Benoit Hilt is Assistant Professor of computer networking at the University of Haute-Alsace, France. His current research focuses on high mobility wireless communication, realistic simulation in the ITS domain and cooperative communication for autonomous driving.Marion Berbineau is Research Director at IFSTTAR, France in wireless communications for vehicular applications. Her research interests are mainly radio channel characterization and modeling, electromagnetic simulations, signal processing for telecommunications, performance evaluation at physical layers and GNSS applications.Alexey Vinel is Full Professor in computer communications at Halmstad University, Sweden. His research interests include wireless communications and networking, cooperative intelligent transportation systems and autonomous driving.Alain Pirovano is Professor at ENAC (French Civil Aviation University) and Head of the Communication Networks Research Group. His research activities focus on routine, reliable, and distributed systems particularly in the context of wireless ad hoc networks and aeronautical networks.

• Preface xiChapter 1 Simulation of Convergent Networks for Intelligent Transport Systems with VSimRTI 1Robert PROTZMANN, Björn SCHÜNEMANN and Ilja RADUSCH1.1 Introduction 11.2 Fundamentals of cooperative ITS 21.2.1 Message types 21.2.2 Application categories 31.2.3 Supporting facilities 41.3 Overall simulation framework 51.4 Simulation of cellular networks 61.4.1 Regions and cells 101.4.2 Delay models 111.4.3 PR-Model and PL-Model 121.4.4 Capacity Model 131.4.5 Topological and geographical messaging 141.5 Simulation study 141.5.1 Evaluation metrics 161.5.2 Simulation set-up 181.5.3 Simulation results 211.6 Conclusion 251.7 Bibliography 26Chapter 2 Near-field Wireless Communications and their Role in Next Generation Transport Infrastructures: an Overview of Modelling Techniques 29Christian PINEDO, Marina AGUADO, Lara RODRIGUEZ, Iñigo ADIN, Jaizki MENDIZABAL and Guillermo BISTUÉ2.1 Near-field wireless technologies 302.1.1 Near-field versus far-field 302.1.2 Near-field-based technologies in transport 332.2 Characterization of near-field communications 362.2.1 Electrical models 372.2.2 Analysis of the mutual inductance of a squared inductive coupling 372.2.3 Computer-aided electromagnetic calculation 402.3 Discrete event simulators 422.3.1 Riverbed Modeler 432.3.2 OMNeT++ 442.3.3 ns-2 452.3.4 ns-3 452.3.5 Discrete event simulator comparison for near-field communication 462.4 Conclusions 472.5 Bibliography 48Chapter 3 Trace Extraction for Mobility in Civil Aeronautical Communication Networks Simulation 51Fabien GARCIA and Mickaël ROYER3.1 Traffic regulations 523.1.1 General airspace 523.1.2 North Atlantic airspace 533.2 Mobility for network simulation 543.2.1 Types of mobility models for AANETs 543.2.2 Comparison of mobility model types 553.3 Example of mobility trace extraction 563.3.1 Extraction of information 573.3.2 Traces filtering 573.3.3 Enhancing traces 583.4 Toward cooperative trajectories 603.5 Bibliography 60Chapter 4 Air-Ground Data Link Communications in Air Transport 61Christophe GUERBER, Alain PIROVANO and José RADZIK4.1 Introduction 614.1.1 Context 614.1.2 OMNeT++ 634.2 Continental air-ground data link communications and VDL mode 2 634.2.1 Communication system 634.2.2 Dimensioning parameters and bottlenecks 654.2.3 Simulation model 674.2.4 Analysis of simulation results 694.3 Oceanic air-ground data link communications and AMS(R)S 714.3.1 The aeronautical mobile satellite (route) service and Classic Aero 714.3.2 Dimensioning parameters and bottlenecks 734.3.3 Simulation model 744.3.4 Analysis of simulation results 754.4 Summary and further work 764.5 Bibliography 77Chapter 5 A Virtual Laboratory as an Assessment Tool for Wireless Technologies in Railway Systems 79Patrick SONDI, Eric RAMAT and Marion BERBINEAU5.1 Introduction 805.2 ERTMS subsystems and related test beds 815.2.1 The functional subsystem of the ERTMS 815.2.2 The telecommunication subsystem of the ERTMS 845.3 A virtual laboratory based on co-simulation for ERTMS evaluation 865.3.1 Why a co-simulation approach? 