Centrality Analysis in Vehicular Networks

To better understand networking and security aspects of VANETs, we have been investigating network connectivity issues and mappings of car networks to the underlying road topology. Using this mapping and various metrics based on centrality, we locate hot-spots in vehicular networks to determine the most vulnerable points for jamming. We also use these to optimize the placement of roadside units

Mobility Models for Vehicular Communications

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-15497-8_11The experimental evaluation of vehicular ad hoc networks (VANETs) implies elevate economic cost and organizational complexity, especially in presence of solutions that target large-scale deployments. As performance evaluation is however mandatory prior to the actual implementation of VANETs, simulation has established as the de-facto standard for the analysis of dedicated network protocols and architectures. The vehicular environment makes network simulation particularly challenging, as it requires the faithful modelling not only of the network stack, but also of all phenomena linked to road traffic dynamics and radio-frequency signal propagation in highly mobile environments. In this chapter, we will focus on the first aspect, and discuss the representation of mobility in VANET simulations. Specifically, we will present the requirements of a dependable simulation, and introduce models of the road infrastructure, of the driver’s behaviour, and of the traffic dynamics. We will also outline the evolution of simulation tools implementing such models, and provide a hands-on example of reliable vehicular mobility modelling for VANET simulation.Manzoni, P.; Fiore, M.; Uppoor, S.; Martínez Domínguez, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC. (2015). Mobility Models for Vehicular Communications. En Vehicular ad hoc Networks. Standards, Solutions, and Research. Springer. 309-333. doi:10.1007/978-3-319-15497-8_11S309333Bai F, Sadagopan N, Helmy A (2003) The IMPORTANT framework for analyzing the impact of mobility on performance of routing protocols for adhoc networks. Elsevier Ad Hoc Netw1:383–403Baumann R, Legendre F, Sommer P (2008) Generic mobility simulation framework (GMSF). In: ACM mobility modelsBononi L, Di Felice M, D’Angelo G, Bracuto M, Donatiello L (2008) MoVES: A framework for parallel and distributed simulation of wireless vehicular ad hoc networks. Comput Netw 52(1):155–179Cabspotting Project (2006) San Francisco exploratorium’s invisible dynamics initiative. http://cabspotting.org/index.htmlCamp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mobile Comput 2(5):483–502. Special issue on Mobile Ad Hoc Networking: Research, Trends and ApplicationsCavin D, Sasson Y, Schiper A (2002) On the accuracy of MANET simulators. In: Proceedings of the second ACM international workshop on principles of mobile computing. ACM, New York, pp 38–43Choffnes D, Bustamante F (2005) An integrated mobility and traffic model for vehicular wireless networks. In: ACM VANETDavies V (2000) Evaluating mobility models within an ad hoc network. Master’s thesis, Colorado School of Mines, Boulder, Etats-UnisEhling M, Bihler W (1996) Zeit im Blickfeld. Ergebnisse einer repräsentativen Zeitbudgeterhebung. In: Blanke K, Ehling M, Schwarz N (eds) Schriftenreihe des Bundesministeriums für Familie, Senioren, Frauen und Jugend, vol 121. W. Kohlhammer, Stuttgart, pp 237–274ETH Laboratory for Software Technology (2009) K. Nagel. http://www.lst.inf.ethz.ch/research/ad-hoc/car-traces/Fiore M, Härri J (2008) The networking shape of vehicular mobility. In: ACM MobiHoc, Hong Kong, ChinaFiore M, Haerri J, Filali F, Bonnet C (2007) Vehicular mobility simulation for VANETS. In: Proceedings of the 40th annual simulation symposium (ANSS 2007), Norfolk, VAFleetnet Project - Internet on the Road (2000) NEC Laboratories Europe. http://www.neclab.eu/Projects/fleetnet.htmGawron C (1998) An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int J Mod Phys C 9(3):393–407Haerri J, Filali F, Bonnet C (2009) Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Commun Surv Tutorials 11(4):19–41. doi: 10.1109/SURV.2009.090403 . http://dx.doi.org/10.1109/SURV.2009.090403Härri J, Fiore M, Filali F, Bonnet C (2011) Vehicular mobility simulation with VanetMobiSim. Simulation 87(4):275–300. doi: 10.1177/0037549709345997 . http://dx.doi.org/10.1177/0037549709345997Hertkorn G, Wagner P (2004) The application of microscopic activity based travel demand modelling in large scale simulations. In: World conference on transport researchHuang