An evaluation of vehicular networks with real vehicular GPS traces
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An evaluation of vehicular networks with real vehicular GPS traces Yanmin Zhu1,2* , Chao Chen1 and Min Gao3 Abstract Vehicular networks have attracted increasing attention from both the academy and industry. Applications of vehicular networks require efficient data communications between vehicles, whose performance is concerned with delivery ratio, delivery delay, and routing cost. The most previous work of routing in vehicular networks assumes oversimplified node mobility when evaluating the performance of vehicular networks, e.g., random mobility or artificial movement traces, which fails to reflect the inherent complexity of real vehicular networks. To understand the achievable performance of vehicular networks under real and complex environments, we first comprehensively analyze the affecting factors that may influence the performance of vehicular networks and then introduce four representative routing algorithms of vehicular networks, i.e., Epidemic, AODV, GPSR, and MaxProp. Next, we develop an NS-2 simulation framework incorporating a large dataset of real taxi GPS traces collected from around 2,600 taxis in Shanghai, China. With this framework, we have implemented the four routing protocols. Extensive trace-driven simulations have been performed to explore the achievable performance of real vehicular networks. The impact of the controllable affecting factors is investigated, such as number of nodes, traffic load, packet TTL, transmission range, and propagation model. Simulation results show that a real vehicular network has surprisingly poor data delivery performance under a wide range of network configurations for all the routing protocols. This strongly suggests that the challenging characteristics of vehicular networks, such as unique node mobility, constraints of road topology, need further exploration. 1 Introduction Vehicular networks have attracted increasing attention from both the academy and industry because of their potential in fostering a wide spectrum of existing applications, such as driving safety, intelligent transport services [1], mobile Internet access, and file sharing [2-4]. Vehicular networks exhibit some similar characteristics in mobile ad hoc networks (MANETs) and delay tolerate networks (DTNs), depending on the density of vehicles. MANETs and DTNs share the advantage of requiring no support of a fixed infrastructure. When the vehicle density is higher, vehicular networks show stronger similarity with MANETs because of better network connectivity. On the contrary, when the density is lower, vehicular networks show more similarity *Correspondence: [email protected] 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 Shanghai Key Lab of Scalable Computing and Systems, Shanghai 200240, China Full list of author information is available at the end of the article
with DTNs, where the network is subject to more frequent disruption. However, compared with MANETs and DTNs, vehicular networks are more complex
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