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The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV.
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China Communications • October 2014
signal range and drop out of the network, oth-
er vehicles can join in, connecting vehicles to
one another to create a mobile Internet. We de-
termine that VANET only covers a very small
mobile network that is subject to mobility con-
straints and the number of connected vehicles.
Several characteristics of large cities, such as
traffic jams, tall buildings, bad driver behav-
iors, and complex road networks, further hin-
der its use. Therefore, for VANET, the objects
involved are temporary, random and unstable,
and the range of usage is local and discrete,
i.e., VANET cannot provide whole (global)
and sustainable services/applications for cus-
tomers. Over the past several decades, there
has not been any classic or popular implemen-
tation of VANET. The desired commercial
interests have not emerged either. Therefore,
VANET’s usage has begun to stagnate.
In contrast to VANET, IoV has two main
technology directions: vehicles’ neworking
and vehicles’ intelligentialize. Vehicles’ net-
working is consisting of VANET (also called
vehicles’ interconnection), Vehicle Telematics
(also called connected vehicles) and Mobile
Internet (vehicle is as a wheeled mobile termi-
nal). Vehicles’ intelligence is that the integra-
tion of driver and vehicle as a unity is more
intelligent by using network technologies,
which refers to the deep learning, cognitive
computing, swarm computing, uncertainty
articial intelligence, etc. So, IoV focuses on
the intelligent integration of humans, vehi-
cles, things and environments and is a larger
Abstract: The new era of the Internet of
Things is driving the evolution of conventional
Vehicle Ad-hoc Networks into the Internet of
Vehicles (IoV). With the rapid development of
computation and communication technologies,
IoV promises huge commercial interest and
research value, thereby attracting a large
number of companies and researchers. This
paper proposes an abstract network model of
the IoV, discusses the technologies required to
create the IoV, presents different applications
based on certain currently existing
technologies, provides several open research
challenges and describes essential future
research in the area of IoV.
Keywords: internet of vehicles; VANET;
vehicle telematics; network model
According to recent predictions1, 25 billion
“things” will be connected to the Internet
by 2020, of which vehicles will constitute a
significant portion. With increasing numbers
of vehicles being connected to the Internet of
Things (IoT), the conventional Vehicle Ad-
hoc Networks (VANETs) are changing into
the Internet of Vehicle (IoV). We explore the
reasons for this evolution below.
As is well-known, VANET [1] turns every
participating vehicle into a wireless router or
mobile node, enabling vehicles to connect to
each other and, in turn, create a network with
a wide range. Next, as vehicles fall out of the
An Overview of Internet of Vehicles
YANG Fangchun, WANG Shangguang, LI Jinglin, LIU Zhihan, SUN Qibo
State Key Laboratory of Networking and Switching Technology
Beijing University of Posts and Telecommunications, Beijing, China
1 http://www.academia.
China Communications • October 2014 2
the conventional Vehicle Ad-hoc Networks
(VANETs), Vehicle Telematics, and other con-
nected vehicle networks have to evolve into
the Internet of Vehicle (IoV). The question
accordingly arises as to why such systems did
not evolve into IoT, Internet or wireless mo-
bile networks.
The main reason is that some characteris-
tics of IoV are different from IoT, Internet or
wireless mobile networks. Firstly, in wireless
mobile networks, most end-users’ trajectories
follow a random walk model. However, in
IoV, the trajectory of vehicles is subject to the
road distributions in the city. Secondly, IoT
focuses on things and provides data-aware-
ness for connected things, while the Internet
focuses on humans and provides information
services for humans. However, IoV focuses
on the integration of humans and vehicles, in
which, vehicles are an extension of a human’s
abilities, and humans are an extension of a
vehicle’s intelligence. The network model,
the service model, and the behavior model of
human-vehicle systems are highly different
from IoT, Internet or wireless mobile network.
Finally, IoV interconnects humans within and
around vehicles, intelligent systems on board
vehicles, and various cyber-physical systems
in urban environments, by integrating vehi-
cles, sensors, and mobile devices into a global
network, thus enabling various services to be
delivered to vehicles and humans on board
and around vehicles. Several researchers have
referred to the vehicle as a manned computer
with four wheels or a manned large phone in
IoV. Thus, in contrast to other networks, ex-
isting multi-user, multi-vehicle, multi-thing
and multi-network systems need multi-level
collaboration in IoV.
In this paper, we first provide a network
model of IoV using the swarm model and an
individual model. We introduce existing re-
search work focusing on activation and main-
tenance of IoV. Then, we survey the various
applications based on some currently existing
technologies. Finally, we give several open re-
search challenges for both the network model
and the service model of human-vehicle sys-
network that provides services for large cities
or even a whole country. IoV is an open and
integrated network system with high manage-
ability, controllability, operationalization and
credibility and is composed of multiple users,
multiple vehicles, multiple things and multiple
networks. Based on the cooperation between
computation and communication, e.g., col-
laborative awareness of humans and vehicles,
or swarm intelligence computation and cog-
nition, IoV can obtain, manage and compute
the large scale complex and dynamic data of
humans, vehicles, things, and environments to
improve the computability, extensibility and
sustainability of complex network systems
and information services. An ideal goal for
IoV is to finally realize in-depth integration
of human-vehicle-thing-environment, reduce
social cost, promote the efficiency of trans-
portation, improve the service level of cities,
and ensure that humans are satisfied with
and enjoy their vehicles. With this denition,
it is clear that VANET is only a sub network
of IoV. Moreover, IoV also contains Vehicle
Telematics [2], which is a term used to dene
a connected vehicle interchanging electronic
data and providing such information services
as location-based information services, remote
diagnostics, on-demand navigation, and au-
dio-visual entertainment content. For IoV, Ve-
hicle Telematics is simply a vehicle with more
complex communication technologies, and the
intelligent transportation system is an applica-
tion of IoV, but vehicle electronic systems do
not belong to IoV.
