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Communication Networks Group

Gaurav Gubbi Suresha

Internet Access in Vehicular Ad-Hoc Networks Basedon Infrastructure and LTE Communications

Masters Thesis in Communication and Signal Processing

02 January 2017

Please cite as:Gaurav Gubbi Suresha, “Internet Access in Vehicular Ad-Hoc Networks Based on Infrastructure and LTECommunications,” Masters Thesis (Mastersarbeit), Technische Universität Ilmenau, Deptpartment of ElectricalEngineering and Information Technology, January 2017.

Technische Universität IlmenauDepartment of Electrical Engineering and Information Technology


Helmholtzplatz 2 · 98693 Ilmenau · Germanyhttp://www.tu-ilmenau.de/kn

http://www.tu-ilmenau.de/kn

Internet Access in Vehicular Ad-Hoc NetworksBased on Infrastructure and LTE

Communications

Masters Thesis in Communication and Signal Processing

submitted by

Gaurav Gubbi Suresha

in the


Deptartment of Electrical Engineeringand Information Technology

Technische Universität Ilmenau

Advisors: Prof. Dr. rer. nat. Jochen SeitzDr.-Ing Maik Debes

Submission Date: 02 January 2017

Declaration

I declare that the work is entirely my own and was produced with no assistance fromthird parties.I certify that the work has not been submitted in the same or any similar form forassessment to any other examining body and all references, direct and indirect, areindicated as such and have been cited accordingly.

(Gaurav Gubbi Suresha)Ilmenau, 02 January 2017

Acknowledgement

I owe my deepest gratitude to my supervisor Prof. Dr. Jochen Seitz, TU Ilmenau,for the opportunity to work on this thesis and M. Sc. Yuri Cotrado Sehgelmeble forproviding me the topic. I am thankful for their continual feedback and support. I wouldlike to thank all my professors and teachers for giving great insights into the subjectswhich have always fascinated me.

I am also grateful to Florian Hagenauer from the University of Paderborn, whopatiently assisted me in solving many of the issues related to work. I am greatlyindebted to my parents and friends for their love, support and motivation, and last butnot the least, the Almighty for the strength to finish my work.

Masters thesis Gaurav Gubbi Suresha iii

Abstract

Vehicular Ad-Hoc Networks (VANETs) has captured the attention of automotive andnetworking faculties. With their large real-time deployments, we can foresee a saferand comfortable traveling experience. The exchange of information between vehiclesand infrastructure is always limited by utilization of network resources like bandwidth.Overuse of resources will result in poor connectivity between vehicles and infrastructureand likely increase the delay in gaining useful information or message required. Thus,connections are supposed to be validated by managing their access to various accesspoints through routing as connection links are established for short periods of time anddisconnections occur often. Routing in VANET is a challenging process mainly due tothe high mobility of vehicles and huge buildings causing shadowing in the city.

As part of this thesis, we would be studying the potential of Heterogeneous Networks(HetNets) in providing access to the Internet for vehicles. For this, we first make adetailed survey of different approaches for Internet access, then, we design, build andimplement our algorithm on OMNeT++ under various conditions. Analysis of theresults showed us that the algorithm performance is good and the Cars gained access tothe Internet in a confined amount of time.

Masters thesis Gaurav Gubbi Suresha v

Kurzfassung

Vehicular Ad-hoc-Vernetzung hat die Aufmerksamkeit der Automobil- und Networking-Fähigkeiten erfasst. Mit ihren umfangreichen Echtzeit-Bereitstellungen können wir einesichere und komfortable Reiseerlebnis voraussehen. Der Informationsaustausch zwischenFahrzeugen und Infrastruktur ist immer durch Nutzung von Netzwerkressourcen wieBandbreite begrenzt. Übernutzung der Ressourcen führen zu schlechten Verbindungenzwischen Fahrzeugen und Infrastruktur und wahrscheinlich erhöhen die Verzögerunggewinnen nützliche Informationen oder Meldung erforderlich. So sollen Verbindungendurch die Verwaltung des Zugangs zu verschiedenen Access Points durch routing wieVerbindung für kurze Zeit Verlinkung sind und Unterbrechungen, häufig auftretenüberprüft werden. Verlegung im VANETs ist ein schwieriger Prozess, vor allem aufgrundder hohen Mobilität von Fahrzeugen und riesige Gebäude verursacht Schattierung inder Stadt.

Im Rahmen dieser Arbeit würden wir das Potenzial des heterogenen Netzwerk studierenGewährung des Zugangs zum Internet für Fahrzeuge. Dazu machen wir zunächst einenausführlichen Überblick über verschiedene Ansätze für den Internetzugang, dann wirentwerfen, erstellen und implementieren unsere Algorithmus auf OMNeT ++ unterverschiedenen Bedingungen. Auswertung der Ergebnisse zeigte uns, dass die Algorithmus-Leistung gut ist und die Autos Zugang zum Internet in einem begrenzten Zeitraumgewonnen.

Masters thesis Gaurav Gubbi Suresha vii

Contents

Abstract v

Kurzfassung vii

1 Introduction 11.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Goal of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Thesis Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Background 52.1 Wireless Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Vehicular Ad-Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.1 Intelligent Transportation Systems . . . . . . . . . . . . . . . . . 82.2.2 Wireless Access in Vehicular Environment (WAVE) . . . . . . . . 9

2.2.2.1 IEEE 802.11p . . . . . . . . . . . . . . . . . . . . . . . 102.2.2.2 IEEE 1609.4 Multi-channel coordination . . . . . . . . 122.2.2.3 IEEE 1609.3 . . . . . . . . . . . . . . . . . . . . . . . . 132.2.2.4 Wave Management Entity (WME) . . . . . . . . . . . . 13

2.3 Application Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3.0.1 Safety Related Services and Use Cases . . . . . . . . . . 132.3.0.2 Non-Safety Related Services and Use Cases . . . . . . . 14

2.4 Long Term Evolution (LTE) . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Methodology 173.1 Idea Behind Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Architecture 214.1 Concept Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.2 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Masters thesis Gaurav Gubbi Suresha ix

Contents

4.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5 Implementation 275.1 Network Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1.1 OMNeT++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.1.2 INET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.1.3 SimuLTE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.1.4 SUMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.1.5 Veins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.2 Network Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.3 Model of Vehicle Node and RSU . . . . . . . . . . . . . . . . . . . . . . 32

6 Analysis 356.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.2.1 Overall Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.2.2 LTE Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.2.3 DSRC Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406.2.4 Connection Messages . . . . . . . . . . . . . . . . . . . . . . . . . 40

7 Conclusion and Future Work 457.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

List of Acronyms 46

List of Figures 49

List of Tables 51

Bibliography 53

x

Chapter 1

Introduction

1.1 Overview

In recent years there has been a very drastic increase in the vehicular traffic, accidentsand with regard to environmental pollution as well. As reported by the WHO, more than100 million people die in traffic accidents worldwide, and the resultant economic lossesare up to $500 billion each year [1]. In order to avoid such incidents and also provideinformation about the surroundings and entertainment, a new and dedicated technologywas needed. Thus Vehicular Ad-Hoc Networks (VANETs), is one such technology aimingto improve road safety and travel comfort for drivers and passengers. There is also asurvey where more than half of the consumer asked were interested in the concept ofconnected cars, in which 22% of them had no issues spending $30 to $60 a month forthe value added services while on the drive [2].As a result, in recent times there has been a huge research interest in this field

from industries, academic institutes and government agencies in terms of novel designarchitectures and implementations. VANETs is designed keeping in mind to support theintegration of increasing number of wireless products that are used in a vehicle such asremote keyless entry devices, laptops, mobiles and Personal Digital Assistants(PDAs).They can be utilized for a broad range of safety and non-safety applications, allowingfor value added services such as safety, automatic tolling, traffic management, improvednavigation and infotainment applications such as providing access to the Internet.

