IT 17 085
Examensarbete 30 hp
November 2017
An Urban Mobility Overlay for
Evaluating Cellular Driven Vehicle
Teleoperation
Yiqing Wang
Institutionen för informationsteknologi
Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student
An Urban Mobility Overlay for Evaluating Cellular
Driven Vehicle Teleoperation
Yiqing Wang
Cellular Driven Vehicle Teleoperation is far too expensive to deploy and implement in real life. Most research community use simulation as a solution. Nowadays most evaluations are based on one simulator either for traffic or network. In order to guarantee the safety and accuracy for operating a vehicle at distance, in this project, we first did a state-of-art study of simulators and developed traffic and network scenarios using microscopic traffic simulator SUMO (Simulator of Urban MObility) and accurate network simulator NS3 (Network Simulator 3) then integrated them together. To have the accurate result the scope of the traffic mobility model need to be realistic and fulfill several requirements and we have accomplished all the general requirements needed such as map size, mobility obstacles (traffic congestion, car density, road regulation) and realism. The process of the work is presented in this report.
We evaluate the integrated scenario from aspects of feasibility, capacity and
performance and the result shows that it's feasible to use LTE network to teleoperate up to 5 vehicles with granted Quality of Service requirements in area of Kista. A drawback of this project is the simulation is off-line. Furthermore, to simulate the behavior of on-line simulation we include traffic congestion avoidance and we run the simulation multiple times in small time scale.
Tryckt av: Reprocentralen ITC IT 17 085
Popular Scientific Summary in Swedish
Cellul¨art driven fordonsteleoperation ¨ar alldeles f¨or dyr att distribuera och implementera i verkligheten. S˚a i de flesta forsknings sammanhang ¨ar simulering en l¨osning. I dagens forskning ¨ar utv¨arderingen baserad p˚a en simulering antingen f¨or trafik eller n¨atverk. F¨or att garantera s¨akerhet och noggrannhet f¨or ett fordon i r¨orelse p˚a ett avst˚and, i detta projektet, har vi f¨orst studerat ”the state of art” av simulatorer och utvecklat trafik och n¨atverk scenarios med hj¨alp av microscopic traffic simulator SUMO (Simulator of Urban MObility) och accurate network simulator NS3 (Network Simulator 3 ) sen integrerade dem tillsammans. F¨or att ˚astadkomma ett noggrant resultat beh¨ovde omr˚adet som man unders¨okte vara realistiskt och uppfylla flertalet krav och vi hade genomf¨ort alla de generella kraven som beh¨ovdes s˚a som kartstorlek, mobilitetshinder (tr¨angsel, fordons t¨athet, v¨agreglering) och realism. Processen f¨or arbetet beskrivet ovan ¨ar presenterat i denna rapport.
Vi utv¨arderar de integrerade scenariona fr˚an de f¨oljande aspekterna genomf¨orbarhet, kapacitet och prestanda och resultatet visar att det ¨ar genomf¨orbart att anv¨anda LTE network f¨or att utf¨ora teleoperation upp till 5 fordon, med till˚aten Quality of Service krav i Kista omr˚adet. En nackdel av detta projektet ¨ar att det ¨ar en off-line simulering. Vi-dare, f¨or att simulera beteendet av en on-line simulator inkluderade vi tr¨angsel avvikande ˚atg¨arder s˚a att ”operat¨oren” ¨andrar fordonets mobilitetsm¨onster medans simuleringen
¨
This thesis is carried out in Ericsson Research in Kista and the process is quite informa-tive. During the project work i experienced how research work is done in a real industrial company.
I would like to sincerely thank my supervisor Aneta Vulgarakis, Senior Researcher at Ericsson Research, for her great help and support all the way from developing of the project to the report writing. I benefit a lot from your ideas, suggestions and comments.
I would also like to give thanks to my reviewer Edith Ngai, Associate Professor at Uppsala University for her valuable help and support.
Also credits go to my co-worker Yifei Jin, Master student in Royal Institute of Technol-ogy(KTH), for his remarkable contribution building the network scenario in the project. As well as all the other people from the research group Cognitive Automation Lab: Elena Fersman, Rafia Inam, Athanasios Karapantelakis and Kevin Wang. Thank you for the time sharing with me and the support.
