An evaluation of vehicular networks with real vehicular GPS traces
EURASIP Journal on Wireless Communications and Networking
An evaluation of vehicular networks with real vehicular GPS traces
Yanmin Zhu 0 1
Chao Chen 1
Min Gao
0 Shanghai Key Lab of Scalable Computing and Systems , Shanghai 200240 , China
1 Department of Computer Science and Engineering, Shanghai Jiao Tong University , Shanghai 200240 , China
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
with DTNs, where the network is subject to more
frequent disruption. However, compared with MANETs and
DTNs, vehicular networks are more complex because of
unique characteristics such as non-uniform node
distribution, fast change of topology, and restricted mobility of
vehicles.
Applications of vehicular networks require efficient
data communications between vehicles. The main
performance of data communication in vehicular networks is
concerned with delivery ratio, delivery delay, and routing
cost. Many routing algorithms have already existed for use
in vehicular networks, such as epidemic [
5
], AODV, GPSR
[
6
], and MaxProp [
7
].
It is very important to understand the performance of
these routing algorithms for vehicular networks.
Unfortunately, most previous work of routing in vehicular
networks assumes oversimplified node mobility when
evaluating the performance of vehicular networks, e.g.,
random mobility [
6,8
] or artificial movement traces [9],
which have been widely used in MANETs and DTNs.
They fail to reflect the inherent complexity of real
vehicular networks. Several important factors of vehicular
networks impact the performance of data delivery in
vehicular networks, which should be taken into account,
e.g., vehicle mobility, vehicle density, and radio
propagation model. Vehicle mobility is restricted by underlying
roads and may have a great impact on the resulting
network topology and hence the availability of radio links
between vehicles. As a result, the routing performance
of the vehicular network is affected by vehicle mobility
[
10,11
].
To understand the achievable performance of
vehicular networks under real and complex environments, we
first comprehensively analyze the factors that may affect
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 [
12
]
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 (...truncated)