E, Hu W, Crowcroft J, Wassell I (2005) Towards commercial mobile ad hoc network applications: a radio dispatch system. In: Sixth ACM international symposium on mobile ad hoc networking and computing (MobiHoc 2005), Urbana-Champaign, ILJaap S, Bechler M, Wolf L (2005) Evaluation of routing protocols for vehicular ad hoc networks in city traffic scenarios. In: ITSTJardosh A, Belding-Royer E, Almeroth K, Suri S (2003) Towards realistic mobility models for mobile ad hoc networks. In: ACM/IEEE international conference on mobile computing and networking (MobiCom 2003), San Diego, CAKim J, Sridhara V, Bohacek S (2009) Realistic mobility simulation of urban mesh networks. Ad Hoc Netw 7(2):411–430Krajzewicz D (2009) Kombination von taktischen und strategischen Einflüssen in einer mikroskopischen Verkehrsflusssimulation. In: Jürgensohn T, Kolrep H (eds) Fahrermodellierung in Wissenschaft und Wirtschaft. VDI-Verlag, Düsseldorf, pp 104–115Krajzewicz D, Blokpoel RJ, Cartolano F, Cataldi P, Gonzalez A, Lazaro O, Leguay J, Lin L, Maneros J, Rondinone M (2010) iTETRIS - a system for the evaluation of cooperative traffic management solutions. In: Advanced microsystems for automotive applications 2010, VDI-Buch. Springer, Berlin, pp 399–410Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of SUMO—simulation of urban mobility. Int J Adv Syst Measur 5(3/4):128–138Krauss S (1998) Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Ph.D. thesis, Universität zu KölnKrauss S, Wagner P, Gawron C (1997) Metastable states in a microscopic model of traffic flow. Phys Rev E 55(304):55–97Legendre F, Borrel V, Dias de Amorim M, Fdida S (2006) Reconsidering microscopic mobility modeling for self-organizing networks. Network IEEE 20(6):4–12. doi: 10.1109/MNET.2006.273114Mangharam R, Weller D, Rajkumar R, Mudalige P (2006) GrooveNet: a hybrid simulator for vehicle-to-vehicle networks. In: IEEE MobiquitousMartinez FJ, Cano JC, Calafate CT, Manzoni P (2008) Citymob: a mobility model pattern generator for VANETs. In: IEEE vehicular networks and applications workshop (Vehi-Mobi, held with ICC), BeijingMiller J, Horowitz E (2007) FreeSim: a free real-time freeway traffic simulator. In: IEEE ITSCNagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I 2(12):2221–2229Nagel K, Wolf D, Wagner P, Simon P (1998) Two-lane traffic rules for cellular automata: a systematic approach. Phys Rev E 58:1425–1437NOW - Network on Wheels Project (2008) Hartenstein H, Härri J, Torrent-Moreno M. https://dsn.tm.kit.edu/english/projects_now-project.phpPiorkowski M, Raya M, Lugo A, Papadimitratos P, Grossglauser M, Hubaux JP (2008) TraNS: realistic joint traffic and network simulator for VANETs. ACM Mobile Comput Commun Rev 12(1):31–33Rindsfüser G, Ansorge J, Mühlhans H (2002) Aktivitätenvorhaben. In: Beckmann K (ed) SimVV Mobilität verstehen und lenken—zu einer integrierten quantitativen Gesamtsicht und Mikrosimulation von Verkehr, Ministry of School, Science and Research of Nordrhein-WestfalenSaha A, Johnson D (2004) Modeling mobility for vehicular ad hoc networks. In: ACM VANETSeskar I, Maric S, Holtzman J, Wasserman J (1992) Rate of location area updates in cellular systems. In: IEEE 42nd vehicular technology conference, 1992, vol 2, pp 694–697. doi: 10.1109/VETEC.1992.245478Sommer C, German R, Dressler F (2011) Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Trans Mobile Comput 10(1):3–15Tian J, Haehner J, Becker C, Stepanov I, Rothermel K (2002) Graph-based mobility model for mobile ad hoc network simulation. In: SCS ANSS, San DiegoTreiber M, Helbing D (2002) Realistische mikrosimulation von strassenverkehr mit einem einfachen modell. In: ASIM, Rostock, AllemagneTreiber M, Hennecke A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulations. Phys Rev E 62(2):1805–1824UDel Models for Simulation of Urban Mobile Wireless Networks (2009) Stephan Bohacek. http://www.udelmodels.eecis.udel.eduUMass DieselNet Project (2009) UMass diverse outdoor mobile environment (DOME). https://dome.cs.umass.edu/umassdieselnetUppoor S, Trullols-Cruces O, Fiore M, Barcelo-Ordinas JM (2015) Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Trans Mobile Comput 1:1. PrePrints. doi: 10.1109/TMC.2013.27Varschen C, Wagner P (2006) Mikroskopische Modellierung der Personenverkehrsnachfrage auf Basis von Zeitverwendungstagebuchern. Stadt Region Land 81:63–69Yoon J, Liu M, Noble B (2003) Random waypoint considered harmful. In: Proceedings of IEEE INFOCOMM 2003, San Francisco, CAZheng Q, Hong X, Liu J (2006) An agenda-based mobility model. In: 39th IEEE annual simulation symposium (ANSS-39-2006), Huntsville, A