Once again Thank you all.
“In a connected world, everything is possible.”
Abstract ii
Acknowledgements iv
List of Figures vii
List of Tables viii
1 Introduction 1 1.1 Vehicular Networking . . . 1 1.1.1 Vehicle-to-Everything . . . 1 1.2 Research Problem . . . 2 1.3 Thesis Objectives . . . 3 2 Background 5 2.1 Traffic Modelling . . . 5
2.1.1 State of Art of Mobility Model . . . 5
2.1.2 Comparison of Traffic Simulator . . . 7
2.2 Vehicles Routing Algorithms . . . 10
2.2.1 Heuristics in Routing: A* algorithm . . . 10
3 Implementation 13 3.1 Build Kista Scenario . . . 13
3.1.1 Topology . . . 13 3.1.2 Mobility . . . 17 3.2 Integration . . . 18 3.2.1 Real-time Simulation . . . 19 3.2.2 Off-line Simulation . . . 22 4 Evaluation 23 4.1 Simulation Summary of SUMO . . . 23
4.2 Feasibility . . . 24
4.3 Capacity . . . 26
5 Conclusion and Future Work 27
vi
1.1 An example of V2I and V2V scenario use LTE . . . 2
1.2 An example of V2X scenario[1] . . . 2
1.3 Illustration of Simulation Framework . . . 4
2.1 Level of mobility model[2] . . . 6
2.2 General concept map of mobility model generation[3] . . . 7
2.3 Example of Manhattan, Diagonal and Euclidean distance . . . 11
3.1 Select large area from OSM . . . 14
3.2 JOSM view of Kista OSM file . . . 14
3.3 After Filtering Out Irrelevant Nodes . . . 15
3.4 After Filtering Out Irrelevant Nodes and Buildings . . . 15
3.5 Kista Scenario Road Network . . . 15
3.6 Kista Scenario Topology with Polygons . . . 16
3.7 Kista Fading Map . . . 17
3.8 Overview of steps to generate SUMO map file[4] . . . 17
3.9 Bus Stop Location in Kista Scenario . . . 18
3.10 Example Bus Route No.178 from OSM . . . 18
3.11 Establishing a connection to SUMO[5] . . . 19
3.12 Closing a connection to SUMO[5] . . . 20
3.13 VSimRTI architecture[6] . . . 21
3.14 Illustration of real-time simulation work flow between SUMO and NS3 . . 21
3.15 Illustration of off-time simulation work flow between SUMO and NS2[2] . 22 4.1 Traffic Condition in Google Maps at 8:00 am . . . 24
4.2 Delay for Critical Stream in 10M Background Traffic . . . 24
4.3 Packet Loss Ratio for Critical stream in 10M and 1M Background Traffic 25 4.4 Geometry location of antenna and vehicle route . . . 25
4.5 Packet Loss Ratio for non-Critical stream in 10M and 1M Back-ground Traffic . . . 26
4.6 Capacity for Bus Router per eNB . . . 26
List of Tables
2.1 Features of Main Stream Traffic Simulators-1 . . . 9
2.2 Features of Main Stream Traffic Simulators-2 . . . 9
2.3 Features of Main Stream Traffic Simulators-3 . . . 9
3.1 Topology Information . . . 16
3.2 Number of Polygons in the Kista Scenario . . . 16
3.3 Summary of Bus in the Kista Scenario . . . 18
4.1 SUMO Simulation Summary . . . 23
CN Core Network
CQI Channel Quality Indicator eNB Evoloved NodeB
HetVNET Heterogeneous Vehicular NETwork LTE Long Term Evolution
QCI QoS Class Identifier QoS Quality of Service RAN Radio Access Network
RSSI Received Signal Strength Indicator SUMO Simulation of Urban MOblity UE User Equipment
Chapter 1
Introduction
1.1
Vehicular Networking
As the consequence of rapid development of the wireless communication and the con-cept of Internet-of-Things, vehicular communication has been gathering more and more interests among the research community and industry during recent years. Many vehi-cle manufactures have decided to include radio communication interface in their latest model of vehicles. The number of cars with wireless transmitter is increasing and enor-mous data are collected which will be beneficial for the whole society by contributing a more efficient and safer traffic environment.
