AODV can manage low and relatively highmobility of nodes, as well as a variety of data trafficlevels. Route Requests (RREQs), Route Replies (RREPs),Route Errors (RERRs), and hello packets are some typesof the message defined by the protocol (Perkins et al.2003). As it is found that AODV is suited sufficiently forVANET, authors decided to enhance AODV by addingtraffic monitoring features.The model combines routing functionality for deliverythe messages to provide efficient detection of traffic situ-ation; cluster aggregation of nodes to improve stability,avoid broadcast storms and finally a part of a system todefine cloud information network in each cretin area. Ourmodel (a) divides the situations of vehicles into groups,(b) designs a set of metrics to get the correlations amongvehicles and (c) detects the traffic condition in certainareas.PROPOSED WORKDesigning a reliable traffic detection model is achallenge, an interesting and widely considered byresearchers. According to relevant works, previousmodels do not apply the idea of using clustered vehicles’situations to detect traffic conditions. The new modelwill consider this idea to achieve the aim of trafficcondition detection over traffic scenario in ad hocnetwork. A framework of reliable traffic detection isdesigned and simulated in NS-3. IEEE 1609.4 andIEEE 802.11p standard is applied in the model forthe communication between V2V and V2I in ourmodel. Moreover, the model will link the area betweenVANET environment and vehicle’s driver decisionbased on the gathered messages from traffic roads status.Figure 2 illustrates a general diagram of proposedwork which includes two main phases as follow; First,mobility scenario, which is presented as generated mo-bility patterns by simulation of urban mobility software(SUMO), and AODV routing protocol for deliveringpackets between nodes. Second, phase is for trafficcondition detection, it consists of (a) stage of augmentingand defining traffic condition into the mobility pattensfile and (b) detecting the traffic condition stage byclustering the gathered messages. These messages reflectindividual cars’ situations, such as ABS (on/off), brakelights (on/off), windscreen wipers (on/off), etc.Generating Traffic Mobility Using SUMOSUMO is one of the most popular microscopic open-source tools for handling and simulating traffic in largescale of street networks (Krajzewicz et al. 2012). SUMOuses a number of tools to integrate roads network pro-cessing and traffic generation. The roads are representedas cretin edges of an oriented graph, with nodes being theintersections (SUMO 2015). In addition, Edges can carryrelevant traffic information, such as street category, trafficlights location, speed limitations and other signs, etc..Moreover, SUMO has a tool called net-convert, whichis capable to convert digital maps (e.g., OpenStreetMap)into SUMO map format (SUMO 2015).The algorithm 1 shows pseudocode of the suite ofclustering and detecting the traffic condition in theproposed model. Figure 3 explains the stages of algorithmhow they are simulated by presenting the levels of themodel. The lowest level is to identify the network bythe existing routing protocol. Then the traffic detectionsuite which includes (a) level of gathering vehicles’messages, and (b) clustering the vehicles based onthe gathered traffic situations. Finally detecting theroad conditions, including detecting clusters boundaries,based on studying the correlation level between cars’situations. In addition, the following equations explainthe mathematical background for this aim.Where messages v1, v2, v3,... are the gathered individ-ual situations, and A, B, C, D,... represent sets of trafficsituations. Each set of vehicles will create their owncluster as long as they send the same type of messages,even these vehicles might not be physically neighbours.Figure 2: Proposed Framework of Traffic Condition Detectioninitialization: number of vehicles is n , definingtraffic situations, t;while within simulation time doread message vi;if do messages have the same type of trafficsituation thenif the type of message is already aggregatedthenadd to cluster si;elsekeep this messageendelseupdate the clusters by these messages;endwhile there is a correlation among sn doif the correlation between cluster si and sjis positive thenmerge si and sj into a new cluster Ci;elsecheck next clustersendendendAlgorithm 1: Pseudocode of The AlgorithmA = {v1, v3, v4, v6} (1)B = {v1, v2, v3, v6} (2)C = {v3, v7, v8} (3)D = {v7, v10} (4)After creating clusters of traffic situation, the modelstudies the correlation between them. As it is shown inFigure 3: Sample of the Traffic Scenariofigure 3 for example, cluster of hazard lights and othercluster of windscreen wipers (on) may merge and indicateto a higher and a bigger new cluster of slippery roadcondition. Equations 5 and 6 identify the higher level ofclusters, where x and y are detected traffic conditions.x = A ∪B ,where A ∩B 6= φ (5)y = C ∪D ,where C ∩D 6= φ (6)Figure 4 illustrates how sets of traffic situations cancreate sets of traffic conditions. It is shown that one ofsets of