Opportunistic networking in OMNeT

ABSTRACT We describe mechanisms for simulating opportunistic and delay-tolerant networks in the OMNeT++ discrete event simulator. The mechanisms allow for simulating open systems of wireless mobile nodes where mobility-or contact traces are used to drive the simulations. This way mobility generation is separated from the core OMNeT++ protocol simulations which facilitates importing synthetic or real data from external mobility generators, real mobility tracking data or real contact traces. The paper describes the design and implementation of our mechanisms for OMNeT++ and gives an example of how we have used these to simulate opportunistic wireless content distribution in an urban environment

Simulação de comunicações oportunistas em ambientes urbanos utilizando bluetooth

Dissertação de mestrado integrado em Engenharia de ComunicaçõesAssistimos nos dias de hoje nos meios rurais mas maioritariamente nos meios urbanos, um enorme crescimento do uso de dispositivos móveis. Pedestres ou mesmo veículos, passaram a integrar ou transportar dispositivos com sistemas de comunicações como telemóveis, tablets, computadores portáteis ou até relógios inteligentes que trazem a possibilidade de comunicarem entre si. Dessa forma torna-se importante que existam aplicações capazes de simular o movimento e a interação entre estes dispositivos. Já existem atualmente diversos simuladores de mobilidade e comunicações que procuram simular ambientes urbanos, contudo não existem ainda aplicações capazes de fazer simulações em grande escala ou simular específicos protocolos de comunicação. Foi com o intuito de responder a estas lacunas, que foi pensado e nasceu este simulador. É assim objetivo que este para além de fazer simulações de mobilidade para diferentes tipos de atores, consiga também fazer a simulação da comunicação entre estes. Esta dissertação descreve o desenvolvimento de um simulador de comunicações em ambientes urbanos que permita assim simular as comunicações entre os diversos atores intervenientes numa simulação. É pretendido que estas consigam trocar informação entre si, usando para isso mecanismos de simulação tendo em conta pormenores da vida real, tal como o alcance rádio da tecnologia usada, ou a possibilidade de envio de mensagens Broadcast. Foi então simulada a tecnologia de comunicação Bluetooth, e efetuados testes de desempenho e funcionalidade que vieram assim concluir que os objetivos pretendidos desta dissertação foram alcançados.Nowadays in the rural but mostly in the urban areas, the use of mobile devices it’s growing hugely. Pedestrians or vehicles have been incorporating and carrying devices with communication systems such as mobile phones, tablets, laptops or even smart watches that provide the ability to communicate between each others. Therefore it’s important that exists applications capable to simulate the movement and the communications between these devices. There are some mobility and communications simulators that simulate urban environments, however the applications capable of doing large-scale simulations or specific communication protocol simulations are still absent. This simulator was born in order to fill these gaps. It’s aim of this project that beyond capability of mobility simulations for different types of actors, this simulator became also capable to simulate the communications between them. This master thesis then describes the development of urban environment communications simulator, capable of simulate the communications between the different actors in a simulation. It’s intended so, that the actors be able to exchange information between each other, using for that simulation mechanisms taking into account details from real life, as the radio range from the used technology, or the ability to send broadcast messages. Was then simulated the Bluetooth communication technology and performed performance and functionality tests, which thus conclude that the intended goals from this dissertation were achieved