865.3.2 Which data and processes must be modeled in each simulator? 875.3.3 Overall architecture of the ERTMS–OPNET virtual laboratory 895.3.4 Synchronization modes 905.3.5 Virtual laboratory implementations in the ERTMS simulator 925.3.6 Virtual laboratory implementations in OPNET 935.3.7 Virtual laboratory implementations in the co-simulation manager 955.4 Effective use of the ERTMS–OPNET virtual laboratory 975.4.1 A co-simulation scenario with the ERTMS–OPNET virtual laboratory 975.4.2 Efficiency of the co-simulation approach in the evaluation of railway systems 1015.5 Conclusion 1045.6 Bibliography 105Chapter 6 Emulating a Realistic VANET Channel in Ns-3 107Hervé BOEGLEN, Benoit HILT and Frédéric DROUHIN6.1 Introduction 1076.2 Influence of the channel propagation model on VANET simulation 1076.2.1 A realistic IEEE802.11 PHY layer 1086.2.2 Accurate VANET channel propagation modeling 1096.3 A way to realistic channel modeling with ns-2 1126.4 Realistic channel modeling with ns-3 1146.4.1 The Yans WiFi model 1146.4.2 The Physim Wi-Fi model emulating OFDM-based transmission 1156.4.3 Data transmission at ns-3 PHY level 1166.4.4 The internals of WiFi channel modeling 1176.5 Case studies: emulation of realistic VANET channel models in ns-3 1176.5.1 A simplified VANET channel model for an urban environment 1186.5.2 A normalized VANET channel model for urban environments 1216.6 Conclusion and discussion 1236.7 Appendix A: The Abbas et al Model Implementation 1256.8 Bibliography 130Chapter 7 CONVAS: Connected Vehicle Assessment System for Realistic Co-simulation of Traffic and Communications 133Justinian ROSCA, Ines UGALDE, Praprut SONGCHITRUKSA and Srinivasa SUNKARI7.1 Introduction 1337.2 Related work 1357.3 CONVAS co-simulation platform 1387.4 Realistic DSRC channel models 1397.4.1 CONVAS propagation models 1417.4.2 Model tuning based on real-world data 1427.5 Channel model tuning 1437.5.1 Michigan safety pilot model deployment data 1437.5.2 Estimation of PDR 1447.5.3 Model tuning 1467.6 Connected vehicle applications 1497.6.1 Intelligent dilemma zone avoidance 1497.6.2 IDZA implementation in CONVAS 1507.6.3 IDZA performance criteria 1517.7 Experimental results 1517.7.1 CONVAS setup 1517.7.2 Co-simulation results 1527.8 Conclusions 1597.9 Acknowledgments 1607.10 Bibliography 161Chapter 8 Highway Road Traffic Modeling for ITS Simulation 165Marco GRAMAGLIA, Marco FIORE, Maria CALDERON, Oscar TRULLOLS-CRUCES and Diala NABOULSI8.1 Introduction 1658.2 Road traffic models 1668.2.1 Traffic input feeds 1688.2.2 Mobility models 1698.3 Fine-tuned measurement-based model 1708.4 Comparative analysis of road traffic models 1748.4.1 Case study scenarios 1748.4.2 Connectivity metrics 1758.4.3 Results 1768.5 Fundamental properties of highway vehicular networks 1788.6 Discussion and conclusions 1818.7 Bibliography 182Chapter 9 F-ETX: A Metric Designed for Vehicular Networks 185Sébastien BINDEL, Benoit HILT and Serge CHAUMETTE9.1 Introduction 1859.2 Link quality estimators 1879.2.1 Hardware-based LQE 1889.2.2 Software-based 1899.2.3 Discussion 