1.2 Goal of the Thesis

Routing in VANET in urban scenarios is a challenging process mainly due to the highspeed of the vehicular nodes and huge buildings causing shadowing in the city. Therefore,

Masters thesis Gaurav Gubbi Suresha 1

1 Introduction

inter-vehicular communications V2V suffers from the highly dynamic behavior of VANETwhere connection links are established for short periods of time and disconnectionsoccur quite often. This situation can be improved by including infrastructure support,namely RSU that are fixed nodes providing V2I communications. Additionally, somevehicle might be capable of Long Term Evolution (LTE) access and offer this service toother vehicles as well, if the LTE cell is not congested. Thus, a combination of Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I) and LTE should drastically improveInternet access for automotive communications in urban scenarios. Therefore, the goalof this thesis is to develop a concept for Internet access in urban scenarios consideringall three approaches to be used in vehicular networks and validate its effectiveness. Toachieve this goal, the following tasks have to be accomplished:

• Study the current approaches for Internet access provided in VANET

• The main task of this master thesis is to define a routing approach consideringRoad Side Units (RSU) and LTE-capable vehicles as gateways to the Internet.Some beneficial ideas investigated in the previous work item could be reused forthe new concept

• The conceived routing approach must then be implemented using OMNeT++,SUMO and Veins. The simulation model must consider an urban scenario withthe appropriate roads and traffic. Additionally, LTE coverage must be included inthe simulation model too

• This simulation model is then used to evaluate the concept. Meaningful simulationresults must be collected and compared under different conditions (LTE availability,different vehicle density, different speeds, different number of RSUs, etc.). Theresults should be analyzed to discuss the advantages and disadvantages of thedeveloped concept

1.3 Thesis Organisation

The thesis is organized as follows: Chapter 2 gives a background study and introduction todifferent network architecture, Wireless Ad-Hoc Network (WANET), VANET, WirelessAccess in Vehicular Environment (WAVE) architecture and LTE technology. Chapter3 deals with the methodology, giving insights of the idea behind the thesis and theliterature survey. Chapter 4 describes the architecture providing the development ofconcepts, scenarios and the algorithm. Chapter 5 is about the implementation, briefingabout the simulator, modules and model of the vehicles and RSU. Chapter 6 gives

2

1.3 Thesis Organisation

the setup parameters, its values and discussion about the derived simulation results.Chapter 7 is about the conclusion of the thesis and also tells about the future work


Chapter 2

Background

Network architecture can broadly be classified into centralized and de-centralized net-works as shown in Figure 2.1. In centralized network, all the nodes in the network areconnected to a central administrator or a central entity. The nodes communicate to itsintended recipient through the administrator/entity. On the other hand, the nodes inthe de-centralized network are independent. Nodes communicate to its recipient withoutany external assistance by multiple hops through shortest communication path, knownas Ad-Hoc communication.

Centralized De-centralized

Figure 2.1 – Network Architecture (a) Centralized (b) De-Centralized


2 Background

2.1 Wireless Ad-Hoc Networks

WANETs provide the concept of self-configuring nodes which can connect and commu-nicate to each other ’on the fly’. The history of WANET dates back to the DefenseAdvanced Research Project Agency (DARPA) packet radio networks (PRNet) whichevolved into survivable adaptive radio networks (SURAD) program [3]. Recent yearshave seen a new trend of industry and commercial application for the wireless ad-hoc net-works specially in the communication industry due to its benefit of being de-centralized,self-organized, self-configured and dynamic network topologies. Another major advantageof ad-hoc over traditional wired network is its capability to decide network configura-tions, routing and security ’on the fly’. Thus ad-hoc network sees its implementation inmultiple fields depending on their application such as:

• Mobile Ad-hoc NETworks (MANETs): connection of mobile devices

• Vehicular Ad-hoc NETworks (VANETs): connection of vehicles

• SmartPhone Ad-hoc Networks (SPANs): connecting peers through smartphonesusing WiFi, bluetooth etc

• Internet based MANETs (iMANETs): connection of mobile devices with thesupport of internet gateways

Figure 2.2 illustrates basic example of wireless ad-hoc network and as part of thesis,VANETs is concentrated in detail.

2.2 Vehicular Ad-Hoc Networks

VANETs refer to the network formed by the vehicles and other compatible equipmentson the fly. The equipments that can communicate with the vehicles are smartphones,RSUs, network provider base station or Evolved Node B (eNB) and various sensors likethat in toll plazas etc. The most important and distant features that separate VANETfrom other wireless communication are its ability to communicate in diverse environment,the entities i.e., both mobile and static that affect the vehicular communication andthe combination of various communication types i.e., V2V, V2I and V2P (Vehicle-to-Pedestrian). This technology is termed as vehicle-to-any device (V2X) technology inindustry Figure 2.3 depicts the same. Basic intention to form these networks are to fulfillthe intelligent application under the Intelligent Transport Systems (ITS). Cooperativetraffic management, on-board navigation, collision avoidance controls and infotainmentare few examples.

6


Figure 2.2 – Wireless Ad-Hoc Network [4]

Figure 2.3 – Vehicular Ad-Hoc Network [5]


2 Background

VANETs are special type of Mobile Ad-hoc Networks (MANETS), though being awireless network, VANET exhibits unique characteristics which are as follows,

• High mobility and dynamic topology: due to relatively high speeds of the vehiclesresulting in constant change in network topology.

• Prediction and restricted mobility modeling: vehicular mobility is defined by theroad types, traffic rules and geographical topology unlike mobility in MANETswhich are random.

• Localization: for efficient routing in VANETs, requires a Global Positioning System(GPS) which is used to detect the position of the vehicle at a given time withaccuracy.

• Delay constraints: safety applications requires the warning messages to be deliveredto a vehicle in a very strict duration of time. This one of the most importantaspect of ITS and hence no compromise here.

For the safety of vehicles and the comfort for passengers, there is a huge amount ofresearch work from industries and government agencies in many countries. Standards indifferent countries are mostly common and in sync, but have few variations in termsof application and implementations. In this thesis, United States defined Direct ShortRange Communication (DSRC) and its related standards are used and referred. Thedescription of DSRC and WAVE standards are done in the further section.

2.2.1 Intelligent Transportation Systems

VANET is an integral part of the ITS [6]. In ITS, each vehicle takes on the role of sender,receiver, and router [7] to exchange information with vehicular network or transportationagency, which then processes the information as per their respective application. Forcommunication between vehicles and Road Side Units RSU, vehicles must be equippedwith On-Board-Unit (OBU), a radio interface that ensures short-range wireless ad-hocnetworks to be formed [8]. Vehicles may posses a GPS or a Differential Global PositioningSystem (DGPS) receiver. RSU must be placed efficiently in a geographical area tofacilitate communication and are connected to a backbone network either wired orwireless. The number and distribution of RSU would be based on protocol. For example,in some protocol, RSU are distributed evenly throughout the road network, while fewprotocols require it only at intersections, others may expect it to be at region borders.Communication configurations in ITS can be made as inter-vehicle, vehicle-to-roadside,and routing-based communications. In inter-vehicle communication, vehicle-to-roadside,and routing-based communications rely accurate and up-to-date information about

8


the surrounding environment, hence requires accurate positioning systems and bettercommunication protocols. Intra-vehicular communication use technologies such as IEEE802.15.1 (Bluetooth), IEEE 802.15.3 (Ultra-wide Band) and IEEE 802.15.4 (Zigbee) tosupport wireless communication inside a vehicle. ITS also extends to a smarter worldand the same can be seen in Figure 2.4

2.2.2 Wireless Access in Vehicular Environment (WAVE)

In 1999, the U.S. FCC commission allocated 75 MHz of DSRC [10] spectrum at 5.9GHz to be used for V2V communications in VANET. The DSRC radio technology isessentially IEEE 802.11a adjusted for low overhead in the DSRC spectrum. It is beingstandardized as IEEE 802.11p (WAVE) [11]. The FCC allocated frequency range of5.850 - 5.925 GHz is associated to spectrum allocated for WAVE. It is divided into sixService Channel (SCH) and a Control Channel (CCH) of 10MHz each and a reserveguard band of 5MHz. Based on the transmission power of the antenna, up to 1Km oftransmission range cane be achieved.