In the last several years, the emergence of
IoT, cloud computing, and Big Data has driven
demand from a large number of users. Individ-
ual developers and IT enterprises have pub-
lished various services/applications. However,
because VANET and Vehicle Telematics lack
the processing capacity for handling global
(whole) information, they can only be used in
short term applications or for small scale ser-
vices, which limits the development and popu-
lar demand for these applications on consumer
vehicles. There is a desperate need for an open
and integrated network system. Therefore,
This paper proposes
an abstract network
model of the IoV, dis-
cusses the technolo-
gies required to create
the IoV, and presents
different applications
based on certain cur-
rently existing tech-
China Communications • October 2014
people who consume or provide services/ap-
plications of IoV. Human do not only contain
the people in vehicles such as drivers and pas-
sengers but also the people in environment of
IoV such as pedestrians, cyclists, and drivers’
family members. Vehicle in IoV terminology
refers to all vehicles that consume or provide
services/applications of IoV. Thing in IoV ter-
minology refers to any element other than hu-
man and vehicle. Things can be inside vehicles
or outside, such as AP or road. Environment
refers to the combination of human, vehicle
and thing.
The individual model focuses on one vehi-
cle. Through the interactions between human
and environment, vehicle and environment,
and thing and environment, IoV can provide
services for the vehicles, the people and the
things in the vehicles. In the model, the in-
tra-vehicle network is used to support the
interaction between human and vehicle, and
the interaction between vehicle and thing in
that vehicle. The inter-vehicle network is used
tems, i.e., enhanced communication through
computation and sustainability of service pro-
viding, and outline essential future research
work in the area of IoV.
The rest of this paper is organized as fol-
lows. Section 2 describes our proposed net-
work model of IoV. The overview of IoV is
presented from three different perspectives in
Section 3. In Section 4, several open research
challenges and essential future research work
related to IoV are outlined. Finally, we present
this paper’s conclusions in Section 5.
As shown in Fig. 1, we propose a network
model of IoV based on our previous work [3],
in which the model is composed of a swarm
model and an individual model. The key as-
pect of the network model is the integration
between human, vehicle, thing, and environ-
Human in IoV terminology refers to all the
Fig.1 Network model of IoV
China Communications • October 2014 4
significantly improve the quality of vehicle
service, while a bad wireless access may often
lead to the breakdown of services. As is well-
known, routing technology is the research core
of traditional networks. For IoV, while routing
is still the core of the inter-vehicle network,
it is also essential for delivering the control
message. Finally, IoV has the two most im-
portant elements, i.e., users and network. For
a simple IoV, wireless access is its user, and
routing is its network. With the development
of IoV, however, these elements might be less
important, and other technologies may play
a vital role, such as collaboration technology
and swarm intelligence computing. However,
due to page limitations, a detailed discussion
is beyond this paper.
Note that the technologies introduced in this
section cannot cover the technologies of IoV,
and most of them belong to VANET [4] or Ve-
hicle Telematics. The reason is that IoV is an
open and integrated network system composed
of multiple users, multiple vehicles, multiple
things and multiple networks, and an integrat-
ed IoV is not described. Hence, this section
mainly focuses on existing technologies and
applications, even if they do not represent the
technologies and applications of IoV.
3.1 Activation of IoV
There are many steps in the activation of IoV,
but the most important step is to take the vehi-
cles into the integrated network of IoV using
wireless access technologies. At present, there
are many existing wireless access technologies
such as WLANs, WiMAX, Cellular Wireless,
and satellite communications [5]. As shown in
Fig. 1, most of these technologies are used to
connect vehicles to each other in IoV.
WLAN contains IEEE 802.11a/b/g/n/p
standards. IEEE 802.11-based WLAN, which
has achieved great acceptance in the market,
supports short-range, relatively high-speed
data transmission. The maximum achievable
data rate in the latest version (802.11n) is ap-
proximately 100 Mbps. IEEE 802.11p is a new
communication standard in the IEEE 802.11
family which is based on the IEEE 802.11a.
to support the interaction between human and
environment, vehicle and environment, and
thing and environment. Swarm model focus-
es on multi-user, multi-vehicle, multi-thing
and multi-network scenarios. Through swarm
intelligence, crowd sensing and crowd sourc-
ing, and social computing, IoV can provide
services/applications. Moreover, in this model,
the interaction between human and human, ve-
hicle and vehicle, and thing and thing, all need
an integrated network to collaborate with each
other and with the environment. Note that IoV
has a computation platform for providing vari-
ous decisions for whole network, and there are
many virtual vehicles with drivers correspond-
ing to physica vehicles and drivers. Then we
call the virtual vehicle with driver as Autobot.
In the IoV, Autobot can interact with each
other by using swarm computing technologies
and provide decision-making information for
IoV in the computation platform.
Over a decade ago, both industrial and aca-
demic researchers proposed many advanced
technologies for the application layer, the mo-
bile model & the channel model, the physical
layer & the data link layer, the network layer
& the transport layer, and security & privacy;
these technologies are all used in IoV. In this
section, we only focus on giving an overview
of the technologies and their applications in
IoV, and do not describe the details of the
technologies. The overview describes the acti-
vation of the IoV, maintenance of the IoV, and
IoV applications.
For the activation and maintenance of the
IoV, we only summarize the wireless access
technology and the routing technology. There
are several reasons for focusing on these
two technologies. Firstly, most researchers
working on IoV focus on wireless access and
routing, for which the number of proposed re-
search works are the highest. Secondly, wire-
less access technologies play an important role
in IoV. A good wireless access technology can
China Communications • October 2014
quality of service, even for non-line-of-sight
transmissions. The key advantage of WiMAX
compared to WLAN is that the channel access
method in WiMAX uses a scheduling algo-
rithm in which the subscriber station needs
to compete only once for initial entry into the
Cellular wireless comprises of 3G, 4G
and LTE. Current 3G networks deliver data
at a rate of 384 kbps to moving vehicles, and
can go up to 2 Mbps for xed nodes. 3G sys-
tems deliver smoother handoffs compared to
WLAN and WiMAX systems, and many nota-
ble works have been proposed. For example,
Chao et al. [8] modeled the 3G downloading
and sharing problem in integration networks.