1909.3 Analysis of legacy estimation techniques 1909.3.1 Type of window 1919.3.2 Window analysis 1939.4 The F-ETX metric 1959.4.1 Window management algorithms 1959.4.2 Multi-assessment approach 1979.4.3 Routing integration framework 1999.5 Simulation settings 2019.5.1 First scenario 2029.5.2 Second scenario 2029.6 Simulation results 2029.6.1 Performance of the multi-estimators 2039.6.2 Performance of routing protocols 2069.7 Conclusion 2089.8 Bibliography 209Chapter 10 Autonomic Computing and VANETs: Simulation of a QoS-based Communication Model 211Nader MBAREK, Wahabou ABDOU and Benoît DARTIES10.1 Introduction 21110.2 Autonomic Computing within VANETs 21210.2.1 Autonomic Computing 21210.2.2 Autonomic vehicular communications 21310.3 Broadcasting protocols for VANETs 21310.3.1 Deterministic methods 21510.3.2 Stochastic methods 21610.4 Autonomic broadcasting within VANETs 21810.4.1 Optimization of broadcasting protocols in VANETs 21810.4.2 Self-management architecture 21910.4.3 QoS-based broadcasting 22110.5 Simulation of a QoS-based communication model 22210.5.1 ADM: autonomic dissemination method 22210.5.2 Simulation environment 22810.5.3 Performance evaluation 22910.6 Conclusion 23110.7 Bibliography 232List of Authors 235Index 239

Benoit Hilt is Assistant Professor of computer networking at the University of Haute-Alsace, France. His current research focuses on high mobility wireless communication, realistic simulation in the ITS domain and cooperative communication for autonomous driving.Marion Berbineau is Research Director at IFSTTAR, France in wireless communications for vehicular applications. Her research interests are mainly radio channel characterization and modeling, electromagnetic simulations, signal processing for telecommunications, performance evaluation at physical layers and GNSS applications.Alexey Vinel is Full Professor in computer communications at Halmstad University, Sweden. His research interests include wireless communications and networking, cooperative intelligent transportation systems and autonomous driving.Alain Pirovano is Professor at ENAC (French Civil Aviation University) and Head of the Communication Networks Research Group. His research activities focus on routine, reliable, and distributed systems particularly in the context of wireless ad hoc networks and aeronautical networks.

• Preface xiChapter 1 Simulation of Convergent Networks for Intelligent Transport Systems with VSimRTI 1Robert PROTZMANN, Björn SCHÜNEMANN and Ilja RADUSCH1.1 Introduction 11.2 Fundamentals of cooperative ITS 21.2.1 Message types 21.2.2 Application categories 31.2.3 Supporting facilities 41.3 Overall simulation framework 51.4 Simulation of cellular networks 61.4.1 Regions and cells 101.4.2 Delay models 111.4.3 PR-Model and PL-Model 121.4.4 Capacity Model 131.4.5 Topological and geographical messaging 141.5 Simulation study 141.5.1 Evaluation metrics 161.5.2 Simulation set-up 181.5.3 Simulation results 211.6 Conclusion 251.7 Bibliography 26Chapter 2 Near-field Wireless Communications and their Role in Next Generation Transport Infrastructures: an Overview of Modelling Techniques 29Christian PINEDO, Marina AGUADO, Lara RODRIGUEZ, Iñigo ADIN, Jaizki MENDIZABAL and Guillermo BISTUÉ2.1 Near-field wireless technologies 302.1.1 Near-field versus far-field 302.1.2 Near-field-based technologies in transport 332.2 Characterization of near-field communications 362.2.1 Electrical models 372.2.2 Analysis of the mutual inductance of a squared inductive coupling 372.2.3 Computer-aided electromagnetic calculation 402.3 Discrete event simulators 