documentclass{ccs-thesis}% options:% germanthesis – Thesis is written in German% plainunnumbered – Don’t print numbers on plain pages% earlydraft – Settings for quick draft printouts% watermark – Print current time/date at bottom of each page% phdthesis – switch to PhD thesis style% twoside – double sided% cutmargins – text body fills complete page% Author name. Separate multiple authors with commas.author{Gaurav Kumar Singh}irthday{10. November 1989}irthplace{Danapur, India}% Title of your thesis. itle{Clustering in Vehicular Networks}% Choose one of the following lines. Feel free to change the word “Informatik” to match your degree program.% hesistype{Masterarbeit im Fach Informatik} hesiscite{Master’s Thesis~(Masterarbeit)}% hesistype{Bachelorarbeit im Fach Informatik} hesiscite{Bachelor Thesis~(Bachelorarbeit)} hesistype{Seminararbeit im Fach Informatik} hesiscite{Seminar Thesis~(Seminararbeit)}% List of advisors, separated by commas.advisors{Jun.-Prof. Dr.-Ing. Christoph Sommer, Prof. Dr.-Ing. habil. Falko Dressler}% List of referees, separated by commas.
eferees{Jun.-Prof. Dr.-Ing. Christoph Sommer, Prof. Dr.-Ing. habil. Falko Dressler}% Define abbreviations used in the thesis here.acrodef{WSN}{Wireless Sensor Network}acrodef{WANET}{Wireless Ad Hoc Network}acrodef{MANET}{Mobile Ad Hoc Network}acrodef{VANET}{Vehicular Ad Hoc Network}acrodef{CH}{Cluster Head}acrodef{CM}{Cluster Member}acrodef{CHP}{Cluster Head Pending}acrodef{RSU}{Road Side Unit}acrodef{GPS}{Global Positioning System}acrodef{VEINS}{Vehicles in Network Simulation}acrodef{V2V}{Vehicle-to-Vehicle}acrodef{V2I}{Vehicle-to-Infrastructure}egin{document}pagenumbering{roman}maketitlechapter*{Abstract}addcontentsline{toc}{chapter}{Abstract}egin{otherlanguage*}{american} This thesis captures an overview of ideas, techniques, results and future possibilities of clustering in vehicular networks. Clustering is a technique to group nodes based on a selected criterion which defines certain level of similarities among the nodes. Grouping the nodes together in such a way helps define or design a set of functionalities applicable only to the group and can be applied to the smaller sub-set. In a ac{VANET} environment, clustering presents possibilities to group vehicles based on a parameter of interest and help to reduce the network traffic, achieve better network throughput and effective information dissemination. This thesis presents a set of parameter and respective methodologies based on them for ac{VANET}s as a comparative study. First chapter presents the motivation behind the clustering and outlines the basic set of problems which is presented by vehicular networks which the researches are trying to address. The second chapter describes the methodologies grouping them based on the main parameters used for clustering. The third chapter introduces the evaluation techniques along with some important metrics used to compare the effectiveness of the algorithms and analysis of results. Finally, the thesis captures some ideas which will give an overview of the future research work on this topic.end{otherlanguage*}acresetallcleardoublepage ableofcontentscleardoublepagepagenumbering{arabic}chapter{Introduction}%chapter{Einleitung}label{sec:introduction}Along with the advancement in wireless networking in the past two decades, there has been a lot of research targetedtowards developing techniques to minimize the network overhead and achieve effectiveness within the system. A specialclass of wireless network, ac{WANET}, which allowed nodes to communicate with each other without the need ofspecial infrastructure such as bridges and routers was developed. ac{WANET} led to use of wireless communicationfor special applications with needs of distributed control. Shortly, use of ac{MANET} increased which allowedcontinuous movement of the nodes. This was followed by use of wireless networking among vehicles to create ac{VANET}which allows communication of various parameters among vehicles focused towards applications for safety and cooperativedriving. The use of wireless networks in various domains has led to a lot of research focused towards improvementsand optimization which are often valid for all domains.Clustering in wireless networks involves grouping nodes together which are geographically close to each other based on acertain set of parameters. Parameter selection for clustering depends mostly on the type of application which would usethe clustered network. In ac{VANET}s, clustering of vehicles into groups provides a basis for limiting the networkingoverhead and interference by efficiently defining the target nodes for communication and designing filters to limit the traffic.Due to the possibility of selecting huge range of parameters, numerous solutions have been proposed which target variousscenarios in the ac{VANET}s. extcite{6256251} and extcite{BALI2014134} presents a detailed overview of research work in thisfield in past years. In the following sections we would look at some of the important terminologies to createa general overview of clustering in ac{VANET} and help us discuss and understand the methodologies better.section{Terminologies}In this section, we look at some of the common terminologies used widely across the methodologiesfor