Figure 2.4 – Intelligent Transport System [9]


2 Background

Ch 172 Ch 174 Ch 176 Ch 178 Ch 180 Ch 182 Ch 184R

5.8

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Figure 2.5 – DSRC Channel Allocation [12]

Channel 178 is assigned as control channel and remaining channels: 172, 174, 176, 180,182, 184 are service channels. As per FCC, channels 172 and 184 are reserved for publicsafety message exchange. Channels 174, 176, 180 and 182 can exchange non-safety dataexchange. Adjacent channels can be combined to form 20MHz service channels [13].

The WAVE standard is a collection of IEEE 802.11p and variants of IEEE 1609. TheIEEE 1609 suite of standards are utilized for the upper layers in DSRC. Network layersupports two protocols:

• Wave Short Message Protocol (WSMP): for quick message broadcasting

• Internet Protocol Version (IPvX): to handle IP data

Figure 2.6 shows the architecture of DSRC and the 1609 standard for each layer.Further, all standards are described briefly.

2.2.2.1 IEEE 802.11p

The IEEE 802.11p PHY and MAC layers are based and derived from IEEE 802.11astandard [15]. IEEE 802.11a is basically standardized for stations that have relativespeed and/or fixed indoor environments. This encouraged the committee to maintainstandard for the industries to adapt and implement them. One of the major change is tosupport Outside the Context of BSS (OCB), that is capability to exchange immediatedata exchange without the basic operation of authentication and association. In physical

10


Physical Layer 802.11p

MAC Layer 802.11p, 1609.4

Data Link Layer 1609.3

IPv6

TCP/UDP

WSMP

Man

agem

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Application Layer 1609.11

Figure 2.6 – IEEE WAVE Architecture [14]

layer Orthogonal Frequency Division Multiplexing (OFDM) is used for digital datamodulation and certain parameters modification/enhancement is listed in Table 2.1.

MAC layer follows Enhanced Distributed Channel Access (EDCA) for the mediumaccess contention. The method classifies the data into traffic categories based ontheir priority. Each Access Category (AC) is assigned a certain number of transmitopportunity known as the Transmit Opportunity (TXOP), higher the priority higheris the TXOP. The Contention Window (CW) for each of the AC is calculated as perIEEE 802.11 [16], the same is listed in Table 2.2.

Parameter IEEE 802.11a IEEE 802.11pModificationsData rates(Mbps) 6, 9, 12, 18, 24, 36, 48, 54 3, 4.5, 6, 9, 12, 18, 24, 27HalvedSymbol duration 4s 8sdoubledGuard time 0.8s 1.6sdoubledSub-carrier spacing 0.3125 MHz 0.15625 MHzHalved

Table 2.1 – Comparison of IEEE 802.11a and 802.11p


2 Background

Access Category CWmin CWmaxBackground AC0 15 1023Best Effort AC1 15 1023Video AC2 7 15Voice AC3 3 7

Table 2.2 – EDCA Contention Window

2.2.2.2 IEEE 1609.4 Multi-channel coordination

Several enhancements for channel coordination functions has been added in IEEE 802.11MAC in this standard [17], clearly defining various data and management planes. Dataplanes manages time synchronization between channel control and service channels, alsoit routes a packet to a right frequency channel. Similar to IEEE 802.11 MAC, dataplane here is divided into 8 levels of priority and further classified as AC of EDCA.Figure 2.7 shows the channel routing and four EDCA queues.

MAC Layer Management Entity (MLME) planes carries out management planefunctionalities such as multi-channel synchronization, monitoring timing advertisements,handling Vendor Specific Action (VSA). Here, the channel access is divided into 100 mssynchronization intervals, in which CCH has access for 50 ms and the other 50 ms forthe SCH.

Internal Contention

BK BE VI VO

CCH (WSM, WSA)

Internal Contention

BK BE VI VO

SCH (WSM, IP)

Channel Routing

Medium Contention

Figure 2.7 – Multi-channel coordination [18]

12

2.3 Application Use Cases

2.2.2.3 IEEE 1609.3

This standard defines the Networking and link layer services in data and managementplane [19]. WSMP and IPv6 protocols are supported in network layer and any WAVEdevice can support any one or both the protocols for their operation. WSMP allows thehigher layers to decide upon the lower layer parameters such as transmission power, datarates of Wave Short Messages (WSMs). WSMs can be a broadcast or sent to a specificnode or a specific group, into with details about its speed, position, etc. Applicationsare identified by an unique Provider Service ID (PSID), by which it is differentiatedat nodes when received. IP protocol stack supports TCP, UDP at the transport layerand IPv6 at network layer. The transmit power, data rate required for IP data shouldbe set in a profile and used as default by physical layer. Service announcements canbe made using Wave Service Advertisements (WSA) for IP based applications, thenthe link layer differentiates the data related to WSMP and IP by the ’Ethertype’ valuein the SNAP header. Design of this standard improves the air interface efficiency andsupports low latency data exchange for mobile nodes.

2.2.2.4 Wave Management Entity (WME)

The WME defines a set of management functionalities required to provide WAVEnetworking services [19]. Applications send service requests to the WME. Based on thenode, whether it is a Provider or User, the request is handled. WAVE devices can besingle or multi PHY device. A single-PHY device has one antenna and has capabilityto operate in one frequency at a time. However, a multi-PHY antenna has more thanone antenna and can simultaneously operate on multiple frequency channels.

2.3 Application Use Cases

ITS services can be broadly classified into safety and non-safety use cases [2, 20]. Theformer disseminates real-time safety-related messages, e.g., various warning messagesincluding abrupt brake warning messages, while the latter is to optimize the flow ofvehicles in order to reduce travel time and improve the road users’ experience.

2.3.0.1 Safety Related Services and Use Cases

Safety services main aim is to reduce the risk/chance of road accidents and the possibilityof deaths for vehicular users. Timeliness and reliability are the two most demandingrequirements for this kind of services. The minimum frequency of broadcasting periodicmessages of the safety service varies from 1 Hz to 10 Hz, and the reaction time of majority


2 Background

drivers ranges from 0.6 s to 1.4 s [21]. Thus, the optimum maximum latency can be setto not more than 100 ms. Safety messages are standardized as Basic Safety Message(BSM) by SAEJ2735 and Cooperative Awareness Message (CAM) and DecentralizedEnvironmental Notification Message (DENM) by ETSI.

• Cooperative Awareness Message - CAMs are periodically broadcasted to the areaof interest mainly for road warning purposes. The exchanged messages usuallyinclude the information on a vehicle’s status, type, positions, speed and so on.

• Decentralized Environmental Notification - DENMs are usually triggered by specialevents. The purpose of DENM is to notify the vehicles in the area of interest ofpotential hazards.

Few examples of use cases are [22]:

• Emergency brake light warning

• Forward collision warning

• Intersection movement assist

• Blind spot and lane change warning

• Do not pass warning

• Control loss warning

2.3.0.2 Non-Safety Related Services and Use Cases

The main focus of non-safety services is to enable a more efficient and comfortabledriving experience. The main objective of non-safety services is to enable a more efficientand comfortable driving experience. Non-safety services can be roughly classified intotwo categories, i.e., traffic efficiency and infotainment services [23]. The former is toimprove smooth traffic movement also offering secondary benefits which is not directlyassociated with traffic management [24]. For example, by efficient traffic scheduling, thetravel time and fuel consumption can be minimized. The latter provides on-demandentertainment information to passing vehicles. Compared to safety services, non-safetyservices have different QoS requirements. For most non-safety services, the minimumfrequency of broadcasting periodic messages is 1 Hz, while the maximum latency is 500ms [23].

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2.4 Long Term Evolution (LTE)

2.4 Long Term Evolution (LTE)

A vast variety of applications for road safety, traffic efficiency and infotainment areintended to answer the urgent call for smarter, greener, and safer mobility. Due tothe high speeds of vehicles and the dynamic topology changes in VANET, it is quitetough to provide satisfactory ITS services and cost only through a single wirelessnetwork. Although IEEE 802.11p is considered the default standard for vehicularcommunications, researchers have recently started to investigate the usability of LTE tosupport vehicular applications [20]. Therefore, by integrating different wireless accessnetworks, various demanding communications requirements of ITS services can besatisfied. The integration of multiple wireless technologies can be commonly termed asHeterogeneous Network (HetNet). The focus of wireless technology in this thesis is LTEand thus only LTE is being described.