422.3.1 Riverbed Modeler 432.3.2 OMNeT++ 442.3.3 ns-2 452.3.4 ns-3 452.3.5 Discrete event simulator comparison for near-field communication 462.4 Conclusions 472.5 Bibliography 48Chapter 3 Trace Extraction for Mobility in Civil Aeronautical Communication Networks Simulation 51Fabien GARCIA and Mickaël ROYER3.1 Traffic regulations 523.1.1 General airspace 523.1.2 North Atlantic airspace 533.2 Mobility for network simulation 543.2.1 Types of mobility models for AANETs 543.2.2 Comparison of mobility model types 553.3 Example of mobility trace extraction 563.3.1 Extraction of information 573.3.2 Traces filtering 573.3.3 Enhancing traces 583.4 Toward cooperative trajectories 603.5 Bibliography 60Chapter 4 Air-Ground Data Link Communications in Air Transport 61Christophe GUERBER, Alain PIROVANO and José RADZIK4.1 Introduction 614.1.1 Context 614.1.2 OMNeT++ 634.2 Continental air-ground data link communications and VDL mode 2 634.2.1 Communication system 634.2.2 Dimensioning parameters and bottlenecks 654.2.3 Simulation model 674.2.4 Analysis of simulation results 694.3 Oceanic air-ground data link communications and AMS(R)S 714.3.1 The aeronautical mobile satellite (route) service and Classic Aero 714.3.2 Dimensioning parameters and bottlenecks 734.3.3 Simulation model 744.3.4 Analysis of simulation results 754.4 Summary and further work 764.5 Bibliography 77Chapter 5 A Virtual Laboratory as an Assessment Tool for Wireless Technologies in Railway Systems 79Patrick SONDI, Eric RAMAT and Marion BERBINEAU5.1 Introduction 805.2 ERTMS subsystems and related test beds 815.2.1 The functional subsystem of the ERTMS 815.2.2 The telecommunication subsystem of the ERTMS 845.3 A virtual laboratory based on co-simulation for ERTMS evaluation 865.3.1 Why a co-simulation approach? 865.3.2 Which data and processes must be modeled in each simulator? 875.3.3 Overall architecture of the ERTMS–OPNET virtual laboratory 895.3.4 Synchronization modes 905.3.5 Virtual laboratory implementations in the ERTMS simulator 925.3.6 Virtual laboratory implementations in OPNET 935.3.7 Virtual laboratory implementations in the co-simulation manager 955.4 Effective use of the ERTMS–OPNET virtual laboratory 975.4.1 A co-simulation scenario with the ERTMS–OPNET virtual laboratory 975.4.2 Efficiency of the co-simulation approach in the evaluation of railway systems 1015.5 Conclusion 1045.6 Bibliography 105Chapter 6 Emulating a Realistic VANET Channel in Ns-3 107Hervé BOEGLEN, Benoit HILT and Frédéric DROUHIN6.1 Introduction 1076.2 Influence of the channel propagation model on VANET simulation 1076.2.1 A realistic IEEE802.11 PHY layer 1086.2.2 Accurate VANET channel propagation modeling 1096.3 A way to realistic channel modeling with ns-2 1126.4 Realistic channel modeling with ns-3 1146.4.1 The Yans WiFi model 1146.4.2 The Physim Wi-Fi model emulating OFDM-based transmission 1156.4.3 Data transmission at ns-3 PHY level 1166.4.4 The internals of WiFi channel modeling 1176.5 Case studies: emulation of realistic VANET channel models in ns-3 1176.5.1 A simplified VANET channel model for an urban environment 1186.5.2 A normalized VANET channel model for urban environments 1216.6 Conclusion and discussion 1236.7 Appendix A: The Abbas et al Model Implementation 1256.8 Bibliography 130Chapter 7 CONVAS: Connected Vehicle Assessment System for Realistic Co-simulation of Traffic and Communications 133Justinian ROSCA, Ines UGALDE, Praprut SONGCHITRUKSA and Srinivasa SUNKARI7.1 Introduction 1337.2 Related work 1357.3 CONVAS co-simulation platform 1387.4 Realistic DSRC channel models 1397.4.1 CONVAS propagation