Qingwen et al. [9] made the first attempt in
exploring the problem of 3G-assisted data
delivery in VANETs. However, due to central-
ized switching at the mobile switching center
(MSC) or the serving GPRS support node
(SGSN), 3G latency may become an issue for
many applications. Vinel [3] provided an an-
IEEE 802.11p is designed for wireless access
in the vehicular environment to support intel-
ligent transport system applications. The use
of wireless LANs in VANETs requires further
research. For example, Wellens et al. [6] pre-
sented the results of an extensive measure-
ment campaign evaluating the performance of
IEEE 802.11a, b, and g in car communication
scenarios, and showed that the velocity has a
negligible impact, up to the maximum tested
speed of 180 km/h. Yuan et al. [7] evaluated
the performance of the IEEE 802.11p MAC
protocol applied to V2V safety communica-
tions in a typical highway environment. Wi-
MAX contains IEEE 802.16 a/e/m standards.
IEEE 802.16 standard-based WiMAX are able
to cover a large geographical area, up to 50
km, and can deliver significant bandwidth to
end-users - up to 72 Mbps theoretically. While
IEEE 802.16 standard only supports fixed
broadband wireless communications, IEEE
802.16e/mobile WiMAX standard supports
speeds up to 160 km/h and different classes of
Fig.2 Wireless access technologies in IoV
Management and
Control on the AP
Management and
Control on the AP
Management and
Control on the AP
Cellular Network
Cellular Network
Satellite Network
Management and
Control on the AP
Centralized Management
and Control Unit
and Control Unit
and Control Unit
China Communications • October 2014 6
3.2 Maintenance of IoV
There are also many aspects to the mainte-
nance of IoVs, such as data-awareness, virtual
networks, and encoding, but the most im-
portant aspect is the switching of the control
message for IoV. Routing technology is the
suitable solution, and in IoV, is dependent on
a number of factors such as velocity, density,
and direction of motion of the vehicles. As
shown in Fig. 1, vehicles can either be the
source or the destination during the process of
routing, and various standards have been built
to accomplish the task of routing. With the
growing needs of the users to access various
resources during mobility, efcient techniques
are required to support their needs and keep
them satised.
Topology based maintenance: Because of
the large overhead incurred for route discovery
and route maintenance for highly mobile unco-
ordinated vehicles, only a few of the existing
routing protocols for inter-vehicle networks
are able to handle the requirements of safety
applications [10,11]. An important group of
routing protocols for ad-hoc networks is based
on topology, and needs the establishment of
an end-to-end path between the source and the
destination before sending any data packet.
Due to rapid changes in the network topology
and highly varying communication channel
conditions, the end-to-end paths determined
by regular ad-hoc topology-based routing pro-
tocols are easily broken. To solve this prob-
lem, several routing protocols have been pro-
posed [12,13] [14,15] [16] [17]. For example,
Namboodiri and Gao [12] proposed a predic-
tion-based routing for VANETs. The PBR is a
reactive routing protocol, which is specically
tailored to the highway mobility scenario, to
improve upon routing capabilities without us-
ing the overhead of a proactive protocol. The
PBR exploits the deterministic motion pat-
tern and speeds, to predict roughly how long
an existing route between a “node” vehicle
and a “gateway” vehicle will last. Using this
prediction, the authors pre-emptively create
new routes before the existing route lifetime
alytical framework which allows comparing
802.11p/WAVE and LTE protocols in terms of
the probability of delivering the beacon before
the expiration of the deadline. Lei et al. [4]
studied the potential use cases and technical
design considerations in the operator con-
trolled device-to-device communications. The
potential use cases were analyzed and classi-
fied into four categories. Each use case had
its own marketing challenges and the design
of related techniques should take these fac-
tors into consideration. Gerla and Kleinrock
[5] discussed LTE cellular service in a future
urban scenario with very high bandwidth and
broad range. The so-called cognitive radios
will allow the user to be “best connected” all
the time. For instance, in a shopping mall or in
an airport lounge, LTE will become congested,
and the user’s cognitive radio will disconnect
from LTE. For vehicles, due to large costs, sat-
ellite communications are barely used, except
for GPS. It is only a supplement for temporary
and emergency uses, when other communica-
tion technologies are invalid or unavailable.
Looking at the wireless access technologies
described above, we think that the 4G or LTE
should be the most efficient technology to
launch the inter-vehicle network and to acti-
vate the IoV. The reasons are as follows. First-
ly, 4G or LTE is the most used communication
standard, and has been deployed by most
countries to provide access services. Obvious-
ly, any vehicle can use it to connect to the IoV.
Secondly, in the context of high buildings and
a complex city environment, the performance
of 4G or LTE is the best among all wireless
access technologies. Finally, in the past ten
years, the development of VANET has been
very slow, and can barely be used in the real
world. The main reason is that the connected
vehicles cannot maintain VANET in city roads
because the goals of drivers are random and
different. To maintain the VANET, all vehicles
must access the integrated network of IoV,
after which IoV can be activated to provide
services for users.
China Communications • October 2014
which combines store-carry-and-forward tech-
nique with routing decisions based on geo-
graphic location. These geographic locations
are provided by GPS devices. In GeoSpray,
authors proposed a hybrid approach, mak-
ing use of a multiple copy and a single copy
routing scheme. To exploit alternate paths,
GeoSpray starts with multiple copy schemes
which spread a limited number of bundle cop-
ies. Afterwards, it switches to a single copy
scheme, which takes advantage of additional
opportunities. It improves delivery success
and reduces delivery delay. The protocol ap-
plies active receipts to clear the delivered bun-
dles across the network nodes. Compared with
other geographic location-based schemes, and
single copy and non-location based multiple
copy routing protocols, it was found that Geo-
Spray improves delivery probability and re-
duces delivery delay. In contrast to the above
work, Bernsen and Manivannan proposed [20]
a routing protocol for VANETs that utilizes an
undirected graph representing the surrounding
street layout, where the vertices of the graph
are points at which streets curve or intersect,
and the graph edges represent the street seg-
ments between those vertices. Unlike existing
protocols, it performs real-time, active trafc
monitoring and uses these data and other data
gathered through passive mechanisms to as-
sign a reliability rating to each street edge.
