28,455 research outputs found

    Mobility Models for Vehicular Communications

    Full text link
    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.; MartĂ­nez DomĂ­nguez, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano EscribĂĄ, 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 reprĂ€sentativen Zeitbudgeterhebung. In: Blanke K, Ehling M, Schwarz N (eds) Schriftenreihe des Bundesministeriums fĂŒr 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, HĂ€rri 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.2009.090403HĂ€rri 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 EinflĂŒssen in einer mikroskopischen Verkehrsflusssimulation. In: JĂŒrgensohn T, Kolrep H (eds) Fahrermodellierung in Wissenschaft und Wirtschaft. VDI-Verlag, DĂŒsseldorf, 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, UniversitĂ€t 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, HĂ€rri 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):31–33RindsfĂŒser G, Ansorge J, MĂŒhlhans H (2002) AktivitĂ€tenvorhaben. In: Beckmann K (ed) SimVV MobilitĂ€t 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

    Performance evaluation of an efficient counter-based scheme for mobile ad hoc networks based on realistic mobility model

    Get PDF
    Flooding is the simplest and commonly used mechanism for broadcasting in mobile ad hoc networks (MANETs). Despite its simplicity, it can result in high redundant retransmission, contention and collision in the network, a phenomenon referred to as broadcast storm problem. Several probabilistic broadcast schemes have been proposed to mitigate this problem inherent with flooding. Recently, we have proposed a hybrid-based scheme as one of the probabilistic scheme, which combines the advantages of pure probabilistic and counter-based schemes to yield a significant performance improvement. Despite these considerable numbers of proposed broadcast schemes, majority of these schemes’ performance evaluation was based on random waypoint model. In this paper, we evaluate the performance of our broadcast scheme using a community based mobility model which is based on social network theory and compare it against widely used random waypoint mobility model. Simulation results have shown that using unrealistic movement pattern does not truly reflect on the actual performance of the scheme in terms of saved-rebroadcast, reachability and end to end delay

    Robotic Wireless Sensor Networks

    Full text link
    In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

    Towards Opportunistic Data Dissemination in Mobile Phone Sensor Networks

    Get PDF
    Recently, there has been a growing interest within the research community in developing opportunistic routing protocols. Many schemes have been proposed; however, they differ greatly in assumptions and in type of network for which they are evaluated. As a result, researchers have an ambiguous understanding of how these schemes compare against each other in their specific applications. To investigate the performance of existing opportunistic routing algorithms in realistic scenarios, we propose a heterogeneous architecture including fixed infrastructure, mobile infrastructure, and mobile nodes. The proposed architecture focuses on how to utilize the available, low cost short-range radios of mobile phones for data gathering and dissemination. We also propose a new realistic mobility model and metrics. Existing opportunistic routing protocols are simulated and evaluated with the proposed heterogeneous architecture, mobility models, and transmission interfaces. Results show that some protocols suffer long time-to-live (TTL), while others suffer short TTL. We show that heterogeneous sensor network architectures need heterogeneous routing algorithms, such as a combination of Epidemic and Spray and Wait
    • 

    corecore