1.1.1 Vehicle-to-Everything
There are several vehicular communication types namely to-Vehicle(V2V), Vehicle-to-Infrastructure(V2I), Vehicle-to-Network(V2N) and Vehicle-to-Pedestrian(V2P) with focus on different aspects. For example V2V normally refers to the communication be-tween vehicles with focus on safety. Information can be received by the vehicle nearby to the event(accident, road congestion point, etc...) and send to the vehicle approaching the incident to improve traffic efficiency and safety. V2I generally refers to the commu-nication between vehicles and the network infrastructure with focus on traffic efficiency.
Figure 1.1: An example of V2I and V2V scenario use LTE
V2X(Vehicle-to-Everything)[1] contains any types of communication as long as any ve-hicle is involved, either as source or destination. Different manufactures of veve-hicles can communicate by using wireless communication through some standardized protocols and interfaces so all the vehicles can be connected. V2X can enhance road safety and traffic efficiency by collecting and providing information to all the participants in the traffic. For example the road users can send the real time information to the traffic operator who can use the data to give better control of traffic by sending route information to other road users so they can make better decisions.
Figure 1.2: An example of V2X scenario[1]
1.2
Research Problem
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3G system from channel sizes down to 5MHz in increments of 1.5MHz[9]. Another import feature of LTE is spectrum efficieny is improved which allows more data can be transmitted in a given bandwidth[10]. IEEE 802.11p is a specific wifi standard using 5.9GHz frequency band. Compare to LTE it can provide direct communication with very small latency for direct communication among nodes but lack of spatial coverage.
Consider the high data rates, capability and reliability LTE naturally becomes a better choice for vehicular communication. Another advantage of LTE is the network infras-tructure. LTE base stations(eNodeB) normally are located in much higher places, this is helpful for our case by avoiding multi-path loss caused by vertical obstacles.
Every coin has two sides, on the other hand LTE network is suffering highly loaded background traffic which may lead to some delay sensitive communications miss their deadlines. Vehicle teleoperation requires a very responsive network between the control room and the vehicle, the latency shall be in the range of tens of milliseconds to make sure safety. Furthermore, the video streaming service from the camera in the vehicle also put a high quality-of-service requirement[11]. LTE lacks of scheduling mechanism in MAC layer for the QoS Class Identifier(QCI). Detailed descripition about LTE protocol can be found in my co-worker Y.Jin’s thesis[12].
Before deploying system to the real world, simulation is essential so potential errors to the system can be detected before implementation thus the system is predictable and reliable. Only simulating the communication network is not complete to evaluate the quality, attention to the dynamic vehicle mobility pattern needs to be paid such as topology of road network and/or the speed change due to specific constrains. Our research problem focuses on developing a real-time simulation testbed using LTE cellular network and a microscopic urban mobility model, to evaluate the realization of different QoS requirements during the teleoperation.
1.3
Thesis Objectives
project is done by two master students one focus on the Network Simulator for full-layer LTE and the other focus on the traffic simulator for microscopic mobility scenario. This thesis is focused on the latter. The objectives of the thesis are the following:
• Do a state-of-art research and evaluate different traffic simulators to determine the suitability in the simulation framework.
• Build a microscopic realistic traffic scenario of a given urban area. Our choice is Kista, Stockholm.
• Integrate the developed traffic scenario with the LTE network scenario. The traffic scenario will generate nodes following certain mobility models which are defined by the real rode map topology, traffic parameters such as speed limit, lane changes and vehicle density and so on. The integrated traffic and network simulator should be in an closed feedback loop: Network simulator calculate the network using the data(position, routes, speed, etc) provided from mobility model in traffic simulator, the network simulator could give feedback to traffic simulator to change the vehicle routes.
• Evaluate the network requirements for teleoperated vehicles in our integrated testbed.
• Evaluate the performance of our integrated testbed for teleoperated vehicles.
• Evaluate the capacity of our integrated testbed for teleoperated vehicles.