clustering in ac{VANET}s.egin{itemize} item extbf{ac{CM}:} All the nodes which become part of the cluster and participate in the communication within the cluster. item extbf{ac{CH}:} Each cluster is supposed to elect one of the ac{CM} to act as the ac{CH} based on some rules. It is possible for any ac{CM} to be elected as the ac{CH} but some of the algorithms may apply special requirements for a ac{CM} to be elected to ensure stability of the cluster. The responsibilities of a ac{CH} may vary but in general, it is responsible for the maintenance of the cluster (addition and removal of nodes) and communication with the external nodes (other ac{CH}s and ac{RSU}s). item extbf{ exttt{HELLO} Message:} Also referred as exttt{DISCOVER}, is the first message sent by a vehicle to identify the presence of existing cluster. This is mostly a broadcast frame. item extbf{ exttt{INVITE} Message:} This is transmitted by a ac{CH} or a ac{CM} in respond to a received exttt{HELLO} message if the requesting vehicle is found fit to join the cluster.end{itemize}cref{fig:cluster} shows a typical cluster organization with one ac{CH} and multiple ac{CM}s.egin{figure}h% centering includegraphicswidth=0.9 extwidth{figures/cluster} caption{Typical ac{VANET} cluster } label{fig:cluster}end{figure}chapter{Clustering methodologies}label{sec:methodologies}In order to achieve a robust and effective communication in the high mobility environment, ac{VANET} applications use variousparameters to cluster the nodes into meaningful sub-groups based on the application requirement. This leads to a widerange of clustering methodologies which define applications specific algorithms useful for respective application ora generic algorithm with possibility to handle different requirements. In this chapter, we discuss some of the commonparameters used individually or in combination with each other to define thresholds for vehicles to join cluster andcluster head selection along with some methodologies which uses them for clustering.section{Clustering Parameters}Parameters play an important role to build stable cluster effectively which can be used to perform the applicationspecific communication with minimal overhead. Some of the common parameters which are used by the algorithms toform basis for threshold calculation are summarized in cref{tab:parameters}.egin{longtable}{>{
aggedright}p{3.5cm}p{7.5cm}} hline Parameter & Description \ hline endhead hline caption{Common clustering parameters}label{tab:parameters} \ endfoot hline caption{Common clustering parameters contd…} \ endlastfoot Vehicular mobility & This is the most common parameter used in the algorithms. Mobility of the vehicles are measured in terms of relative velocity and average velocity over time of the vehicle \ Direction of the travel & In many cases, information to be shared between vehicles only has relevance if the vehicles share the same path and direction. This knowledge can be used by the algorithms for forming trajectory tables or assigning Road IDs to compare directions and route \ Destination & Used by applications which give importance to route taken by the vehicles to provide longer stable cluster \ Density & Mostly used to differentiate sparse and dense networks and define different communication model to ensure reliable communication in both scenarios \ Unique ID & Simplest clustering parameter, commonly used to identify and cluster vehicles requiring multi-hop reliable communication \ Location & Used with application requiring location based information such as intersection support and congestion avoidance \end{longtable}section{Typical clustering Operations}To group vehicles togethers there are two main set of operations used by the clustering algorithms. First are the cluster creation operationsas shown in cref{fig:creation}.egin{figure}h centering subfloatCluster creation with single vehicle{label{fig:operation1}includegraphicswidth=0.9 extwidth{figures/cluster-step1}}% vfill% subfloatCluster creation with multiple vehicles{label{fig:operation2}includegraphicswidth=0.9 extwidth{figures/cluster-step3}} caption{Cluster creation operations} label{fig:creation}end{figure}As the name suggest these operations are used to create a new cluster after verifying the fitness of theparticipants. cref{fig:operation1} shows a case where there is no vehicle around. The vehicle first sends the exttt{HELLO} messages todiscover presence of other cluster and after a certain timeout identifies that there is no one around and starts a new cluster, becoming thenew ac{CH}. The second operation depicted in cref{fig:operation2} is used when there are vehicles in the vicinity which have not yet formedany cluster. In this case the vehicles discover each other via exttt{HELLO} messages and form a cluster followed by an election of theac{CH}.The second set of operations are used for cluster maintenance shown in cref{fig:maintenance}. Cluster joining operations shown in cref{fig:operation3}is used to add new members to the cluster. The request to join is initiated by the vehicle by sending the exttt{HELLO} message. When the ac{CH} receivesthis message, it verifies if the vehicle is fit to join the cluster and then invites it using exttt{INVITE} message. Cluster merging happens