Then, considering the different environments,
a qualitative survey of position-based rout-
ing protocols was made in [21], in which the
major goal was to check if there was a good
candidate for both environments or not. An-
other perspective was offered by Liu et al.
[22], who proposed a relative position based
message dissemination protocol to guarantee
high delivery ratio with acceptable latency and
limited overhead. Campolo et al. [23] used the
time, space and channel diversity to improve
the efficiency and robustness of network ad-
vertisement procedures in urban scenarios.
Clustering based maintenance: In this
type of routing scheme, one of the nodes
among the vehicles in the cluster area becomes
a clusterhead (CH), and manages the rest of
expires. Toutouh et al. [13] proposed a well-
known mobile ad hoc network routing proto-
col for VANETs to optimize parameter settings
for link state routing by using an automatic
optimization tool. Nzounta et al.[15] proposed
a class of road-based VANET routing proto-
cols. These protocols leverage real-time ve-
hicular trafc information to create paths. Fur-
thermore, geographical forwarding allows the
use of any node on a road segment to transfer
packets between two consecutive intersections
on the path, reducing the path’s sensitivity to
individual node movements. Huang et al. [16]
examined the efciency of node-disjoint path
routing subject to different degrees of path
coupling, with and without packet redundancy.
An Adaptive approach for Information Dis-
semination (AID) in VANETs was presented
in [14], in which each node gathered the in-
formation on neighbor nodes such as distance
measurements, xed upper/lower bounds and
the number of neighboring nodes. Using this
information, each node dynamically adjusts
the values of local parameters. The authors
of this approach also proposed a rebroadcast-
ing algorithm to obtain the threshold value.
The results obtained show that AID is better
than other conventional schemes in its cate-
gory. Fathy et al. [17] proposed a QoS Aware
protocol for improving QoS in VANET. The
protocol uses Multi-Protocol Label Switching
(MPLS), which runs over any Layer 2 technol-
ogies; and routers forward packets by looking
at the label of the packet without searching the
routing table for the next hop.
Geographic based maintaining. The geo-
graphic routing based protocols rely mainly
on the position information of the destination,
which is known either through the GPS sys-
tem or through periodic beacon messages. By
knowing their own position and the destination
position, the messages can be routed directly,
without knowing the topology of the network
or prior route discovery. V. Naumov et al. [18]
specically designed a position-based routing
protocol for inter-vehicle communication in a
city and/or highway environment. Soares et al.
[19] proposed the GeoSpray routing protocol,
China Communications • October 2014 8
dynamic transmission range, the direction of
vehicles, the entropy, and the distrust value
parameters. Wang et al. [26] rened the orig-
inal PC mechanism and proposed a passive
clustering aided mechanism, the main goal of
which is to construct a reliable and stable clus-
ter structure for enhancing the routing perfor-
mance in VANETs. The proposed mechanism
includes route discovery, route establishment,
and data transmission phases. The main idea
is to select suitable nodes to become cluster-
heads or gateways, which then forward route
request packets during the route discovery
phase. Each clusterhead or gateway candidate
self-evaluates its qualication for clusterhead
or gateway based on a priority derived from a
the nodes, which are called cluster members.
If a node falls in the communication range of
two or more clusters, it is called a border node.
Different protocols have been proposed for
this scheme, and they differ in terms of how
the CH is selected and the way the routing is
done. R. S. Schwartz et al. [24] proposed a
dissemination protocol suitable for both sparse
and dense vehicular networks. Suppression
techniques were employed in dense networks,
while the store-carry-forward communication
model was used in sparse networks. A. Daein-
abi [25] proposed a novel clustering algorithm
- vehicular clustering - based on a weighted
clustering algorithm that takes into consider-
ation the number of neighbors based on the
Table I Relative comparison of routing protocols in IoV maintain schemes
density Speed Probabilistic
Delay Routing Latency No. of
Hops Distance Packet
Loss Throughput Bandwidth Feasibility
PBR [10] Medium High High ND Medium Low Medium ND Medium Medium
OLSR [11] Low Medium Medium Medium Low ND Low High Low Low
AID [12] High Medium Medium Low High Medium ND ND Medium Low
eMDR [26] High ND High Medium High Medium High ND Low High
SADV [27] Medium Medium Low Medium High High Medium Medium ND Low
RBVT-R[13] Medium ND Medium Low Medium Medium ND High Medium Medium
QoSAware[15] ND ND Low ND Low Low Medium Low Low Low
CAR [16] Medium Medium Low High ND High ND Low Low Low
GRANT [28] High Medium Low ND High Low ND ND ND Low
GpsrJ+ [29] High Medium Medium High Medium Low ND ND ND Medium
GyTAR [30] Medium Medium Low Medium Low ND ND Medium Medium Medium
LOUVRE [31] Low ND Low High High ND High Low ND Low
GeoCross [32] Medium Low Medium Low High Medium ND ND ND High
GeoSpray[17] Medium Medium Low Low ND ND Low ND High Low
RIVER [18] Medium Low ND Medium Medium ND ND Low Medium Medium
GeoSVR [33] Medium Medium High Medium High High Low ND ND Medium
RPB-MD [20] High High High Medium ND ND ND High Medium High
AVRM [21] High Medium Medium High ND Medium Medium ND High Medium
FTLocVSDP[34] Low ND Medium Low Low High Low Medium High High
SRD [22] High High High ND High Medium Medium High High Medium
VWCA [23] Medium High High ND Medium ND Low High High High
PassCAR [24] High High Low Low High Medium ND Medium Low Low
MDDC [35] Medium Low Medium ND Medium Low ND Medium High Medium
C-VANETs [25] Low High ND Low Medium High Medium Medium High Medium
VADD [36] ND Low ND High High High High High High Medium
MURU [37] High High Medium Low Medium Medium Low High High Medium
EEDAHRP[38] Medium ND Medium Medium Medium Medium ND Low High Medium
ND = Not Determined
China Communications • October 2014
avoidance. At present, collision avoidance
technologies are largely vehicle-based systems
offered by original equipment manufacturers
as autonomous packages which broadly serve
two functions, collision warning and driver
assistance. The former warns the driver when
a collision seems imminent, while the latter
partially controls the vehicle either for steady-
state or as an emergency intervention [41]. To
be specic, collision warning includes noti-
cations about a chain car accident, warnings
about road conditions such as slippery road,
and approaching emergency vehicle warning
[5]. On the one hand, collision warnings could