Chapter 2
Background
2.1
Traffic Modelling
Mobility model is a set of rules that regulate how the nodes(participants in the traffic) will be generated and their moving pattern. Using a simple random generated mobility model is a common practise for V2X research community in earlier years but obviously such models is not accurate enough because they are ironing many microscopic traffic factors such as car acceleration/braking, queuing in the intersection, which could affect network performance greatly. The mobility model should be as realistic as possible to describe the behavior of the traffic flow to make sure the network evaluation is accurate.
2.1.1 State of Art of Mobility Model
Mobility models are normally divided into either microscopic or macroscopic sometimes mesoscopic perspectives. Macroscopic models focus on traffic flows and road topology including constraints could affect the traffic movement such as traffic lights signals. It’s mainly used by traffic engineers for traffic planning or road topology design. There are also some research articles about mobility patterns in wireless cellular network[13][14] on macroscopic scope. Mesoscopic model study the elements in small groups and another microscopic model with focus on behaviour of individual member of traffic such as their position , acceleration/deceleration, lane changing and so on. Since we want to build a realistic scenario and study the communication performance, a microscopic mobility model will be needed and suitable.
Jerome Harri et al[15] defined that ”a realistic mobility model should include : Accurate and realistic topological maps;smooth deceleration and acceleration;obstacles; attraction
Figure 2.1: Level of mobility model[2]
points;simulation time;non-random distribution of vehicles and intelligent driving pat-terns”. All these criteria seems relevant to our scenario and will be considered during the implementation.
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Figure 2.2: General concept map of mobility model generation[3]
We need to consider several microscopic criteria to set up the rules regarding how the cars following other vehicles, how they overtaking and how they behave in the intersections. They are all important for generate realistic scenario and have several widely used models available. For example Krauss Model[19] for car following and Gipps Model[20] for Lane Changing. After years of development, advanced traffic simulators start to be used such as The Simulation of Urban MObility(SUMO) [21] and VISSIM(Verkehr In St¨adten SIMulationsmodell)[22] and they can be used to simulate urban mobility in microscopic scope which is exactly what we need.
2.1.2 Comparison of Traffic Simulator
There are several traffic simulators available today to model vehicular mobility at macro-scopic or/and micromacro-scopic level and each of them has advantages and disadvantages. The most suitable one need to fulfill several simulator related criteria such as capability for map formats, algorithms for intersection and routing, visualization for output etc. We do not have hard requirements for all these criteria but some pre-defined criteria must be focused for the selection. The traffic simulator for our project should be open-source, support at least microscopic type, can handle multiple vehicles, has resource cheap GUI, also suitable for integration with a network simulator.
The CitySim [23] simulator could simulate stochastic queue-based traffic flow in urban area for city planning purpose with a 3D graphical user interface. It could be used by authority to simulate comprehensive urban system to evaluate the traffic volumes, emissions, atmospheric dispersion and noise within cities. It is open-source but it is not a mainstream traffic simulator so there is not a big community behind it for us to seek support and it’s not designed for integration with a network simulator.
could exchange and synchronize data with a network simulator continuously using a TCP connection. The limitation is the lane connection management in complex junction and we have to pay to use the program.
The CANU Mobility Simulation Environment(CanuMobiSim) [25], is a flexible frame-work for user mobility modeling in a variety of conditions. CanuMobiSim supports maps in the Geographical Data Files (GDF) format and provides implementations of several random mobility models. It features at both macroscopic and microscopic levels. CanuMobiSim has an extension VANETMOBISIM. At macroscopic level, VanetMobiSim support for multi-lane roads, separate directional flows, differentiated speed constraints and traffic signs at intersections. At microscopic level, VanetMobiSim provides realistic V2V and V2I interactions such as Fluid Traffic Model[26]. Moreover it can generate trace files for network simulator but it’s not possible to control on-going simulation.