models 1417.4.2 Model tuning based on real-world data 1427.5 Channel model tuning 1437.5.1 Michigan safety pilot model deployment data 1437.5.2 Estimation of PDR 1447.5.3 Model tuning 1467.6 Connected vehicle applications 1497.6.1 Intelligent dilemma zone avoidance 1497.6.2 IDZA implementation in CONVAS 1507.6.3 IDZA performance criteria 1517.7 Experimental results 1517.7.1 CONVAS setup 1517.7.2 Co-simulation results 1527.8 Conclusions 1597.9 Acknowledgments 1607.10 Bibliography 161Chapter 8 Highway Road Traffic Modeling for ITS Simulation 165Marco GRAMAGLIA, Marco FIORE, Maria CALDERON, Oscar TRULLOLS-CRUCES and Diala NABOULSI8.1 Introduction 1658.2 Road traffic models 1668.2.1 Traffic input feeds 1688.2.2 Mobility models 1698.3 Fine-tuned measurement-based model 1708.4 Comparative analysis of road traffic models 1748.4.1 Case study scenarios 1748.4.2 Connectivity metrics 1758.4.3 Results 1768.5 Fundamental properties of highway vehicular networks 1788.6 Discussion and conclusions 1818.7 Bibliography 182Chapter 9 F-ETX: A Metric Designed for Vehicular Networks 185Sébastien BINDEL, Benoit HILT and Serge CHAUMETTE9.1 Introduction 1859.2 Link quality estimators 1879.2.1 Hardware-based LQE 1889.2.2 Software-based 1899.2.3 Discussion 1909.3 Analysis of legacy estimation techniques 1909.3.1 Type of window 1919.3.2 Window analysis 1939.4 The F-ETX metric 1959.4.1 Window management algorithms 1959.4.2 Multi-assessment approach 1979.4.3 Routing integration framework 1999.5 Simulation settings 2019.5.1 First scenario 2029.5.2 Second scenario 2029.6 Simulation results 2029.6.1 Performance of the multi-estimators 2039.6.2 Performance of routing protocols 2069.7 Conclusion 2089.8 Bibliography 209Chapter 10 Autonomic Computing and VANETs: Simulation of a QoS-based Communication Model 211Nader MBAREK, Wahabou ABDOU and Benoît DARTIES10.1 Introduction 21110.2 Autonomic Computing within VANETs 21210.2.1 Autonomic Computing 21210.2.2 Autonomic vehicular communications 21310.3 Broadcasting protocols for VANETs 21310.3.1 Deterministic methods 21510.3.2 Stochastic methods 21610.4 Autonomic broadcasting within VANETs 21810.4.1 Optimization of broadcasting protocols in VANETs 21810.4.2 Self-management architecture 21910.4.3 QoS-based broadcasting 22110.5 Simulation of a QoS-based communication model 22210.5.1 ADM: autonomic dissemination method 22210.5.2 Simulation environment 22810.5.3 Performance evaluation 22910.6 Conclusion 23110.7 Bibliography 232List of Authors 235Index 239

Benoit Hilt is Assistant Professor of computer networking at the University of Haute-Alsace, France. His current research focuses on high mobility wireless communication, realistic simulation in the ITS domain and cooperative communication for autonomous driving.Marion Berbineau is Research Director at IFSTTAR, France in wireless communications for vehicular applications. Her research interests are mainly radio channel characterization and modeling, electromagnetic simulations, signal processing for telecommunications, performance evaluation at physical layers and GNSS applications.Alexey Vinel is Full Professor in computer communications at Halmstad University, Sweden. His research interests include wireless communications and networking, cooperative intelligent transportation systems and autonomous driving.Alain Pirovano is Professor at ENAC (French Civil Aviation University) and Head of the Communication Networks Research Group. His research activities focus on routine, reliable, and distributed systems particularly in the context of wireless ad hoc networks and aeronautical networks.