Fuzzy logic traffic signal controller enhancement based on aggressive driver behavior classification

The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control

A large-scale vehicular mobility dataset of the Cologne urban area

National audienceVehicles are envisioned to become real communication hubs in the near future, thanks to the growing presence of radio interfaces on the cars as well as to the increasing utilization of smartphones and tablets by their passengers. The single most distinguishing feature of vehicular networks lies in the mobility of users, which is the result of the interaction of complex macroscopic and microscopic dynamics. Notwithstanding the improvements that vehicular mobility modeling has undergone during the past few years, no car traffic trace is available today that captures both macroscopic and microscopic behaviors of drivers over a large urban region, and does so with the level of detail required for networking research. In this paper, we present a realistic synthetic dataset of the car traffic over a typical 24 hours in a 400 sq km region around the city of Cologne in Germany. We outline how our mobility description improves today's existing traces and show the potential impact that a comprehensive representation of vehicular mobility can have one the evaluation of networking technologies

Generation and analysis of realistic mobility models for mobile ad hoc networks.

Simulation modeling is an integral part of conducting research in communication networks and distributed systems. In systems involving mobile nodes, accurate modeling of mobility has primary importance. Mobility has a fundamental influence on the behavior and performance of the system. However, only few mobility models have been used in nearly all simulations in the past. These models are simple and highly random. As a result, the simulation studies based on these random mobility models have been heavily criticized for their credibility. We feel that availability of a software tool with the following capability, at least in part, would alleviate this crisis. The software must facilitate researchers to: (i) model a wide range of mobility with varying degrees of realism (ii) analyze the modeled mobility visually and statistically and (iii) transport the mobility trace in a format that can be used in most widely used simulators. The development of a software tool with the above mentioned capabilities is the main contribution of this thesis. In this thesis, after presenting a comprehensive survey on realistic mobility models, we present a realistic mobility generator software called RLMobiGen that can be used to specify, generate, analyze, and then export the mobility trace. The mobility trace can then be used in the simulation studies of mobile ad hoc networks. RLMobiGen is a comprehensive, highly interactive, and user friendly software. --P.iii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b168630

Information Sharing in Vehicular AdHoc Network

Relevance Technique broadcast the useful information and removes the redundant data. 802.11e protocol implementation has certain flaws and is not suitable for VANETs scenarios. Main issue in 802.11e protocol is internal sorting of packets, no priority mechanism within the queues and often lower priority traffic get more medium than high priority traffic. In this paper, the mathematical model of relevance scheme is enhanced so that it can consider the network control in real scenario by considering the impact of malicious node in network. Problems of 802.11e protocol can be resolved by making virtual queue at application level. We analyze the comparison of simple virtual queue with the over all impact of virtual queue and mathematical model. Similarly we compare the mathematical model with over all impact of virtual queue and modified mathematical model using NS-2 simulator