Initials 9
Table 2.1: Features of Main Stream Traffic Simulators-1
Open Source Microscopic Marcoscopic Map Captiable Multi Lane
VanetMobisim yes yes yes high yes
VISSIM[29] no yes yes high yes
TRANSIMS no yes yes high yes
BonnMotion[30] yes no yes high yes
SUMO yes yes yes high yes
CARISMA unknown yes yes high yes
MobiREAL[24] yes no yes high yes
CitySim yes no yes high yes
PARAMICS[31] no yes yes high yes
Table 2.2: Features of Main Stream Traffic Simulators-2
Source/Destination Position Path Computation NS support VanetMobisim random, attraction Point density/Dijkstra/speed yes
VISSIM unknown unknown yes(cpp API)
TRANSIMS random random no
BonnMotion random, attraction point density/Dijkstra/A*[32]/user define no
SUMO random, attraction point density/Dijkstra/ yes(TraCI[33]) CARISMA random density/Dijkstra/A*/user define yes
MobiREAL random, attraction point random no
CitySim unknown unknown no
PARAMICS unknown unkown no
Table 2.3: Features of Main Stream Traffic Simulators-3
GUI Radio Obstacles Plattform VanetMobisim yes yes java
VISSIM yes unknown c++
TRANSIMS yes random unknown BonnMotion yes yes java
SUMO yes yes c++
CARISMA yes yes c++
MobiREAL yes no c++
2.2
Vehicles Routing Algorithms
Routes planning is always an important factor for traffic planning and optimization. How to reach destination from source point varies in traffic dynamics(road congestion/inci-dents can happen) and the decision will affect simulation significantly. There are several routing algorithm existing for example Genetic Algorithms[34], Tabu search[35], A* al-gorithm and so on. The general process of how we implement dynamic traffic route routing is as following:
Algorithm 1 Dynamic Traffic Routing
Input: input parameters Start Position, End Position, weight: travel time/speific routes
Output: Routes to destination
Calculate the initial route according from origin to destination using chosen routing algorithm.
for each intersection do if destination reached then
Trip finished else
Check traffic condition
if Incident happen and will affect the further path then
Remove the old routing table and re-calculate the new routes else
Follow the existing routing table return Routes to destination
2.2.1 Heuristics in Routing: A* algorithm
We use heuristics path finding to calculate the shortest path. We did the calculation iterativly until converges to an equilibrium state. The key to calculate the path is the equation below:
f (n) = g (n) + h (n) (2.1)
Initials 11
the open list and n will become their parent node. Then n will be removed from the open list to the close list. Next step we need to chose one node from the open list, the one with smallest f(n) value will be chosen and then go through the same process until the destination is reached. However we can not guarantee this will always give us the shortest path as it use heuristics to sort the queue in the open list. Normally there are three heuristics to apply depending on the moving rules:
• Manhattan Distance
h (n) = abs (cur n.x − dest node.x) + abs (cur n.y − dest node.y) (2.2)
This is based on the street geography of Manhattan in New York City. We use this when that allows 4 directions of movement.
• Diagonal Distance
h (n) = max {abs (cur n.x − dest.node) + abs (current n.y − dest.n)} (2.3)
We use this when that allows 8 directions of movement.
• Euclidean Distance
h (n) = sqrt (abs (cur node.x − goal.x) + abs (cur node.y − dest.y)) (2.4)
We use this when that allows any direction of movement.
Figure 2.3: Example of Manhattan, Diagonal and Euclidean distance
Chapter 3
Implementation
Now we have discussed and chose SUMO as the traffic simulator to build our microscopic scenario for our testbed. In order to solve our research problems there are certain requirements need to be fulfilled: First it need to describe an city area including different road categories but not exceed the coverage of one LTE eNodeB. Second it need to support complex mobility modelling to represent traffic-follow in urban area. Third it should guarantee reality in microscopic level. Fourth it should be compatible with another network simulator for integration. Also the scenario should have the capability to re-use in some other user cases by the other research community.
3.1
Build Kista Scenario
3.1.1 Topology
The topology of Kista is a typical city urban area: a town center area surrounded by several neighbourhoods which are divided in different functionalists (Residential/Com-mercial/Occupation). In SUMO the road network is modeled using XML files. They can be written by hand, define map primitives such as nodes and edges and then con-nect them together, or the data can be imported from some other sources like Open-StreetMap(OSM) directly. OSM is a free user-edit project that provided map over the whole world. ”OpenStreetMap creates and provides free geographic data such as street maps to anyone who wants them. The project was started because most maps you think of as free actually have legal or technical restrictions on their use, holding back people from using them in creative, productive, or unexpected ways.”[37]. We choose OSM not only because it can provide road information but OSM also gathers information such as building shapes, parking-lot,bus stops and many other Point of Interests(POI). These
information will be needed for generating traffic base on people’s daily activities. Lo-cations and the geometries of some POIs can be used as obstacles in wireless signal prorogation which is crucial concerning to our research problem.