be used to warn cars of an accident that oc-
curred further along the road, thus presenting
a pile-up from occurring. On the other hand,
they could also be used to provide drivers with
early warnings and prevent an accident from
happening in the rst place. Note that driving
near and through intersections is one of the
most complex challenges that drivers face be-
cause two or more trafc ows intersect, and
the possibility of collision is high [42]. The
intelligent intersection, where such conven-
tional trafc control devices as stop signs and
trafc signals are removed, has been a hot area
of research for recent years. Vehicles coordi-
nate their movement across the intersection
through a combination of centralized and dis-
tributed real-time decision making, utilizing
global positioning, wireless communications
and in-vehicle sensing and computation1. A
number of solutions for collision avoidance
of multiple vehicles at an intersection have
been proposed. A computationally efficient
control law [43-45] has been derived from ex-
ploitation of the monotonicity of the vehicles’
dynamics, but it has not been applied to more
than two vehicles. An algorithm that addresses
multi-vehicle collisions, based on abstraction,
has been proposed in [46]. An algorithmic ap-
proach to enforcing safety based on a time slot
assignment, which can handle a larger number
of vehicles, is found in [41]. Colombo et al.
[47] designed a supervisor for collision avoid-
ance, which is based on a hybrid algorithm
that employs a dynamic model of the vehicles
weighted combination of the proposed metrics.
P. Miao [27] proposed a cooperative commu-
nication aware link scheduling scheme, with
the objective of maximizing the throughput
for a session in C-VANETs. They let the RSU
schedule the multi-hop data transmissions
among vehicles on highways by sending small
sized control messages.
Based on the above overview, we provide
a relative comparison of all routing protocols
in Tab 1. In this table, Route length is the total
distance between source and destination. PDR
is the packet delivery ratio. Latency is the in-
terval of time between the rst broadcast and
the end of the last host’s broadcast. Latency
includes buffering, queuing, transmission and
propagation delays.
3.3 IoV applications
With the rapid development of numeric infor-
mation technology and network technology, it
is brought forward that theautomatization and
intelligentization of vehicle.This gives birth
to lots of applications which combine safe
driving with service provision. For example,
Apple CarPlay, originally introduced as iOS in
vehicles, offer full-on automobile integration
for Apple’s Maps and turn-by-turn navigation,
phone, iMeessage, and music service5. Similar
to CarPlay, Google Android Auto provides a
distraction-free interface that allows drivers
enjoy the services by connecting Android de-
vices to the vehicle. Chinese Tecent recently
launched its homegrown navigation app Lu-
bao that features user generated contents and
social functions7. For demonstration purposes,
in this paper, IoV applications can be divided
into two major categories: Safety applications
and User applications. Applications that in-
crease vehicle safety and improve the safety
of the passengers on the roads by notifying the
vehicles about any dangerous situation in their
neighborhood are called safety applications.
Applications that provide value-added services
are called User applications.
Technologies to enhance vehicular and
passenger safety are of great interest, and
one of the important applications is collision
China Communications • October 2014 10
physical connection to the vehicle. When a ve-
hicle enters the area near a service garage, the
service garage can query the vehicle for its di-
agnostic information to support the diagnosis
of the problem reported by the customer. Even
as the vehicle approaches, the vehicle’s past
history and the customer’s information can be
retrieved from a database and made available
for the technician to use [49].
The objective of IoV is to integrate multiple
users, multiple vehicles, multiple things and
multiple networks, to always provide the best
connected communication capability that is
manageable, controllable, operational, and
credible. Such a network system is not cur-
rently available, but it is highly desirable for
advancing the capabilities of future IoV ap-
plications. Efcient wireless access solutions
will be essential for manageable and credible
IoV. The solutions should consider the com-
munication coverage limitation in a complex
city. Sophisticated solutions can be developed
that enhance the communication ability using
diverse technologies for the IoV. The transport
of big data, especially video, over the IoV,
can aggravate the network burden. Efficient
methods will also need to be developed for the
sustainability of service providing as vehicles
become mobile nodes in the global network.
The integration of human and vehicle as one
end-to-end user creates a new network model
and service model, which are very different
from existing models. These new models
may disturb the operations of the IoV. New
methods will be needed to assure a good qual-
ity IoV experience.
Although many challenges have been
proposed, most of them have focused on the
VANET or Vehicle Telematics, and few re-
searchers have proposed the challenge of IoV
from a multi-user, multi-vehicle, multi-thing
and multi-network perspective. Obviously,
there are many unprecedented challenges for
IoV, but we only specify three challenges in
this section that need to be urgently addressed:
and periodically solves a scheduling problem.
The intelligent intersection is motivated by the
potential benets of comfort, safety, and ef-
ciency. Removing the driver from negotiating
the passage through complicated intersections
will improve driver comfort. Furthermore,
smooth coordination of vehicles through in-
tersections will provide improvements in fuel
efciency, vehicle wear, travel time and trafc
ow. It can also be switched over seamlessly
at higher trafc intensities to a more tradition-
al trafc control operation without the need for
any lights, stop signs or human intervention
User applications are quite varied, ranging
from real-time or non-real-time multimedia
streaming and interactive communications
such as video-conferencing, weather informa-
tion or Internet access such as data transfer,
Web browsing, music download and interac-
tive games, to roadside service applications,
such as location and price lists of restaurants
or gas-stations [5]. Generally speaking, user
applications provide two basic user-related
services: co-operative local services and global
internet services. Co-operative local services
are applications focusing on infotainment that
can be obtained from locally based services
such as point of interest notification, local
electronic commerce and media downloading.