LTE in a Nutshell: LTE represents the new generation of mobile radio networksdefined by the Third Generation Partnership Project (3GPP) [25]. The earlier systemslike Global System for Mobile Communication (GSM) that operated on circuit switchedmanner for real time communication was enhanced to IP based architecture like GeneralPacket Radio System (GPRS) to increase the data rates. Later, the introduction ofWideband Code Division Multiple Access (WCDMA) through Universal Mobile Terres-trial System (UMTS) to further increased the data rates. UMTS used both the circuitswitched and packet switched connections for real time and data communication purposesrespectively. In UMTS, every User Equipment (UE) is identified with an IP address fordata communication, concurrently using customary paging through the circuit switchedmethods. Whereas, LTE system is characterized by a flat all-IP architecture Figure 2.8with a reduced number of network devices.

LTE, due to its simplified architecture, can provide a round-trip time theoreticallylower than 10 ms, and transfer latency in the radio access up to 100 ms [2]. Thissuits delay-sensitive vehicular applications specially for the safety applications. Thearchitecture is composed of

• Evolved NodeBs (eNBs) - manages radio resources and handover events

• Mobility Management Entity (MME) - responsible for control procedures, likeauthentication and security and storing of UE’s position information

• Servicing Gateway (S-GW) - responsible for routing, data forwarding, and chargingby coupling with the policy and charging rules function (PCRF)

• Packet Data Network Gateways (P-GW) - an interface that allows communicationwith IP and circuit-switched networks


2 Background

Figure 2.8 – LTE Architecture [26]

The challenging demand for yet higher data rates was realized through better ac-cess methods like Orthogonal Frequency Division Multiple Access (OFDMA). Thisenabled in better features like modulation of higher orders up to 64-QAM, better band-widths up to 20MHz, up to 4x4 - downlink spatial multiplexing, peak data rates up to300Mbps/75Mbps in downlink/uplink theoretically. LTE uses OFDMA for downlinkcommunication and Single Carrier - Frequency Division Multiple Access (SC-FDMA)for uplink communications. Multiple-Input Multiple-Output (MIMO) techniques largelyimproves the spectral efficiency even at higher speeds, which makes LTE efficient inchallenging and dynamic propagation environments similar to that of vehicular commu-nications. Packet scheduler plays an important role at the eNB, it selects the trafficflow to server and based on the related QoS requirements specified by the QoS ClassIdentifier (QCI) it decides an appropriate modulation and coding scheme. QCI provideswith feedback from the UEs regarding channel quality. Additionally, LTE also supportshigh-quality multicast and broadcast transmissions through the Evolved MultimediaBoadcast Multicast Service (eMBMS) [27, 28] in the core and in radio access network aswell. It offers the possibility of transmitting data only once to a set of users registered tothat particular service, instead of transmitting data to each and every node separately.Currently there is an ongoing standardization work for LTE-Advanced (LTE-A) by

the 3GPP in order to enhance bit rate, capacity and spectral efficiency by the supportof advanced MIMO techniques, carrier aggregation and relay nodes.

16

Chapter 3

Methodology

3.1 Idea Behind Thesis

The motivation for considering HetNet can be summarized as follows:

• Vehicular networks exhibits certain specific characteristics, for example, fastdynamic network topology and high speed of vehicle, thus employing just onewireless communication technology to support all the ITS services is a challengingtask

• The effect of exponentially increasing traffic in most modern cities with highupdate rate of existing data arises an alarming issue where the huge amounts ofexisting data bypasses the capabilities of processing and transmitting within aacceptable elapsed time slot in the commonly used vehicular frameworks [2, 29]

• Multiple wireless communication interfaces are expected to be equipped in futurevehicles. Currently there has been a lot of work focusing on heterogeneous orcooperative vehicular networks with several scheduling, relay selection and resource-allocation schemes have been designed to improve the performance of vehicularnetworks [30–34]

Furthermore, each of the currently available wireless technologies offers unique benefits,but drawbacks as well.

3.2 Related Work

The most common issue with integrated networks revolves around the selection andmanagement of vehicular gateway nodes acts as communicating facility/interface be-tween the IEEE 802.11p-based VANET and the LTE. Since vehicles exhibit no similar


3 Methodology

characteristics(for e.g., Vehicles travel in random directions), it is not possible for thegateways to reach out to them nor can be placed geographically anywhere if the node isfixed. One approach used to address this is to create clusters of vehicles and select aCluster Head (CH) or a Gateway Vehicle for each cluster which could eventually becomegateways. In [35], a VANET-Third-Generation (3G) integrated network architectureis discussed where vehicles are dynamically clustered according to similar metrics. Inthese clusters, a minimum number of vehicles are equipped with both IEEE 802.11pand 3G network interfaces selected as vehicular gateways to connect VANETs to the3G network. In [36], the authors proposes an algorithm where a distinct ID is assignedto all nodes randomly and the node with the Lowest ID (LID) in a cluster is madethe CH. LID showed a better throughput when compared with the highest degreealgorithm which was dependent on the maximum number of neighbors. However, thehighest degree algorithm is better than LID in terms of the number of clusters whichare fewer in the highest degree scheme. In [37], authors proposes the concept wherethey employ taxis as mobile gateway. The source vehicle broadcasts RREQ to finddestination/gateway vehicle as similar to AODV protocol but TTL limits to 3 hops.Gateway vehicle uses 3G interface to send packet to base station. Base station forwardspacket to back-end gateway controller in order to search location of destination vehicleand forward the packet to nearest gateway vehicle. Finally, gateway vehicle forwardspacket via 802.11 interface to the destination vehicle. Similar to [37], authors in [38]makes use of buses(GPS equipped) as mobile backbone and adopts road segment basedrouting approach with street awareness, road segment is chosen and its hop count isestimated. Dijkstra algorithm selects route with minimum expected hop count andrecords road segment and estimated hop count in a route table. Then, road segmentelects a packet in a near junction which results in less bandwidth. In [39], the authorclearly classified different clustering method with brief advantages and disadvantages.

Traditional wireless protocols like AODV [40] and DSR [41] are not very apt forVANETS as they do not cope efficiently with rapidly changing topology due to highmobility rate and relative speed. This is shown in [42], where the authors have analyzedAODV, DSR and Swarm Intelligence Routing Protocols and concluded that AODV andDSR are not suitable for VANETs. One way to model a more realistic urban trafficenvironment is to use Vehicular Mobility Model(VMM) as in [43], where the authorsevaluate AODV and OLSR [44] in urban environment under realistic mobility patterns.An adaptive connectivity aware routing (ACAR) protocol selects an optimal route withthe best network transmission quality based on the statistical and real-time density datathat are gathered through an on-the-fly density collection process is proposed in [45].In order to achieve high end-to-end packet delivery ratio with low overheads, MURU, a

18

3.2 Related Work

multi-hop routing protocol which selects a robust paths in urban environment is proposedin [46]. GPSR [47], a location information based routing protocol was introduced. GPSRselects node which is closest to the destination node. This algorithm recovers localmaximum by routing along the perimeter. Since local maximums occurrence is morein the urban scenario GPSR is not much suitable for VANET, as reported by [48].There are proposals for Geographical Source routing [49] protocols with environmentalawareness combined with position-based routing.