Figure 3.1: Select large area from OSM
The map we downloaded from OSM contains more information than we need. So we must manually select what we are interested in such as bus stops and work place locations which can affect the traffic. We do this by using an open source OSM map editor Java OpenStreetMap(JOSM)[38]. It allows to load and edit OSM data such as nodes, ways as well as their relations and metadata tags.
Figure 3.2: JOSM view of Kista OSM file
From Figure 3.2 we can see that the original OSM file directly downloaded contains too many nodes which made the map even difficult for us to observe. It is necessary to clean up the redundant objects. JOSM[38] provides a command line tool osmfilter for that propurse. We can filter out objects with specific tags using command for example:
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Figure 3.3: After Filtering Out Irrel-evant Nodes
Figure 3.4: After Filtering Out Irrel-evant Nodes and Buildings
Now the map in JOSM is clear for us to work. The topology data is actually sufficient for SUMO to run the simulation but the correctness and robustness is not guaranteed so we need to manually check the following to enhance the map:
• All the streets have tag ”highway” which will be used by SUMO to set the speed limit and the right of the lane.
• One-way roads are correctly defined.
• Number of lanes and connections in the intersections.
• Warnings from Netconvert for a smoonth simulation for example adjust the angle of the roads.
It’s an iterative process between JOSM and NetEdit to go through all the steps men-tationed above. Now after that the road map is good enough for SUMO to run the simulation without any significant errors that can affect the traffic flows.
Figure 3.5: Kista Scenario Road Network
Table 3.1: Topology Information
Total number of nodes 1190 Total number of edges 1123 Total edge length [km] 103.11 Total lane length [km] 122.99
The geometrical objects have been kept tract in the previous steps now we need to implement them into our map as colored polygons in order for us to use and easier to see through SUMO-GUI.
A vaild polygon in SUMO is described like this:
< p o l y id =" < P O L Y G O N _ I D >" \\ t y p e =" < T Y P E N A M E >" \\ c o l o r =" < COLOR >" \\ f i l l =" < F I L L _ O P T I O N >" \\ l a y e r =" < L A Y E R _ N O >" \\ s h a p e =" <2 D - P O S I T I O N >[ <2 D - P O S I T I O N >]*"/ >
We use POLYCONVERT to convert the buildings and parking-lots from OSM file to SUMO polygon file(.poly.xml). The polygons are stored as an upper layer compared to the network file.
Table 3.2: Number of Polygons in the Kista Scenario
Buildings 242 Parking 22 Total 264
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Figure 3.7: Kista Fading Map
Figure 3.2 summariz the general process to generate a SUMO map file.
Figure 3.8: Overview of steps to generate SUMO map file[4]
3.1.2 Mobility
Now we have built the network and could load it into SUMO but there will be no vehicle running. In order to have a realistic microscopic traffic scenario the ACTIVITYGEN tool[39] is used. It generate the traffic demand based on a population description. It takes into account activities such as work, school and free-time. Transportation tool can be feet, bus and cars. To be noticed that there are some traffic not covered by Activitygen such as tourist from outside so we add 20% random traffic following uniform distribution.
Figure 3.9: Bus Stop Location in Kista Scenario
Figure 3.10: Example Bus Route No.178 from OSM
Table 3.3: Summary of Bus in the Kista Scenario
Total Number of Bus Lines 11 Total Number of Bus Stops 38 Total Number of Buses 594
ACTIVITYGEN will generate staring and destination edges then we use duaRouter[41] to complete all the edges in between. Moreover , we use a dynamic routing mechanism for the vehicles to avoid road congestion. That means a vehicle will re-route using Dijkstra algorithm every 5 minutes. When SUMO simulation is over we could see from the summary that the dynamic routing mechanism can provide a smooth traffic flow.