Global Internet services focus on data that can
be obtained from global Internet services [48].
Typical examples are community services,
such as insurance and nancial services, eet
management and parking zone management,
which focus on software and data updates234.
Moreover, user applications include three
types of use cases. The rst type gives the ve-
hicle or the driver the freedom to access any
type of information available on the Internet.
The second type allows local businesses, tour-
ist attractions, or other points of interest to
advertise their availability to nearby vehicles.
In this case, a roadside unit broadcasts infor-
mation regarding a point of interest such as
its location, hours of operation, and pricing.
And the third type allows a service station to
assess the state of a vehicle without making a
2 http://elib.dlr.
4 http://www.safespot-eu.
China Communications • October 2014
there is still a network bottleneck associat-
ed with wireless access parts. Joint optimi-
zation among all the layers can be challeng-
ing because solving the bottleneck requires
modification of multiple communication
layers. Most of the works on network op-
timization are derived from heuristics and
simulation. A mathematical framework to
characterize the interactions among the lay-
ers would be desirable. There is much work
to be done to extend the communication
capability to each layer. The difculties are
due to 1) dramatic changes in the channel,
network element, mobility, and resource,
and 2) inconsistent optimization goals in
terms of users, services, and networks.
To combat these problems, several mod-
ifications to the network layer have been
proposed in the literature. However, these
modications could not fundamentally im-
prove the communication ability of the IoV.
Achieving a steady supply of bandwidth
for IoV trafc in presence of congestion is
a challenging task. We may use some intel-
ligent technologies to enhance the commu-
nication ability, and subsequently, to reduce
the redundant network trafc.
 
Cooperation technologies of virtual vehi-
cles with drivers. With the popular of in-car
operating systems such as Carplay, Google
Auto Link, QNX Car, more and more ve-
hicles will be connected to IoV. Then in
the computation platform of IoV, massive
virtual vehicles with drivers can interact
with each other, which can provide better
network experience for vehicles and drivers
by using cooperation technologies. The co-
operation among virtual vehicles with driv-
ers contains two phases. The rst phase is
information sensing and collection of phys-
ical vehicles with drivers including nding
the space-time distribution characteristics
and behavior characteristic of vehicles, es-
tablishing a quantitative evaluation system
of service experience quality, etc. The sec-
ond phase is interaction and evaluation of
virtual vehicles with drivers which includes
what’s the model of mixed network and
1) what is the network model and service
model of human-vehicle in IoV? 2) How to
enhance communication ability in IoV? 3)
How to cooperate among Autobots? 4) How to
assure the sustainability of service providing
in IoV?
 
Network model and service model of hu-
man-vehicle. We know that the network
model of a user derives from the Internet
and that the network model of a vehicle
is from VANET, both of which have been
studied for many years, with many sophisti-
cated models published. However, realizing
an efcient network model of human-vehi-
cle is still an open problem. In the IoV, the
network model of human-vehicle should
be addressed to maximize the resource
utilization, robustness and stability of the
network. Although still an open problem,
some approaches could include the use
of deep learning and cognitive comput-
ing along with network link data sharing
through Big Data technologies. A related
issue here is focusing on nding the service
and its space-time distribution characteris-
tics by establishing a quantitative evalua-
tion system of a typical network service re-
quirement. The next question is whether the
service model of human-vehicle would be
the same as the service model of the Inter-
net or a mobile wireless network? There is
almost no research on this issue. The study
of service characteristics during the process
of coordination with services and network
through the user participation mode of ser-
vices, its evolution mechanism and trends,
and the building of a cognitive learning
model are all fundamental research prob-
lems. The data obtained from such research
can help establish a user's service behav-
ior, in terms of a cognitive learning model
combining human, vehicle, and services, to
improve the ability to cope with complex
space-time change of service requirements
in the IoV.
 
Enhancing communication ability. Although
current communication systems provide the
capacity to support network performance,
China Communications • October 2014 12
With the rapid development of Internet and
communication technologies, vehicles that
often quickly move in cities or suburbs
have strong computation and communication
abilities. IoV is emerging as an important part
of the smart or intelligent cities being pro-
posed and developed around the world. IoV
is a complex integrated network system that
interconnects people within and around vehi-
cles, intelligent systems on board vehicles, and
various cyber-physical systems in urban envi-
ronments. IoV goes beyond telematics, vehicle
ad hoc networks, and intelligent transportation
by integrating vehicles, sensors, and mobile
devices into a global network to enable vari-
ous services to be delivered to vehicular and
transportation systems and to people on board
and around vehicles. This paper first gives a
network model of IoV, and later provides an
abstract taxonomy of IoV activation, mainte-
nance, and applications. Finally, an analysis of
challenges and future study directions in IoV
is also provided.
The work presented in this study is supported
by the Natural Science Foundation of Beijing
under Grant No.4132048, NSFC(61472047),
and NSFC(61202435).
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served as PC Chair of IoV 2014 and SC2 1014, Spe-
cial Track Chair of IEEE APSCC 2014, Vice President
of Service Society Young Scientist Forum (China)
and Guest Editor of IEEE System Journal, Journal of
Computational Science, Journal on Wireless Commu-
nications and Networking, etc. His research interests
include service computing, cloud services and Inter-
net of vehicles.
LI Jinlin, is an associate professor at Beijing Univer-
sity of Posts and Telecommunications. He received
his Ph.D. degree at Beijing University of Posts and
Telecommunications in 2004. His research interests
include Converged network and Service support en-
LIU Zhihan, is a lecturer in State Key Lab of Net-
working and Switching Technology, Beijing University
of Posts and Telecommunications. He has served
as Deputy Secretary-General of China IoV Industry
Technology Innovation Strategic Alliance. His re-
search interests include IoT, IoV and decentralized
SUN Qibo, He received the Ph.D. degree in Com-
munication and Electronic System from the Beijing
University of Posts and Telecommunication, in 2002.