when twoac{CH} come in contact with each other as shown in cref{fig:operation4}. At this point the ac{CH}s decide to merge the clusters if the current state ofthe two cluster is similar in terms of the cluster parameters. After merging, one of the existing ac{CH} becomes the new ac{CH} of the merged cluster andthe other ac{CH} becomes a ac{CM}.It should be noted that the actual implementation of these operations may change from one methodology to another depending upon the parameter and theuse case for the clustering.egin{figure}h centering subfloatCluster joining{label{fig:operation3}includegraphicswidth=0.9 extwidth{figures/cluster-step2}}% vfill% subfloatCluster merging{label{fig:operation4}includegraphicswidth=0.9 extwidth{figures/cluster-step4}} caption{Cluster maintenance operations} label{fig:maintenance}end{figure}section{Methodologies}This section would present some of the methodologies which are popular and used as the basis for several algorithms designed for clusteringin ac{VANET}.subsection{Clustering using vehicular mobility}Vehicular mobility is one of the common and vastly used methodology for clustering. Relative velocity which can be used to differentiatethe vehicles into different sub-groups is used as one of the main parameters in such algorithms. The vehicle mobility is the main elementaffecting the network topology which the algorithms use to estimate dynamicity of the network and improve stability. Clustering algorithmspresented by extcite{ARKIAN2014197, 6737622, 6077004} use relative velocity as the main parameter to define the cluster joining and clusterhead selection metrics.subsection{Clustering using direction and destination}It has been identified that vehicles which travel in same direction or to same destination are supposed to be benefitedmost if they share information to each other. This makes the direction of travel and destination based clustering otherimportant parameters used for clustering. This methodology requires the vehicles to be equipped with ac{GPS} devices toget accurate location information. There can be variations based on the calculation and comparison of the routes of thevehicles. The methodology presented by extcite{6685518} uses trajectory tables to store position information which isthen shared and compared by the vehicles to check the direction and route of travel whereas methodology presentedby extcite{5416361} calculates the direction at intersection points based on the turn the vehicles are going to take. extcite{5735785} has proposed a method which uses lane based information to decide the direction and cluster the vehicles.subsection{Clustering using vehicular density}Density of the vehicles varies based on the environment considered. This is often utilized by the clusteringalgorithm as an important parameter to define different communication methodologies for dense network in city vssparse network on highways. Aim of such algorithms is to reduce network congestion in cities by avoiding unnecessaryflooding and provide longer network coverage on highways using long range communication. extcite{4976256} discussesone such algorithm which uses density based clustering to define different communication model based on connectivity andlink quality estimates.subsection{Hybrid clustering}Some application may require use of more than one parameter to make decision. Clustering algorithms defined for suchapplication are complex and use multiple parameters in combination to each other. The clustering scheme proposedin cite{6077004} uses a combination of location, vehicular velocity and destination information to build up clusterswhich can adapt with changes in any of the parameters. Such methods are always complex and require a lot of communicationbetween the vehicles to share real-time information.subsection{Multi-hop clustering}The methodologies summarized till now in this section relies on one hop communication i.e. direct communication betweenthe ac{CH} and the ac{CM}. The one hop based communication clustered face the problem of frequent handoffs betweenthe clusters for members with high mobility. Multi-hop clustering methods try to address this issue by including N-hopmembers to the cluster. As shown in
ef{fig:2hop} and
ef{fig:3hop}, the multi-hop members communicate to the ac{CH}via the intermediate members and can be located a maximum of N-hop distance away from the ac{CH}. In a multi-hopclustering methodology, the exttt{HELLO} messages contain additionally the number of current hops and are re-broadcastedby a node after adding themselves as an intermediate node. This forms the basis for identifying routes to the N-hop nodesafter clustering. extcite{Zhang2069135} and extcite{6554933} present clustering methods based on multi-hop schemesalong with vehicular mobility.egin{figure}h% centering subfloat1 Hop{label{fig:1hop}includegraphicswidth=0.3 extwidth{figures/1hop}}% hfill% subfloat2 Hop{label{fig:2hop}includegraphicswidth=0.3 extwidth{figures/2hop}}% hfill% subfloat3 Hop{label{fig:3hop}includegraphicswidth=0.3 extwidth{figures/3hop}} caption{Multi-hop clustering (based on~citeFigure~1{6554933})}% label{fig:multihop}%end{figure}chapter{Evaluation and analysis techniques}label{sec:evaluation}Implementing a proposed solution for ac{VANET} with