3.2
Integration
Initials 19
is first let SUMO produce a complete trace file and then let network simulator read the trace file however not possible to control the traffic flow, we call this type of simulation off-line simulation. The other type is real-time/on-line simulation which allows interac-tion between SUMO and network simulator during the simulainterac-tion so the most accurate and efficient result could be given.
3.2.1 Real-time Simulation
In our testbed SUMO provides vehicle mobility model including nodes positions, speed and routes as well as geometry road map. NS3 provides LTE cellular network model and signal fading model during propagation. V2X simulation need to be realistic in microscopic level(provide accurate information) and run in real-time to be useful(for example during simulation re-routing if traffic congestion occurs or signal is weak). If we had a feed-back loop in a live simulation we refer that as the real-time simulation.
The interface in SUMO for external communication TraCI(Traffic Control Interface) is included in SUMO package. It gives access and pass the data between SUMO and external applications. We can retrieve the current values of all objects in the simulation such as vehicles or traffic light, also we can change the state of these objects on-line. One example user case could be when we want to simulate a bus broke-down half way, we could change the bus destination back to the bus depot during the simulation. TraCI uses TCP connection with SUMO where SUMO acts as the server and listing to the remote port for the coming connections. The figure below describes a basic flow of establishing a connection to SUMO.
When the client connects to SUMO by a TCP connection through the configured port it takes control of SUMO. The client needs to send commands to SUMO in every simu-lation step and the objects in SUMO can be changed according to the instruction sent from client. SUMO will also send corresponding acknowledge message as well as some additional result back to the client.
Figure 3.12: Closing a connection to SUMO[5]
The connection can only be shut down by the client by sending a close command.
Initials 21
and synchronizes them. Also several visualization and analysis tools are prepared for VSimRTI.
Figure 3.13: VSimRTI architecture[6]
Yet another middle-ware framework project available is Online Vehicular Network Inte-grated Simulation(OVNIS)[44] platform. It can couple together SUMO and NS3 so the SUMO scenario is embedded in the mobility model of NS3. Accordingly corresponding NS3 module could influence the traffic simulation such as reroute of the vehicles in the mobility model. OVANIS achieved this by using TraCI interfce to create a sub-module in NS3.
However when we try to implement the integration, we found that both OVNIS and VSimRTI have only implemented 802.11p protocol stack not support LTE and they do not consider fading pattern in the urban area neither for the simulation which violated our research problem.
3.2.2 Off-line Simulation
Traffic simulators and network simulators are designed separately so they should be controlled separately and can not be used directly together. Most network simulators are able to generate random movements themselves but now it is possible to load mobility scenario from traffic simulators so the vehicular network evaluation can be more accurate.
Figure 3.15: Illustration of off-time simulation work flow between SUMO and NS2[2]
Chapter 4
Evaluation
For evaluation of the integrated scenario, we focus on three aspects: Feasibility, Capacity and Performance. When doing feasibility test, we calculate the delay of data flows with different priority in one fixed route. In order to find the maximum number of busses our scenario can handle we did capacity test. We sum up the simulation results from SUMO and analyze. The performance of LTE network can be found in Y.Jin’s publication[12].
4.1
Simulation Summary of SUMO
A moving vehicle will be sent to teleport if some issues such as congestion occur that stop the vehicle continue to the destination. In the end of simulation we could trace the simulation detail though log files. Higher percentage of teleports indicate that the traffic flow can not be moving smoothly. In our scenario it’s only around 3% so the unexpected issues in the traffic scenario will not have significant influence to the network performance. We achieve this by a dynamic re-routing mechanism. If a traffic congestion occurs the vehicles will be automatically re-routed to avoid the congestion situation. This is not always true in reality so we only configured 70% vehicles with re-routing set-up and the rest 30% will stick to their routes no matter the traffic condition. We assume this will not affect the realism of the scenario.
Table 4.1: SUMO Simulation Summary
Total simlation time [s] 86494 Number of vehicles loaded 25038
Teleports 921
To verify that the Kista Scenario can behave in a realistic way we use an external tool: Typical Traffic from Google Maps[47].