Chapter 4

Architecture

4.1 Concept Development

Intensive investigations and trials have been carried out on heterogeneous networksto support ITS services. Although clustering is a good mechanism that addresses theproblem of creating interface/gateway for communication, the cluster head electionprocess was based only on a single metric which could result in the cluster heads notpossessing all the essential credentials to manage the VANET clusters. CH enableentire cluster to communicate with the LTE backhaul, especially when it comes togroup communication. Modes of multicasting included a mesh-based and a tree-basedmulticasting. The mesh-based multicasting is more scalable resulting in significantoverhead which affect the VANET performance. A tree-based could reduce the amountof overheads but it is not as robust as a mesh-based. In dynamic scenarios, bothrobustness as well as limited overheads are vital requisites because of the need tosustain the interconnectivity with the LTE back-haul in spite of the network dynamics.Identifying the traffic profiles for different cluster and scheduling priority by the LTEfor end-to-end communication is another important issue.

Analysis of pros and cons of the studied algorithms, an attempt to build a HetNetby combining advantages of multicasting, making use of CH selection and the conceptof [30], wherein, cooperative communication is utilized in vehicular networks to improvethe performance by employing V2V communication to relay data for V2I communication.The same is explained briefly in the next section.


4 Architecture

4.2 Scenario

Focus of the work is to provide Internet to vehicles in the network by instantly adapt-ing to the real scenario without prior knowledge of other vehicles’ position/status orgeographical database, and connection management supported by V2V and V2I com-munication. In most scenarios surveyed eNB chose the route, CH selection, etc, but,in this work the concentration is to make the vehicle decide the route or the gatewaythrough which they connect to Internet. Thus making the network more independentand robust to central infrastructure breakdown like eNB or RSU.To realize this, two types of vehicles, a network with IEEE 802.11p and LTE archi-

tecture are considered. One type of vehicle which is capable of only IEEE 802.11pcommunication is termed as Normal Cars and the other type of vehicle which is capableof both IEEE 802.11p and LTE communication is termed as Special Cars. We also con-sider that both RSU and eNB are connected to the server. Normal Cars access Internetthrough DSRC communication with RSU and Special Cars Communicate through LTEcommunication with eNB. In cases where the RSU is busy or unavailable the Internet

RSU

- Special Car

- Ordinary Car

Internet

- DSRC Communication

- LTE Communication

eNB

Figure 4.1 – Scenario

22

4.3 Algorithm

access for the Normal Car is interrupted, thus it makes use of DSRC communicationto Special Cars to gain access to Internet. Special Cars in turn communicates witheNB and relay the message to Normal Cars through DSRC. The same is depicted inFigure 4.1, realizing cooperative communication.

4.3 Algorithm

As discussed above, Normal Cars send DSRC messages to RSU and Special Cars sendLTE messages to eNB for Internet access. Areas of no RSU coverage can be due tofailure/maintenance, insufficient placements of RSU, at cell edges, etc, thus the NormalCars suffer from no connectivity to the server. In order to fill this gap and providemaximum connectivity to these blind spot, we make use of the V2V communicationbetween Normal and Special Cars, thus establishing cooperative communication. Thisapproach followed is depicted in the flowchart as in Figure 4.2.

Start

Send Connection message to RSU

Receive connection reply

from RSU in 500ms?

Send DSRC message to Special Cars

Process in Special Car

Start

Send Normal Car s message to

eNB

Receive reply from eNB?

Send eNB s message to

intended Normal Car via DSRC

End

Drop message

Select Special Car whose reply

arrives first

Send messages to the selected

Special Car

End

Send messages to RSU

For every 5

sec

Figure 4.2 – Flowchart


4 Architecture

Since the scenario represents Urban environment we assume LTE coverage is goodto serve the entire area of simulation and Special Cars will have connectivity at everypoint of time. For ease of explanation lets consider a scenario of a single Normal Car(N1), an eNB and four Special Cars (S1-S4), the same is shown in Figure 4.3. Initiallywhen N1 switches ON and requires Internet access, it first sends a connection messageto RSU and awaits for a reply. The maximum latency for a non-safety application is500ms [23] and hence the same is taken in our algorithm. When RSU replies to theconnection message within the time frame, N1 will send rest of the messages to RSU. IfN1 receives no reply within the time frame, it broadcasts a DSRC message. Also, letsassume S1 - S4 are in range to receive the DSRC message, after receiving message theywill send N1’s message to the eNB via LTE, S1 - S4 sends back the eNB’s reply to theN1 via DSRC. Post reception of the replies, N1 would have four routes. N1 selects theroute of Special Car whose reply will arrives first. Lets assume S2 would have send thefirst reply to N1, N1 will send rest of the messages to S2 only, thus access is provided tothe Car.

Since we consider vehicles that are mobile most of the time, there is possibility thatthe Normal Car connected to either RSU or a Special Car may go out of range and thusto avoid connection break, we introduce a maintenance in our algorithm. Accordingly,every Normal Car irrespective of the source of Internet access will send a Connectionmessage to RSU and the process again repeats, the same can be seen in Figure 4.2. Thusthe algorithm provides constant check over the connection. The interval of connectionmessage sent is 5 seconds, this is an arbitrary value. This value can be changed, thereason for us to consider this value is an assumption of two reasons: First, if the Caris at a junction where the traffic would be relatively high, the RSU may not be ableto handle the load and may not provide connectivity. Secondly, the car’s speed wouldbe relatively lower in urban roads and given that the range of DSRC is approximately400m, the vehicle may take a bit more than 5 seconds to get out of range. Thus to beon safer side we considered 5 seconds.

24

4.3 Algorithm

N1

S1

S2

S3

S4

eNB

Figure 4.3 – Example Scenario for Explanation


Chapter 5

Implementation

5.1 Network Simulator

The simulation software models the functionality of a process and environment based ontheoretical and quantitative analysis. It is extremely useful in designing, developing andtesting of products without the actual use of the infrastructure. Network simulator isone such software which mimics network behavior in various scenarios. Due to structuraland procedural complexity of networks, simulators are a must need. Many providereusable and configurable components for layers, messages, events which can be easilyprogrammed. Many provide graphical user interface for visualization of the simulations.Popular networks simulators available are NS, OPNET, NetSim. OMNeT++, anacademic version of OPNET is used in this thesis.

5.1.1 OMNeT++

Omnet++ [50] is an open source discrete event simulator for wired and wireless networks.The tool has the features of being extensible, component-based, hierarchical and modular.The tool is written in C++, built on Eclipse based IDE. The tool is used majorly forresearch and education purposes. The GUI and modularity in architecture give the toolits ease of use characteristic. The INET module of the tool includes protocols at differentlayers that are necessary for wired and wireless networks. The modular architecture ofthe tool helps to integrate the modules and packages. The basic entities of the tool,written in C++, can be modeled into complex structures using higher level languagelike Network Description (NED). The simple modules can be connected through gatesto form a compound module and these modules communicate through messages. Thenesting of these modules by connection through gates is not limited. As the tool isbased on kernel library, the simulator adds multiple run time simulation environments


5 Implementation

like graphical (Tkenv), command line (Cmdenv), a compiler for NED and GraphicalNetwork Editor (GNED). This gives an advantage of the simulation by supportinggraphical, animated interpretation or batch execution through the command line. Thefunctionalities related to ad hoc networks, sensor networks, Internet protocols etc areavailable as part of the tool. The tool also supports distributed parallel simulation. Theadvantages of the tool includes the convenience of GUI, easier tracing and debugging,good code base, in-tool graphical support, good technical support from online forums.There are many well defined frameworks in OMNeT++, serving a particular domainof networks. Commonly used frameworks are INET, INETMANET, MiXiM, Castalia,SIMULTE. As INET, SimuLTE and Veins are used in this thesis, they are described indetail.

5.1.2 INET

The INET [51] package consists of the main building blocks for most of the wired andwireless architectures in Omnet++. The IPv4, IPv6, ICMPv6 at network layer, TCP,UDP and SCTP at the transport layer and multiple protocols at the application layerare supported. The protocols like PPP, 802.11 and Ethernet are supported at the linklayer. The routing can be handled through auto configuration i.e., static routing or oneof the many routing protocols that are part of the package. The modules for routingtable, interfaces and their recorders are added as part of this package. Network layersupports both IPv4 and IPv6 related protocols. The error handling is added throughICMP. The application layer that is connected to TCP and UDP can have multipleapplications running. The Relay unit is supported through the package to supportswitching of packets among the Ethernet or between different wireless architectures.The IP layer, UDP has been inherited from this module.

