Realistic urban traffic simulation as vehicular Ad-hoc network (VANET) via Veins framework

These days wireless communication has impacted our daily lives. The developments achieved in this field have made our lives amazingly simpler, easier, convenient and comfortable. One of these developments has occurred in Car to Car Communication (C2CC). Communication between cars often referred to vehicular ad hoc networks (VANET) has many advantages such as: reducing cars accidents, minimizing the traffic jam, reducing fuel consumption and emissions and etc. For a closer look on C2CC studies the necessity of simulation is obvious. Network simulators can simulate the Ad-hoc network but they cannot simulate the huge traffic of cities. In order to solve this problem, in this paper we study the Veins framework which is used to run a Traffic (SUMO) and a Network (OMNET++) simulator in parallel and we simulate the realistic traffics of the city of Cologne, Germany, as an ad-hoc network

VANET Traffic Congestion Detection and Avoidance

The main objectives behind the development of congestion detection algorithms are to detect areas of high traffic density with low speeds. Each vehicle captures and disseminates information such as location and speed and process the information received from other vehicles in the network,   which can be possible through VANET. Vehicular Ad-hoc   Networks are self-organizing networks established among   vehicles equipped with communication facilities Due to recent advancements in vehicular technologies vehicular communication has emerged. Multiple approaches have been proposed to implement congestion detection in VANET. Traffic congestion is a very serious problem in big cities. With the number of vehicles increasing rapidly, especially in cities whose economy is booming, the situation is getting even worse. In this paper we are presenting Detection of Traffic Congestion using proposed approach and analysis of result

Implementation of CAVENET and its usage for performance evaluation of AODV, OLSR and DYMO protocols in vehicular networks

Vehicle Ad-hoc Network (VANET) is a kind of Mobile Ad-hoc Network (MANET) that establishes wireless connection between cars. In VANETs and MANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. In MANETs, the Random Waypoint (RW) model is used as a simulation model for generating node mobility pattern. On the other hand, in VANETs, the mobility patterns of nodes is restricted along the roads, and is affected by the movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based VEhicular NETwork). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We improved CAVENET and implemented some routing protocols. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.Peer ReviewedPostprint (published version

Improvement and performance evaluation of CAVENET: a network simulation tool for vehicular networks

Vehicle Ad-hoc Network (VANET) is a kind of Mobile Ad-hoc Network (MANET) that establishes wireless connection between cars. In VANETs and MANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. In MANETs, the Random Waypoint (RW) model is used as a simulation model for generating node mobility pattern. On the other hand, in VANETs, the mobility patterns of nodes is restricted along the roads, and is affected by the movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based Vehicular NETwork). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We improved CAVENET and implemented some routing protocols. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.Peer ReviewedPostprint (published version

Simulation Of Vehicular Movement in VANET

In the recent years research in the field of vehicular ad-hoc network(VANET) is done extensively. VANET consist of large number of dynamically nodes which are vehicles over a area. Different types of technology and applications are being developed for the VANET . So this VANET technology and applications should be thoroughly checked before deployment in the real world environment. But to test technologies and applications in real world environment is not feasible it involves lot of danger and safety issues, different reports of the testing can’t also be generated so to overcome these limitation we need to carry out simulation of VANET in the computer environment i.e. we should do a computer simulation. Computer simulation is risk and danger free, we can generate different scenario (rural, urban, collision of vehicles) of the VANET using this. So computer simulation is very important in VANET research. Simulation of VANET is divided into two part a. Traffic simulation: Generation of traffic movement, Defining the mobility model for vehicle and creating traffic movement. b. Network simulation: Generating Inter communicating vehicle , Defining communication protocols. And both the simulation are connected in bi-directional coupling

Review on the Simulation of Cooperative Caching Schemes for MANETs

In this paper, a review of the main simulation parameters utilized to evaluate the performance of cooperative caching schemes in Mobile Ad Hoc Networks is presented. Firstly, a taxonomy of twenty five caching schemes proposed in the literature about Mobile Ad Hoc Networks is defined. Those caching schemes are briefly described in order to illustrate their basis and fundamentals. The review takes into consideration the utilized network simulator, the wireless connection standard, the propagation model and routing protocol, the employed simulation area and number of data servers, the number of mobile devices and their coverage area, the mobility model, the number of documents in the network, the replacement policy and cache size, the mean time between requests, the document popularity distribution, the TTL (Time To Live) of the documents and the simulation time. Those simulation parameters have been compared among the evaluation of the studied cooperative caching schemes in order to obtain the most common utilized values. This work will allow to compare the performance of the proposed cooperative caching schemes using a common simulation environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Developing and evaluating routing protocols for rural areas that communicate via data mules

We investigate and enhance the protocols that can bring connectivity to isolated village networks via multiple data mules. These multiple mules communicate in order to find shorter reliable routes and provide higher probability of mes-sage delivery. Using movement traces for multiple data mules for rural-like areas the existing protocols ad hoc on-demand distance vector protocol (AODV) and optimal relay path (ORP) were compared. The results show that a rural route can pro-vide telecommunication between the village networks, further-more that AODV was more applicable to the network than ORP especially as the number of mules increase. Two enhance-ment algorithms (data mule inter-communicator (DMI) and ultimate data mule inter-communicator (UDMIC)) were devel-oped using the existing protocols. The first enhancement was DMI that is based on using clustering of data to improve the performance between rural networks. The second enhance-ment was UDMIC, an adaptive algorithm that examines the situation to select an algorithm to improve performance. This enhancement not only managed to use the best of each protocol but in some cases improved network performance

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

Inter-vehicular communication for collision avoidance using Wi-Fi Direct

Inter vehicular collision avoidance systems warn vehicle drivers of potential collisions. The U.S Department of Transportation (USDOT) National Highway Traffic Safety Administration, in February 2014 has decided to enable vehicular communication among lightweight vehicles to exchange warning messages to prevent accidents. Dedicated Short Range Communications (DSRC) is a communication standard that allows short-range communication between vehicles and infrastructure, exchanging critical safety information to avoid collision. DSRC safety applications include forward collision warning, sudden brake warning and blind spot warning among many other warnings. It is also important to exchange location information between vehicles and pedestrians to avoid accidents. To exchange safety messages using DSRC, dedicated equipment is required. Pedestrians may not benefit from DSRC, as they may not carry dedicated DSRC safety equipment with them. Wi-Fi Direct technology can be used as an alternate to DSRC to exchange safety messages. Wi-Fi Direct enabled smartphones can exchange important safety information without the need of additional equipment. Peer-to-Peer (P2P) connections are formed between Wi-Fi Direct devices to exchange safety information. The Group Owner acts as the access point through which all clients communicate. This work examines how Wi-Fi Direct can be used in vehicular environment to exchange basic safety information between smartphones of vehicle drivers. Wi-Fi Direct and DSRC transmission delays are calculated are calculated. The results show, with more devices in a Wi-Fi Direct group the congestion in the network increases due to unnecessary retransmissions through the group owner. As mitigation, a broadcast method is proposed to reduce the delay. The results illustrate that the P2P group can now accommodate more vehicles and the delay is lesser. The calculations are extended to compute the transmission delay when P2P groups of same size exchange safety messages. The results help analyse the limitations of the system