Figure 4.1: Traffic Condition in Google Maps at 8:00 am
The green and red color represent the speed of the vehicle driving on that road from normal speed to low speed. We compare the running simulation and the snapshots from the typical traffic and the traffic condition is consistent so we can assume that the reality of Kista scenario can be guaranteed.
4.2
Feasibility
The network requirements we have for teloperation are constant latency less than 50ms, constant jitter, more than 99% connectivity guarantee with throughput reached GBR.
Initials 25
We set priorities to different data flow types. Traffic Command flow has higher priority than video flow so the traffic command flow(start from 10s) will affect the video trans-mission. We could observe from figure 4.1 the video flow delay have more fluctuating which is due to command packet, as well as channel fading and codes performance. But it still can guarantee a delay less than 50ms and relatively small jitter. We send some other dummy packets with lowest priority (start from 0.01s) and it has no affect for the transmission of video flow. CQI also can cause fluctuation in delay as the time passing by.
Figure 4.3: Packet Loss Ratio for Critical stream in 10M and 1M Background Traffic
It is important that traffic command should be always received by the vehicles in tel-operation. So we need to make sure the command flow will never lose connectivity. Observing figure 4.2 we could No matter how we change the background traffic through-put, the video flow which have higher priority is not affected, only dummy background traffic lost more packet itself. This is guaranteed by assigning GBR-bearer to the flow with higher priority and NGBR-bearer to the flow with lower priority. NGBR-bearer will only be served until GBR-bearer is fully served.
From perspective of the mobility, we have transmitting antenna located at (880,4100) and moving vehicle has the route from (188,35,4454,56) to (968.36, 4094,65). The vehicle will be closer to the antenna when approaching destination, which will result in more guaranteed connectivity, optimized CQI and decreased PLR.
Figure 4.5: Packet Loss Ratio for non-Critical stream in 10M and 1M Back-ground Traffic
Since this is the feasibility test so we only simulated one bus route.
4.3
Capacity
The capacity of the scenario, which is maximum number of busses that the testbed can handle without violate network requirements is another parameter we need to investi-gate. We have one eNB in the network scenario and test different number of busses in traffic scenario.
Figure 4.6: Capacity for Bus Router per eNB
Chapter 5
Conclusion and Future Work
As we stated the research problems in the beginning of the thesis: simulate and evaluate the teleoperation of the vehicles using LTE cellular network. We set several objectives to achieve that in a proper scientific research manner: The first is to do a state-of-art study and choose the appropriate simulators. To achieve that we conduct a comparison based on the study of exist research results and choose SUMO to build our traffic scenario. The second object is to build the traffic scenario for Kista. The scenario we build has following characters: It is built on real geographical topoogy and included different areas such as residential and work place, city road and high way. It supports different mobility modellings (both stochastic and deterministic). The whole scenario simulates 24 hours traffic movements. It is compatible with other traffic simulator for integration. We emphasize the importance of the mobility modeling in wireless networking. Not only the movements in geographical map is considered but also we take case as well the importance of simulation accuracy by generating the movements under daily activities distribution based on real statistic data. The presented scenario is repeatable that can be easily applied to other user case for example in another city urban area, or to evaluate with other wireless network protocol like IEEE 802.11p. Next object is to integrate the traffic scenario together with network scenario build by my co-worker Yifei JIn. This is the weakness of the project. We didn’t succeed in implementing the real-time simulation due to the only suitable integration framework VSimRTI is not open-source and it has not implemented complete LTE protocol stack so we can not configure and extend that for our integrated scenario. To implement a new integration frame work would be beyond the scope of this thesis project so we are forced to simulate the integrated scenario off-line. However we make use of the emulation features from NS3 to extend the network topology to remote host and elevate this to have user access network topology in remote end. A good compensation we could do is to run the simulation many times in small time scale so we have results very similar to real-time simulation behavior.
So far we have discussed the process of building a V2X simulation testbed framework which is integrated by an accurate LTE network scenario and a realistic microscopic traffic scenario. The simulation results show that it’s feasible by using LTE network infrastructure to teleoperate up tp 5 vehicles per eNB with guaranteed QoS in Kista. However, as mentioned above we have many assumptions, uncertainties and even weak-nesses.
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