5.1.3 SimuLTE

SimuLTE [52] implements eNB and UEs as composite modules associating themselvesand other the entities (e.g. routers, applications) to compose networks. The Bindermodule can access every other node in the system, as can be seen in Figure 4.2, storingdata about their connections to other nodes. Binder, in particular locates the interferingeNBs in order to compute the inter-cell interference perceived by a UE in its servingcell. UEs and eNBs are further composed of many simpler modules. The tool supportsapplications like video streaming, VoIP, real time gaming and FTP. The RLC hasfeatures like Unacknowledged Mode(UM), Acknowledged Mode(AM) segmentationand reassembly and retransmissions (AM Mode only). The MAC layer has beenimplemented with features like buffering, CQI reception, selection of transport formats

28

5.1 Network Simulator

and support to analysis of cross-layers. The PHY layer supports features like SINRcurves, CQI computation, transmit diversity. The UE related features like mobility andinterference have been designed. The package supports different types of cells for eNB’s:Macro, micro and pico cells. Also supported are features like Inter-eNB Co-ordinationusing X2 interface, Co-ordinated Multipoint (CoMP). Different scheduling algorithmsare supported by the system - Proportional Fair (PF), Round Robin (RR) and MaxChannel/Interference (Max C/I).

5.1.4 SUMO

Traffic simulators are necessary to mimic the real world traffic conditions. SUMO[54], a microscopic traffic simulator is built by German Aerospace Center (DLR) [55].The microscopic simulators, models the traffic flows in detail, featuring tracking theinteraction of individual vehicles. It includes features like vehicle movement withoutcollisions, support of different types of vehicles, multi-lanes, routing of individual vehicles,dynamic routing, buildings and a friendly GUI. The real time maps can be importedfrom Openstreetmaps [56]. The traces generated as part of the simulator however, arenot compatible directly with the network simulator, thus an extension needs to be used.

Figure 5.1 – SimuLTE Architecture [53]


5 Implementation

5.1.5 Veins

Sommer et al [57] developed and proposed a framework for Inter Vehicular Commu-nication (IVC) based on OMNeT++ and SUMO, connected through The TCP layerprotocol, Traffic Control Interface (TraCI) as can be seen in Figure 5.2. This toolinherits the features like visualization, GUI support, support of a huge code base andease of coding from OMNet++. Network and rules for the streets and lanes, maximumspeed, obstacles can be captured through the configuration file. OMNet++ supportsannotation to display the connections and communication path between the nodes. ThePHY layer implementation is largely imported from MiXiM framework, supportingmultiple radio features like radio propagation, delay, multipath, SINR. The MAC layercan be implemented from IEEE802.11p and IEEE1609.x family standards and WSMPis the messaging protocol that is supported. The network layer can import multiplerouting protocols from frameworks like INET. The extension of this framework forLTE, VeinsLTE [58], has been modeled to cater the vehicular networking through LTEframework. The extension inherits the features of LTE architecture from the SimuLTEpackage of OMNet++.

5.2 Network Modules

Network module binds together all the communicating nodes with the modules requiredfor managing and coordinating connections between the nodes. Different modulesfrom the above mentioned packages have been configured to setup the simulation onOmnet++. Figure 5.3 shows all the modules required for simulation of vehicle to vehiclecommunication. Functions of all the modules is described briefly.

Figure 5.2 – Veins Architecture [57]

30

5.2 Network Modules

Annotation Manager

Interface Table

Routing Table/Recorder

Mobility

Obstacle Control

TraCIScenario Manager

IP Network Configurator

LifeCycle Controller

Connection Manager

Notification Board

Figure 5.3 – Network Module

• Annotation manager - Visualization of connections is drawn through this module.Message names, time of sent messages and the participating entities is displayedby drawing a line from the sender and receiver on reception of message. Thismodule helps in understanding the flow and exchanges of the messages to andfrom the nodes for the developer.

• TraCI Scenario manager - Running instance of the SUMO is connected to theOMNeT++ simulation through this manager. Bidirectional coupling using TraCIprotocol is implemented in this module. The vehicle nodes are created dynamicallyas per the road traffic scenario specified in the SUMO route xml file.

• Connection manager - This module coordinates the connections between all thenodes. Connectivity in different ranges of frequencies can also be modeled throughthis, which would be essential as a single set at a frequency can affect every receivernode listening in that frequency. Channel parameters like interference distance,obstacle shadowing and antenna parameters like maximum transmission power,carrier frequency affect the links. Losses caused by the surroundings are modeledas an analogue model, where, it filters the transmitted signal according to lengthof transmission in that channel.

• Obstacle control - This models the obstacles in the surroundings which affectsthe signal traveling in the channel. Walls and buildings have been modeled as


5 Implementation

objects to create the obstacles for simulation. The module is of major significancewhile defining different environment topologies like urban, rural or highways forvehicular networking scenarios.

• Mobility - The TraCIMobility and VeinsMobility are mobility modules used inTraCI and Veins packages. The VeinsMobility module imparts a realistic mobilitypatterns for vehicular movement.

• Lifecycle controller - Manages starting, shutting down, suspending and restartingoperations of nodes in the network.

• Routing table/recorder - It records updates in routing and interface table changesof all nodes in the network into the file name specified. The member functions inthe module also enables addition, removal of routes. This assists in functionalitieslike enumeration and finding the best possible routes to the destination.

• IP Network configurator - This module is imported from INET and assigns anetwork address(IPv4 or IPv6) to every node that is added dynamically or duringthe beginning of simulation.

• Interface Table - This module stores host interfaces addresses like the wlan0, eth0.Dynamic registration of interface happens during the initialization through theNetwork Interface Cards (NIC).

• Notification Board - This module is used when nodes are to be notified about thechanges during simulation, for example, update in routing and interface tables,addresses, positions of nodes.

5.3 Model of Vehicle Node and RSU

As the connection setup was the major focus of the work, a scenario was to be built wherethe optimization of connections was to be tested among vehicles and infrastructure. Thecellular architecture was also to be tested in the case of vehicular networking to assesstheir scaling abilities. The tool was chosen to support both the cellular architecture andIEEE 802.11p for vehicular networking. Thus an object model of a realistic vehicularscenario with a V2V support from IEEE802.11p and V2I infrastructural support fromLTE was the basic building block of the implementation.VeinsLTE framework provides an architecture for the Vehicle node is depicted in

Figure 5.4, where, the node has an application layer followed by a decision layer andsubsequently two stacks [58]. Two stacks for the node are meant to support two different

32

5.3 Model of Vehicle Node and RSU

Application Layer

Decision Maker

HeterogeneousToLTE

LTE NIC

Network Layer

UDP

IEEE 802.11p NIC

Veins Mobility

IP Configurator

Routing Table

Interface Table

Notification Board

Figure 5.4 – Model of Vehicle Node

network architectures in vehicular communication, i.e., IEEE 802.11p and LTE. TheIEEE 802.11p stack supports a direct communication among the vehicles without anyassistance of central architecture through DSRC. The LTE stack, which is imported fromthe SimuLTE framework, provides the cellular basis for the communication. The DecisionMaker layer decides upon the switching between the two architectures. Application willhave the freedom to switch between the architectures or it can let the Decision makerto choose with the switching.