real vehicles for testing is not straight forward.It always requires availability of various equipment and testing environment to implement a prototype system whichwould be suitable for evaluating the proposed method. Due to this, most of the authors of the proposed solution usesimulation as the main method of evaluation and producing data for evaluation. This chapter will discuss some ofthe important tools and techniques used for evaluation of clustering methodologies for ac{VANET} and presentthe common metrics derived from the simulation data for comparison.Simulation for ac{VANET} involves two aspects, first is modelling the vehicular mobility and second is modelling thenetwork communication between the vehicles. Another major challenge is to define the interlink between these modelsto build a realistic simulation environment. Some of the popular network simulators used are OMNET++footnote{url{https://www.omnetpp.org/}},ns3footnote{url{https://www.nsnam.org/}} and VanetMobiSim~cite{4127230}. Vehicular mobility can be defined by SUMO~cite{dlr71460}which is most popular tool capable of managing vehicular mobility via predefined traffic scenarios or some specific mobility model. extcite{sommer2011bidirectionally} presented ac{VEINS} which defines extensions to interconnect OMNET++ and SUMO. A combination ofthese tools is used to provide simulation environments for evaluating the clustering methodologies.section{Important parameters for evaluation}The simulation parameters play important role in effectively producing data which is suitable to derive metrics for evaluation.It mostly depends on the characteristics of the simulated model which in this case is clustering methods so mostly dependson the parameters~cref{tab:parameters} chosen for clustering and some typical networking and vehicular mobilitycharacteristics. cref{tab:simparams} lists down some of the parameters along with their typical values in simulation environment.egin{table}h centering egin{tabular}{>{
aggedright}p{5.4cm}p{3.4cm}} oprule Parameter & values \ midrule Simulation Time & SI{120}{second} – SI{400}{second} \ Route length & SI{3}{kilometer} – SI{10}{kilometer} \ Average speed of Vehicles & SI{10}{meterpersecond} – SI{40}{meterpersecond} \ Transmission rate & SIper-mode=symbol,per-symbol = p{6}{megaitspersecond} – SIper-mode=symbol,per-symbol = p{27}{megaitspersecond} \ Communication range & SI{100}{meter} – SI{400}{meter} \ Size of messages & SI{100}{yte} – SI{150}{yte} \ No. of vehicles/Flow rate & SI{1800}{vehicleperhour} – SI{3600}{vehicleperhour} \ ottomrule end{tabular} caption{Typical simulation parameters} label{tab:simparams}end{table}section{Analysis of results}Once the data is captured using the simulation of the target clustering methods, it should be used for analysis against somemetrics which allows comparative evaluation of the proposed schemes. Some of the important metrics which are used to comparethe performance of the clustering algorithms for ac{VANET} are as follows.egin{itemize} item extbf{Cluster head duration/Cluster lifetime}: Most important metrics used to measure the stability of clusters. Longer duration of ac{CH} is resultant of stable clusters which can perform better in a dynamic environment. item extbf{Number of clusters}: This metric gives quality of formation of the clusters. Higher number of clusters would lead to cluster merging and increased cluster formation overhead whereas smaller number means large cluster sizes which would result in cluster division due communication failures in high mobile environment. item extbf{Cluster member duration/Connectivity}: Longer member duration are important for stability and effective communication. Member duration is also a measure of connectivity and the link quality achieved by the clustering method which directly affects the network communication between the nodes.end{itemize}chapter{Conclusion}label{sec:conclusions}This thesis introduces the reader with the requirements for clustering in vehicular networks along with variousterminologies and methodologies proposed in literature by various researchers. It also gives a brief overviewof tools and metrics which are important for the evaluation of methodologies in ac{VANET} systems. The methodologiesmentioned in this thesis only provides a brief introduction to the main ideas for clustering in vehicular networks tothe reader.Currently, there is a huge advancement being made in the wireless communication technologies which will openvarious new opportunities for improving the communication environment for ac{VANET}s in the future.Improved communication mechanisms for ac{V2V} and ac{V2I} communication would provide more stable andreliable communication channels. Thus providing basis for improving existing clustering methodologies byextending them to use new technology and design new methodologies to achieve better performance in termsof stability and networking connectivity.I believe this thesis should be a good starting point for introducing the concept of clustering in vehicularnetwork and highlighting the important areas which the reader can further explore based on their needs.cleardoublepagelistofabbreviationsclearpagelistoffiguresclearpagelistoftablesclearpageprintbibliographyend{document}