He is currently an Associate Professor at the Beijing
University of Posts and Telecommunication in China.
He is a member of the CCF. His current research in-
terests include network security, network intelligence
and services
IEEE Conference on, 2011, pp. 6134-6139.
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YANG Fangchun, received his PhD degree in com-
munication and electronic system from the Beijing
University of Posts and Telecommunication in 1990.
He is currently a professor at the Beijing University of
Posts and Telecommunication, and president of Stra-
tegic Alliance of China Internet of Vehicles Industrial
Technology Innovation. His current research interests
include network intelligence and Internet of vehicles.
He is a fellow of the IET.
WANG Shangguang, is an associate professor at
Beijing University of Posts and Telecommunications.
He received his Ph.D. degree at Beijing University
of Posts and Telecommunications in 2011. He has
... , n, and ∆i = 5}, for each SW size. The SW sizes taken represent a set of multiples of NB, where SWset = {k * NB/ k = [1,3], ∆k = 0.2}. Note that, when multiple NB values equal zero (k=1), it means that the SW has just one STT (SW=1). ...
Full-text available
Traditional vehicle ad hoc networks (VANETs) have evolved toward the Internet of Vehicles (IoV) during the past ten years with the introduction of 5G communication technology and the growing number of vehicles linked to the Internet. The coexistence of IEEE 802.11p and 5G becomes critical to build a heterogeneous IoV system that benefits from both technologies, being that the IEEE 802.11p standard remains the best option for direct communications and safety-critical applications. The IEEE 1609 standard family and the ETSI ITS-G5 standard family both use the IEEE 802.11p standard as a MAC mechanism. To avoid dangerous situations, vehicles require the periodic exchange of awareness messages. With the increase in vehicle density, the MAC layer will suffer from radio channel congestion problems, which in turn will have a negative impact on the safety application requirements. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate channel congestion; this was achieved by adapting transmission parameters such as transmit power and data rate. However, several studies have demonstrated that DCC has drawbacks and suffers from poor performance when the channel load is very high. This paper investigates a new promising DCC technique called transmission timing control (TTC), to reduce the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. Obtained results showed a convergence between the applied experiments and our mathematical model, achieving an error margin of only 5%, which confirms the validity of the equation proposed.
... Therefore, the relationship between radar and camera LC coordinates is an affine transformation, and there is no translation in the transformation because the origin of their coordinate system overlaps. Hence, the transformation of camera LC to radar LC is given as (6) x r = R sin(θ) y r = R cos(θ) . ...
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Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. The effectiveness of the digital twin building is verified based on the real-world traffic data.
... If we look at the table, we can see that I pulled 24 articles from it, all of which are based on IOV and IoT. When I checked Google Scholar, I found that most papers on the IoT are about security and attacks, which Types of Security Measurement [18] The growth of the internet of vehicles is accelerating as it links the occupants of the car to various cyber systems that can be used to scan for and identify viruses. By connecting vehicles, sensors, and mobile devices into a worldwide network, IoV extends beyond telematics, vehicle ad hoc networks, and intelligent transportation to enable the delivery of numerous services to vehicular and transportation systems, as well as to persons inside and around vehicles. ...
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The Internet of Vehicles IoV commonly referred to as connected automobiles is a vast network that connects various entities including users sensors and vehicles They will connect across a network to lessen traffic accidents and improve both the security and safety of smart vehicles The Internet of Vehicles is subject to a wide variety of threats including spoofing attacks recognition attacks privacy attacks and verification attacks Our the primary concern when creating any new smart gadget is the users safety which will be improved by identifying solutions to the various cyber threats Therefore we will cover the security of smart automobiles in this literature review including their attacks and solutions.
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This paper investigates low‐latency offloading strategy in a non‐orthogonal multiple access aided mobile edge computing (NOMA‐MEC) system consisting of K edge servers, one mobile user and one cloud server. An intelligent edge server selection strategy (IESSS) based on Markov decision process (MDP) is proposed to select an edge server, in order to reduce the task completion latency of this system. When an edge server is selected by the proposed IESSS, a joint optimization problem of power allocation and task scheduling factors is formulated to minimize the task completion latency of the hybrid NOMA‐MEC system. To solve the formulated non‐convex optimization problem with coupled variables, a low‐complexity adaptive power‐task resource allocation iterative (APTRAI) algorithm is proposed. Simulation results demonstrate the advantages of the proposed IESSS and verify the convergence and time complexity of the proposed APTRAI algorithm.
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A new networking paradigm, which is seen as a cutting-edge approach to networks, is currently a priority research area: nature-inspired networking (NiN), which is inspired from nature such as biological, social, and physical phenomena. The book is a place for highly original ideas about how the nature is going to shape networking systems of the future. Hence, it focuses on theory and applications, which encompass rigorous approaches and cutting-edge solutions that take inspiration from nature for the development of novel problem solving techniques. To this end, we will take advantage of formal engineering methods and establish in this book formal and practical aspects of NiN to achieve foundations and practice of NiN. The book is a reference for readers who already have a basic understanding of networking and are now ready to know how to use rigorous approaches to develop networking that is inspired by nature. Hence, the book includes both theoretical contributions and reports on applications. To keep a reasonable trade off between theoretical and practical issues, chapters were carefully selected to, on one hand, cover a broad spectrum of formal and practical aspects and, on the other hand, achieve as much as possible in a self-contained book. Formal and practical aspects are presented in a straightforward fashion by discussing in detail the necessary components and briefly touching on the more advanced components. Therefore, theory and applications demonstrating how to use the formal engineering methods for NiN will be described using sound judgment and reasonable justifications. This book, with chapters contributed by prominent researchers from academia and industry, will serve as a technical guide and reference material for engineers, scientists, practitioners, and researchers by providing them with state-of-the-art research findings and future opportunities and trends. These contributions include state-of-the-art architectures, protocols, technologies, and applications in NiN. In particular, the book covers existing and emerging research issues in NiN. The book has nine chapters addressing various topics from theory to applications of NiN based on rigorous interdisciplinary approaches.