For the thesis, the algorithm designed, is written into the application layer. Internetaccess or connection to the server through DSRC and LTE have been establishedSeparately in the application layer to enable ease of performance analysis during thereception. For LTE infrastructure, a server at the eNB has been configured to providethe Internet access. In case of the IEEE 802.11p, the RSU provides Internet accessthrough DSRC and architecture for the RSU is show in Figure 5.5.IEEE 802.11p NIC has the basic wireless functionalities specified through the PHY

and MAC layers. The PHY layer is based on IEEE 802.11p standard, it enablesthe radio functionalities and supports the following analogue models: Simple pathloss model, Log normal shadowing, Jakes fading, Two ray interference model, Simpleobstacle shadowing, PER model. The transmission range of a node will depend on thedistance between transmitter and receiver, transmission power, channel attenuation,interference. The MAC layer implements the basic features of IEEE 802.11p and 1609.4,it enables the QoS features, carrier sensing, time synchronization etc. It switches channel


5 Implementation

Physical Layer

802.11p MAC

Application LayerMobility

IEEE 802.11p NIC

Figure 5.5 – Model of RSU Node

frequencies between CCH and SCH channels every 50 ms. LTE stack is made of aheterogeneousToLTE, UDP, network layer and a NIC. HeterogeneousToLTE layer isused to address the dynamically added vehicles into the simulation. The UDP at thetransport layer enables connection between the LTE architecture and the applicationlayer. The network layer with basic auto host configuration provides static routingfor the packets. The network layer connects the eNB to the server through Peer toPeer connection. The LTE NIC consists of the main LTE stacks like Packet DataConvergence Protocol - Radio Resource Control (PDCP-RRC), Radio Link Control(RLC), MAC and PHY layers. The PDCP-RRC layer takes care of functionalitieslike header compression, Connection Identifier (CID) allocation and management thatidentifies every connection separately. The RLC layer takes care of transferring of packetsto or from PDCPRRC depending on the modes like AM, UM, TM. MAC layer adds thefunctionalities like adaptive modulation and coding (AMC), scheduling, encapsulationof packets to transmit frames to higher or lower layers, channel feedback managementand Hybrid automatic repeat request (HARQ). The PHY layer includes functionalitiesfor radio signal transmission like handling of control messages, computation of channelfeedback through the Channel Quality Indicator (CQI), reception and transmission ofthe data defining profiles related to radiation for pico, micro and macro cells.

34

Chapter 6

Analysis

The concentration or goal is to provide Internet access in Urban Environment, it would bebeneficial to start the analysis in a small scale area and putting the proposed algorithmto its paces and deriving the limitations for the larger urban environment. Hence tostart with, we use the scenario defined by Florian Hagenauer in [58] to build and testour algorithm.

6.1 Setup

The playground for the simulation can be visualized in Figure 6.1. The map consists oftwo perpendicular roads intersecting each other at the center and length of each road is

Figure 6.1 – Map Used for Simulation


6 Analysis

400m. Choosing this scenario has another reason for our simulation, i.e., RSU can coverthe area.

Table 6.1, Table 6.2 and Table 6.3 are the design parameters used for the simulation.RSU and eNB is placed in the center in Figure 6.1 to provide coverage to the entirearea. Analysis of the scenario is done through testing the algorithm performance interms of delays by varying the number of cars in the simulation and its load.

6.2 Simulation Results

Simulation is carried out by running 50 cars with each car having a message to send atevery second, then, the load is further increased to 100ms and to 1ms intervals. Thesame is simulated with 100 and 200 cars. We are interested in observing the variationsin delay with varying loads and number of cars. We observe the Overall Delay with itscorresponding DSRC and LTE delays.

6.2.1 Overall Delay

Figure 6.2 and Figure 6.3 shows the graph for the Overall Delay of the network with loadat every second and every 100ms respectively. For load with every second we observethat the delay for simulation with 50 and 100 cars is almost similar with maximumdelay of approximately 50ms and 38ms at peak times where the maximum number ofcars appears in the simulation area.

Simulation with 200 and 400 cars also exhibits characteristics like the 50 and 100 carscurve but with higher delays at the peaks. The interesting feature to observe here isthat delay remains constant for certain period of time and at peak a sudden increase indelay appears. For every doubling simulation, say, 50 and 100 Cars, 200 and 400 Cars,the doubled number i.e., 100 and 400 cars respectively has lesser peak delays but thedelay prolongs for a considerable amount of time. Whereas, the 50 and 200 cars’ delayreduces in comparatively lesser time.

Simulation Parameters ValueSimulation Area 400 * 400mNumber of Cars 50, 100, 200Speed Limit 13.89 m/sFading model SimplePathlossModelSimulation Time 500 sData Size 1 MB

Table 6.1 – Simulation Parameters

36


DSRC/WAVE Parameters ValueProtocol IEEE 802.11p / 1609Tx Power 20 mWCarrier Frequency 5.890 GHz

Table 6.2 – DSRC/WAVE Parameters

LTE Parameters ValueeNB Tx Power 45 dBmUE Tx Power 26 dBmCarrier Frequency 2.1 GHzBandwidth 20 MHz

Table 6.3 – LTE Parameters

0 50 100 150 200 2500

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Run Time (s)

Dela

y (

s)

Overall Delay with 1s Send Interval

50 Cars

100 Cars

200 Cars

400 Cars

Figure 6.2 – Overall Delay with Load Every Second


6 Analysis

0 50 100 150 200 250 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Run Time (s)

Dela

y (

s)

Overall Delay with 100ms Send Interval

50 Cars

100 Cars

200 Cars

Figure 6.3 – Overall Delay with Load Every Second

For load with 100ms, the features remain the same, but the delay is much higher,though within the recommended limit of 500ms. To further put the algorithm at itslimits, we introduce a load of 1ms with 200 cars.

0 50 100 150 200 250-5

0

5

10

15

20

25

30

Run Time (s)

Dela

y (

s)

Overall Delay with 1s Send Interval

200 Cars

Figure 6.4 – Overall Delay With Load Every Millisecond

38


Figure 6.4 shows the graph for Overall Delay with load for every millisecond. Here,we can clearly observe that the delay is totally not in favour as it exceeds way beyond500ms limit and hence, we do not further continue with simulations beyond 100msload. Another reason to limit the load to 100ms is that we can observe in Figure 6.3,simulation with 200 cars has a peak at 500ms. However a peak or two with 500ms ofnegligible duration is still acceptable.

6.2.2 LTE Delay

Entire Simulation area has LTE coverage, but it is for the Normal Cars that theconnectivity may be disrupted. So, these cars depend on the Special Cars for theirconnectivity. Hence it would be curious to check how the V2V communication affectsLTE Delay.Figure 6.5 and Figure 6.6 shows the variations of LTE Delay for loads for 1s and

100ms respectively. With loads for 1s, simulation with 50 cars has a very low delaythroughout the simulation. With increase in the number of cars, i.e., for 100 cars thereare surges at certain point of time and for 200 cars. The delay is almost even for certainperiod from the start but over the period the surges are constant, thus we can observethat post 125s there is constant surges with higher delays.In simulations with load for every 100ms, the delay was expected to be higher for

higher loads but it was not so. We can observe in Figure 6.5 that it appears to be almost

0 50 100 150 200 2500.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0.055

0.06

Run Time (s)

De

lay (

s)

LTE Delay with 1s Send Interval

50 Cars

100 Cars

200 Cars

Figure 6.5 – LTE Delay With Load for Every Second


6 Analysis

0 50 100 150 200 2500.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Run Time (s)

Dela

y (

s)

LTE Delay with 100ms Send Interval

50 Cars

100 Cars

200 Cars

Figure 6.6 – LTE Delay With Load for Every 100 Millisecond

similar but the amount of surges is very high even at lesser simulation run time. Thisillustrates that the delay with every message in a round trip is fluctuating.

6.2.3 DSRC Delay

Another curious factor is to analyse the effect of V2V for the DSRC Delay. Figure 6.7and Figure 6.8 depicts the variation for DSRC Delay.

The maximum delay for load with 1s is 20ms whereas, for 100ms load the maximumdelay reaches 60ms. In Figure 6.7, the curves have similar pattern for 50, 100 and 200cars, this is also the same with Figure 6.8.