• Preface xiChapter 1 Simulation of Convergent Networks for Intelligent Transport Systems with VSimRTI 1Robert PROTZMANN, Björn SCHÜNEMANN and Ilja RADUSCH1.1 Introduction 11.2 Fundamentals of cooperative ITS 21.2.1 Message types 21.2.2 Application categories 31.2.3 Supporting facilities 41.3 Overall simulation framework 51.4 Simulation of cellular networks 61.4.1 Regions and cells 101.4.2 Delay models 111.4.3 PR-Model and PL-Model 121.4.4 Capacity Model 131.4.5 Topological and geographical messaging 141.5 Simulation study 141.5.1 Evaluation metrics 161.5.2 Simulation set-up 181.5.3 Simulation results 211.6 Conclusion 251.7 Bibliography 26Chapter 2 Near-field Wireless Communications and their Role in Next Generation Transport Infrastructures: an Overview of Modelling Techniques 29Christian PINEDO, Marina AGUADO, Lara RODRIGUEZ, Iñigo ADIN, Jaizki MENDIZABAL and Guillermo BISTUÉ2.1 Near-field wireless technologies 302.1.1 Near-field versus far-field 302.1.2 Near-field-based technologies in transport 332.2 Characterization of near-field communications 362.2.1 Electrical models 372.2.2 Analysis of the mutual inductance of a squared inductive coupling 372.2.3 Computer-aided electromagnetic calculation 402.3 Discrete event simulators 422.3.1 Riverbed Modeler 432.3.2 OMNeT++ 442.3.3 ns-2 452.3.4 ns-3 452.3.5 Discrete event simulator comparison for near-field communication 462.4 Conclusions 472.5 Bibliography 48Chapter 3 Trace Extraction for Mobility in Civil Aeronautical Communication Networks Simulation 51Fabien GARCIA and Mickaël ROYER3.1 Traffic regulations 523.1.1 General airspace 523.1.2 North Atlantic airspace 533.2 Mobility for network simulation 543.2.1 Types of mobility models for AANETs 543.2.2 Comparison of mobility model types 553.3 Example of mobility trace extraction 563.3.1 Extraction of information 573.3.2 Traces filtering 573.3.3 Enhancing traces 583.4 Toward cooperative trajectories 603.5 Bibliography 60Chapter 4 Air-Ground Data Link Communications in Air Transport 61Christophe GUERBER, Alain PIROVANO and José RADZIK4.1 Introduction 614.1.1 Context 614.1.2 OMNeT++ 634.2 Continental air-ground data link communications and VDL mode 2 634.2.1 Communication system 634.2.2 Dimensioning parameters and bottlenecks 654.2.3 Simulation model 674.2.4 Analysis of simulation results 694.3 Oceanic air-ground data link communications and AMS(R)S 714.3.1 The aeronautical mobile satellite (route) service and Classic Aero 714.3.2 Dimensioning parameters and bottlenecks 734.3.3 Simulation model 744.3.4 Analysis of simulation results 754.4 Summary and further work 764.5 Bibliography 77Chapter 5 A Virtual Laboratory as an Assessment Tool for Wireless Technologies in Railway Systems 79Patrick SONDI, Eric RAMAT and Marion BERBINEAU5.1 Introduction 805.2 ERTMS subsystems and related test beds 815.2.1 The functional subsystem of the ERTMS 815.2.2 The telecommunication subsystem of the ERTMS 845.3 A virtual laboratory based on co-simulation for ERTMS evaluation 865.3.1 Why a co-simulation approach? 865.3.2 Which data and processes must be modeled in each simulator? 875.3.3 Overall architecture of the ERTMS–OPNET virtual laboratory 895.3.4 Synchronization modes 905.3.5 Virtual laboratory implementations in the ERTMS simulator 925.3.6 Virtual laboratory implementations in OPNET 935.3.7 Virtual laboratory implementations in the co-simulation manager 955.4 Effective use of the ERTMS–OPNET virtual laboratory 975.4.1 A co-simulation scenario with the ERTMS–OPNET virtual laboratory 975.4.2 Efficiency of the co-simulation approach in the evaluation of railway systems 1015.5 Conclusion 1045.6 Bibliography 105Chapter 6 Emulating a Realistic