traffic situations may participate in more than onecluster of traffic condition decision.Figure 4: Example of Studying Correllations Between Set ofTraffic SituationsCONCLUSIONIt is widely clear that the coming intelligent transporta-tion systems can go further from present capabilities.In this paper we have proposed a system to monitorand detect the traffic conditions based on clustering ofindividual cars situations.The paper illustrated the new technique of the cluster-ing and it also explained how to obtain the correlationamong sets of situations to detect the traffic conditions.The future work will focus on implementing the newmodel on NS-3. In addition, the model will be imple-mented with different routing protocols based on IEEE1609.4 and IEEE 802.11p standard. Results will bepresented and studied based on how the model can affecton the performance of the routing protocols.REFERENCESBishop, R. (2005), Intelligent vehicle technology andtrends, Artech Housse, Boston, USA.Gamati, E. A., Peytchev, E. & Germon, R. (2011),‘Traffic condition detection algorithm (tcda) for vanetnodes in wireless intelligent transportation informationsystems’.Khan, S. (2010), ‘Multi constraint quality of service (qos)routing for vehicular ad-hoc networks’.Krajzewicz, D., Erdmann, J., Behrisch, M. & Bieker, L.(2012), ‘Recent development and applications of sumo–simulation of urban mobility’, International JournalOn Advances in Systems and Measurements 5(3 and4), 128–138.Li, D., Li, Q. & Wang, J. (2012), ‘Traffic informationcollecting algorithms for road selection decision sup-port in vehicle ad hoc networks’, International Journalof Simulation and Process Modelling 7(1), 50–56.Llorca, D. F., Sotelo, M., Snchez, S., Ocaa, M., Rodrguez-Ascariz, J. & Garca-Garrido, M. (2010), ‘Traffic datacollection for floating car data enhancement in v2inetworks’, EURASIP Journal on Advances in SignalProcessing 2010, 5.Manvi, S., Kakkasageri, M. & Pitt, J. (2009), ‘Multiagentbased information dissemination in vehicular ad hocnetworks’, Mobile information systems 5(4), 363–389.Perkins, C., Belding-Royer, E., Das, S. et al. (2003),‘Rfc 3561-ad hoc on-demand distance vector (aodv)routing’, Internet RFCs pp. 1–38.Praba, V. L. & Ranichitra, A. (2013), ‘Detecting ma-licious vehicles and regulating traffic in vanet usingraodv protocol’.SUMO (2015), ‘simulation of urban mobility’.URL: Available: http://sumo.sourceforge.netThomas, M., Peytchev, E. & Al-Dabass, D. (2004),Auto-sensing and distribution of traffic informationin vehicular ad hoc networks, in ‘Proceedings ofthe United Kingdom Simulation Society Conference’,pp. 124–128.Zhao, J. & Cao, G. (2008), ‘Vadd: Vehicle-assisted datadelivery in vehicular¡ emphasis emphasistype=’, Vehic-ular Technology, IEEE Transactions on 57(3), 1910–1922.Zhou, L., Cui, G., Liu, H., Wu, Z. & Luo, D. (2010),‘Nppb: A broadcast scheme in dense vanets’, Informa-tion Technology Journal 9(2), 247–256.AUTHOR BIOGRAPHIESDR. EVTIM PEYTCHEV isReader in Wireless, Mobile andPervasive Computing in the schoolof Science and Technology atNottingham Trent University, UK. Heis leading the Intelligent Simulation,Modelling and Networking ResearchGroup, which consists of 5 lecturers,3 Research Fellows and 6 research students. He is theModule Leader for Systems Software; and Wirelessand Mobile Communications. He also teaches on themodules Software Design and Implementation; MobileNetworking; Enterprise Computing; and ComputerArchitecture.AYMAN ABUFANAS went to theCollege of Industry and Technology(Libya) where he received his BScdegree in electronic engineering. Heworked as a technical manager inprivate section before moving to theHigh Vocational Institute for teaching.Then he went to Nottingham TrentUniversity (the United Kingdom) where he has obtainMSc in computer and technology. Currently, he is a PhDstudent in Nottingham Trent university in the field ofwireless intelligent systems.

Auto-sensing and distribution of traffic information in vehicular ad hoc networks,

Detecting malicious vehicles and regulating traffic in vanet using raodv protocol’.

Intelligent vehicle technology and trends, Artech Housse,

Multi constraint quality of service (qos) routing for vehicular ad-hoc networks’.

Multiagent based information dissemination in vehicular ad hoc networks’, Mobile information systems

Nppb: A broadcast scheme in dense vanets’,

Recent development and applications of sumo– simulation of urban mobility’,

Rfc 3561-ad hoc on-demand distance vector (aodv) routing’, Internet RFCs

simulation of urban mobility’.

Traffic condition detection algorithm (tcda) for vanet nodes in wireless intelligent transportation information systems’.

Traffic data collection for floating car data enhancement

Traffic information collecting algorithms for road selection decision support in vehicle ad hoc networks’,

Vadd: Vehicle-assisted data delivery in vehicular¡ emphasis emphasistype=’, Vehicular Technology,

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Detecting Traffic Conditions Model Based On Clustering Nodes Situations In VANET

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