6.2.4 Connection Messages

As part of the V2V communication, it is interesting to investigate as to how manymessages were sent to the Special Cars by Normal Cars. To illustrate the same Figure 6.9,Figure 6.10, Figure 6.11 and Figure 6.12 has been plotted with X-axis as number ofCars and Y-axis as number of messages. To illustrate less traffic, simulation is runfor 10 cars. We can observe that, in simulation with 10 cars, 2 Normal Cars send andreceive 3 messages, having 100% delivery ratio. Lets term this delivery ratio as Accessto Internet in our thesis. Thus, simulation with 10 cars have 100% access to Internet.In the regular runs with 50, 100 and 200 cars, we can observe that simulation with

50 cars with load for every 1s access to the Internet is 97%, Figure 6.10a. With load

40


0 50 100 150 200 2501.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1x 10

-4

Run Time (s)

De

lay (

s)

DSRC Delay with 1s Send Interval

50 Cars

100 Cars

200 Cars

Figure 6.7 – DSRC Delay With Load for Every Second

0 50 100 150 200 2500

1

2

3

4

5

6x 10

-4

Run Time (s)

Dela

y (

s)

DSRC Delay with 100ms Send Interval

50 Cars

100 Cars

200 Cars

Figure 6.8 – DSRC Delay With Load for Every 100 Millisecond


6 Analysis

(b)

(a)

Figure 6.9 – Connection message for simulation with 10 cars (a) Transmitted (b)Received

(a)

(b)

(a) Send Interval = 1s (a) Transmitted(b) Received

(a)

(b)

(b) Send Interval = 100ms (a) Transmit-ted (b) Received

Figure 6.10 – Connection message for simulation with 50 cars

increased for 100ms the Internet access reduces to 86%, Figure 6.10b. With 100 carsof load for every second Figure 6.11a access is 88% and with load for every 100ms,Figure 6.11b, it is 86%. 87% and 85% access to Internet is gained in simulation of 200cars with loads for 1s and 100ms respectively and the same is shown in Figure 6.12aand Figure 6.12b respectively.

42


(a)

(b)

(a) [Send Interval = 1s (a) Transmitted(b) Received

(a)

(b)

(b) Send Interval = 100ms (a) Transmit-ted (b) Received



6 Analysis

(a)

(b)

(a) [Send Interval = 1s (a) Transmitted (b) Received

(a)

(b)

(b) Send Interval = 100ms (a) Transmitted (b) Received


44

Chapter 7

Conclusion and Future Work

7.1 Conclusion

This report has provided an introduction to Vehicular Networking in terms of why itis needed, how can it be done and what are the benefits it provides. We have alsoprovided an overview of the WAVE standards, discussed various applications of VANETand proposed an approach for Internet access through infrastructure along with LTEarchitecture. We started with literature survey for different approaches for InternetAccess, methodology, algorithm development and simulation. The results are discussedin the analysis.

The simulation area that we have considered is well suited to analyse the resultsderived and give a framework/linearity for deployment to a larger urban area. Sincedeployment of more than 1 RSU in our defined area will be expensive, 1 RSU in 400*400mholds good. Various simulations were carried out to test the network and the same havebeen discussed in previous chapter.

Our algorithm has performed well in most of the cases. With respect to overall delay,cars upto 200 in the area with load for 1s and 100 ms have figures and are much withinthe limitations of 500ms. But fails to accept loads for every 1ms as we have observedthe delay exceeding way over the defined 500ms which is at time reaching maximum of25s.

Our scenario consists of a single eNB, the loads our algorithm posses on the networkis well managed by the eNB. We observe almost constant LTE delay with lesser carsand loads, but even with higher loads, the delay is not too high, in-fact it is 20ms atmaximum. So even with higher loads through our algorithm, eNB would be in a positionto handle without glitches, that is also obvious due to the capability of the eBN.


7 Conclusion and Future Work

With regard to DSRC the cooperative communication provided by DSRC and LTEcommunication is appreciable. The delay related to it the algorithm works well andwithin the confined limits. But with higher loads the delay is constantly high and thereduces only towards the end of simulation where the cars exiting out of the simulationarea.

In all we would like to conclude that the algorithm performs well within our confinedlimits to support this we can observe the percentage of cars gaining access to Internetthrough our algorithm averaging 90% overall in all conditions.

7.2 Future Work

Though the algorithm performed well we made some observations where some workcan be made in order to further improve the Vehicular Networking. In regard withcommunication with the RSU, it is weak in terms of handling capabilities. Simulationwith 10 cars and load for every 1s, 2 cars required the assistance from Special cars forInternet Access. It would be beneficial to work in this regard in order to improve thenetwork efficiency. The LTE Delay we observed did not change largely, hence it is veryuseful to get the VANET completely on the heterogeneous network. Also, we surveyedwork on VANET with LTE and LTE-A, hence we would like to make recommendationto concentrate more towards heterogeneous network. With regard to simulator, wefound insufficient support for OMNeT++, also simulation with high loads, for example,200 cars with 1ms load, it took nearly 48hrs to simulate 250s out of 500s specified andextracting results is also a huge time consuming process. VeinsLTE provides frameworkfor single eNB and causes problem with increasing the number of eNB. Hence lookingat the time consumption and difficulties in integrating different modules, we recommendin considering a different simulator which has better efficiency, speed and support.

46

List of Acronyms

VANET Vehicular Ad-Hoc Network

WANET Wireless Ad-Hoc Network

LTE Long Term Evolution

RSU Road Side Units

ITS Intelligent Transport Systems

V2V Vehicle-To-Vehicle

V2I Vehicle-To-Infrastructure

DSRC Direct Short Range Communication

WAVE Wireless Access in Vehicular Environment

eNB Evolved Node B

GPS Global Positioning System

SCH Service Channel

CCH Control Channel

HetNet Heterogeneous Network

3GPP Third Generation Partnership Project

GSM Global System for Mobile Communication

GPRS General Packet Radio System

WCDMA Wideband Code Division Multiple Access

UMTS Universal Mobile Terrestrial System


7 Conclusion and Future Work

EDCA Enhanced Distributed Channel Access

OFDM Orthogonal Frequency Division Multiplexing

48

List of Figures

2.1 Network Architecture (a) Centralized (b) De-Centralized . . . . . . . . . 52.2 Wireless Ad-Hoc Network [4] . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Vehicular Ad-Hoc Network [5] . . . . . . . . . . . . . . . . . . . . . . . . 72.4 Intelligent Transport System [9] . . . . . . . . . . . . . . . . . . . . . . . 92.5 DSRC Channel Allocation [12] . . . . . . . . . . . . . . . . . . . . . . . 102.6 IEEE WAVE Architecture [14] . . . . . . . . . . . . . . . . . . . . . . . 112.7 Multi-channel coordination [18] . . . . . . . . . . . . . . . . . . . . . . . 122.8 LTE Architecture [26] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.2 Flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.3 Example Scenario for Explanation . . . . . . . . . . . . . . . . . . . . . 25

5.1 SimuLTE Architecture [53] . . . . . . . . . . . . . . . . . . . . . . . . . 295.2 Veins Architecture [57] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.3 Network Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.4 Model of Vehicle Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.5 Model of RSU Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.1 Map Used for Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 356.2 Overall Delay with Load Every Second . . . . . . . . . . . . . . . . . . . 376.3 Overall Delay with Load Every Second . . . . . . . . . . . . . . . . . . . 386.4 Overall Delay With Load Every Millisecond . . . . . . . . . . . . . . . . 386.5 LTE Delay With Load for Every Second . . . . . . . . . . . . . . . . . . 396.6 LTE Delay With Load for Every 100 Millisecond . . . . . . . . . . . . . 406.7 DSRC Delay With Load for Every Second . . . . . . . . . . . . . . . . . 416.8 DSRC Delay With Load for Every 100 Millisecond . . . . . . . . . . . . 416.9 Connection message for simulation with 10 cars (a) Transmitted (b)

Received . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42


List of Figures

6.10 Connection message for simulation with 50 cars . . . . . . . . . . . . . . 426.11 Connection message for simulation with 100 cars . . . . . . . . . . . . . 436.12 Connection message for simulation with 200 cars . . . . . . . . . . . . . 44

50

List of Tables

2.1 Comparison of IEEE 802.11a and 802.11p . . . . . . . . . . . . . . . . . 112.2 EDCA Contention Window . . . . . . . . . . . . . . . . . . . . . . . . . 12

6.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.2 DSRC/WAVE Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 376.3 LTE Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37


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