VANET Channel in Ns-3 107Hervé BOEGLEN, Benoit HILT and Frédéric DROUHIN6.1 Introduction 1076.2 Influence of the channel propagation model on VANET simulation 1076.2.1 A realistic IEEE802.11 PHY layer 1086.2.2 Accurate VANET channel propagation modeling 1096.3 A way to realistic channel modeling with ns-2 1126.4 Realistic channel modeling with ns-3 1146.4.1 The Yans WiFi model 1146.4.2 The Physim Wi-Fi model emulating OFDM-based transmission 1156.4.3 Data transmission at ns-3 PHY level 1166.4.4 The internals of WiFi channel modeling 1176.5 Case studies: emulation of realistic VANET channel models in ns-3 1176.5.1 A simplified VANET channel model for an urban environment 1186.5.2 A normalized VANET channel model for urban environments 1216.6 Conclusion and discussion 1236.7 Appendix A: The Abbas et al Model Implementation 1256.8 Bibliography 130Chapter 7 CONVAS: Connected Vehicle Assessment System for Realistic Co-simulation of Traffic and Communications 133Justinian ROSCA, Ines UGALDE, Praprut SONGCHITRUKSA and Srinivasa SUNKARI7.1 Introduction 1337.2 Related work 1357.3 CONVAS co-simulation platform 1387.4 Realistic DSRC channel models 1397.4.1 CONVAS propagation models 1417.4.2 Model tuning based on real-world data 1427.5 Channel model tuning 1437.5.1 Michigan safety pilot model deployment data 1437.5.2 Estimation of PDR 1447.5.3 Model tuning 1467.6 Connected vehicle applications 1497.6.1 Intelligent dilemma zone avoidance 1497.6.2 IDZA implementation in CONVAS 1507.6.3 IDZA performance criteria 1517.7 Experimental results 1517.7.1 CONVAS setup 1517.7.2 Co-simulation results 1527.8 Conclusions 1597.9 Acknowledgments 1607.10 Bibliography 161Chapter 8 Highway Road Traffic Modeling for ITS Simulation 165Marco GRAMAGLIA, Marco FIORE, Maria CALDERON, Oscar TRULLOLS-CRUCES and Diala NABOULSI8.1 Introduction 1658.2 Road traffic models 1668.2.1 Traffic input feeds 1688.2.2 Mobility models 1698.3 Fine-tuned measurement-based model 1708.4 Comparative analysis of road traffic models 1748.4.1 Case study scenarios 1748.4.2 Connectivity metrics 1758.4.3 Results 1768.5 Fundamental properties of highway vehicular networks 1788.6 Discussion and conclusions 1818.7 Bibliography 182Chapter 9 F-ETX: A Metric Designed for Vehicular Networks 185Sébastien BINDEL, Benoit HILT and Serge CHAUMETTE9.1 Introduction 1859.2 Link quality estimators 1879.2.1 Hardware-based LQE 1889.2.2 Software-based 1899.2.3 Discussion 1909.3 Analysis of legacy estimation techniques 1909.3.1 Type of window 1919.3.2 Window analysis 1939.4 The F-ETX metric 1959.4.1 Window management algorithms 1959.4.2 Multi-assessment approach 1979.4.3 Routing integration framework 1999.5 Simulation settings 2019.5.1 First scenario 2029.5.2 Second scenario 2029.6 Simulation results 2029.6.1 Performance of the multi-estimators 2039.6.2 Performance of routing protocols 2069.7 Conclusion 2089.8 Bibliography 209Chapter 10 Autonomic Computing and VANETs: Simulation of a QoS-based Communication Model 211Nader MBAREK, Wahabou ABDOU and Benoît DARTIES10.1 Introduction 21110.2 Autonomic Computing within VANETs 21210.2.1 Autonomic Computing 21210.2.2 Autonomic vehicular communications 21310.3 Broadcasting protocols for VANETs 21310.3.1 Deterministic methods 21510.3.2 Stochastic methods 21610.4 Autonomic broadcasting within VANETs 21810.4.1 Optimization of broadcasting protocols in VANETs 21810.4.2 Self-management architecture 21910.4.3 QoS-based broadcasting 22110.5 Simulation of a QoS-based communication model 22210.5.1 ADM: autonomic dissemination method 22210.5.2 Simulation environment 22810.5.3 Performance evaluation 22910.6 Conclusion 23110.7 Bibliography 232List of Authors 235Index 239