The Internet of Vehicles is the next‐generation information and technology infrastructure for solving existing challenges. The ability to predict vehicle destinations is a prerequisite for providing more applications, e.g., content caching and data dissemination. However, the data in a real‐world system could be limited. In this letter, the vehicle destination prediction problem with limited data is defined, and a general clustering‐then‐classification framework is further proposed as the solution. Based on a real‐world dataset, the experimental results show that the combination of the DBSCAN clustering algorithm and the XGBoost classifier outperforms the baselines. This article is protected by copyright. All rights reserved.
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Vehicular Ad hoc Networks (VANET) as a sub class of Mobile Ad hoc Networks (MANET) provides a wireless communication among vehicles and vehicle to road side equipment [1]. Important applications of VANET are providing safety for passengers in one hand, and also resource efficiency including traffic as well as environmental efficiency on the other hand. As a result, providing Quality of Service (QoS) has a great role in Intelligent Transportation System (ITS). Different methods over network layers, especially over layer 2 and layer 3 were recently proposed to support QoS in VANET [2]. But in this paper, MPLS [11] as a forwarding method which can be compatible with any layer 2 technology is used in road side backbone network, to improve QoS in terms of end-to-end delay, packet loss and throughput in urban areas, where lots of roadside unit exist (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]
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The moving with fast velocity of each node in vehicular ad hoc network (VANET) results in the existence transient communication links, which degrade the performance of developed protocols. Established routes frequently become invalid, and existing communication flows are interrupted, incurring delay and additional overhead. In this paper we concentrate on measurement end-to-end delay in VANET, and using these results for building route metric with the aim of shrinking route discovery time. For deploying route model we use hybrid routing protocol (combined between proactive and reactive routing protocol) aiming reduce route overhead, accelerate route convergence speed, and enhance route quality in routing table in each node. The simulation results illustrate prominent features of our proposed model.
Internet of vehicles (IoV) is an open converged network system supporting human-vehicles-environment cooperation. Based on in-depth analysis of the development trend of integration of people and vehicles, as well as the demand of human-vehicles-environment cooperation, an object cooperative model was proposed. This model established a new mechanism and an interactive mode of individual objects and group, meanwhile a group collaboration system was put forward. Furthermore, a general architectural reference model of IoV was presented. Utilizing the collaborative computing control layer, the multi-dimensional cooperation of objects-services-communications was performed. Also the key technical requirements for IoV were described. ©, 2014, Beijing University of Posts and Telecommunications. All right reserved.
Conference Paper
In this paper, we propose Geographic Stateless Vehicular Ad Hoc Network (VANET) Routing (GeoSVR), a novel geographic routing protocol that selects an optimal forwarding route using the width of the road to avoid local maximum and improves the forwarding algorithm to ensure the connectivity of the route between two neighbors in an urban VANET. GeoSVR uses the natural connective feature of an urban map to calculate a forwarding route, the packets are forwarded along it and two modes in typical geographic routing protcols are eliminated. The salient features of optimal forwarding route, restricted forwarding algorithm and stateless routing, make GeoSVR suitable for high speeds and dynamical topologies. We showed that in line and urban scenarios, GeoSVR outperforms other wireless routing protocols such as Ad Hoc On Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR). It significantly increases the packet delivery ratio. Although restricted forwarding algorithm brings more hops, GeoSVR's latency is almost not affected and is consistently lower than that of AODV.
To handle the handover challenge in Express Train Access Networks (ETAN), mobility fading effects in high speed railway environments should be addressed first. Based on the investigation of fading effects in this paper, we obtain two theoretical bounds: HO-Timing upper bound and HO-Margin lower bound, which are helpful guidelines to study the handover challenge today and in the future. Then, we apply them to analyze performance of conventional handover technologies and our proposal in ETAN. This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4 ms (which is the fastest one so far), and reduce HO-Margin to 0.16 dB at a train speed of 350 km/h.
Batteries transfer management is one important aspect in electric vehicle (EV) network's intelligent operation management system. Batteries transfer is a special and much more complex VRP (Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots, time windows, simultaneous pickups and deliveries, distance minimization, etc. into account. We call it VRPEVB (VRP with EV Batteries). This paper, based on the intelligent management model of EV's battery power, puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization. By setting a reasonable tabv range, special update rules of the pheromone and path list memory functions, the algorithm can have a better convergence, and its feasibility is proved by the experiment in an EV's demonstration operation system.
Conference Paper
Vehicular 3G (third-generation cellular system) users often need to download files from the Internet through the 3G data network, which is crucial in many application scenarios such as digital map update or location-based advertisement. Although the 3G brings larger coverage and instant access to data transfer, it may also incur high cost. We observe that many applications of vehicular 3G users can actually tolerate certain data access latency. In addition, inter-vehicle communications have been practical and can be exploited for inter-vehicle data delivery. Based on these observations, we propose to augment vehicular 3G users by data sharing through inter-vehicle communications. We formulate an optimization problem. The objective is to minimize the cost of 3G data communications, meanwhile maximizing the success probability of downloading all 3G user data. The two-hop transmission process and the bandwidth limitation in vehicular network are both modeled in the optimization problem. To lower the cost of 3G and meet the delivery ratio and delay constraints of data, one single-stage and one multi-stage algorithms are proposed for selection of seed vehicles (who download the data via 3G channel). We have evaluated our algorithm with simulations with real vehicular traces and the results show that our algorithms reduce the 3G cost and achieve good performance of data downloads.
Conference Paper
As a key enabling technology for the next generation inter-vehicle safety communications, The IEEE 802.11p protocol is currently attracting much attention. Many inter-vehicle safety communications have stringent real-time requirements on broadcast messages to ensure drivers have enough reaction time toward emergencies. Most existing studies only focus on the average delay performance of IEEE 802.11p, which only contains very limited information of the real capacity for inter-vehicle communication. In this paper, we propose an analytical model, showing the performance of broadcast under IEEE 802.11p in terms of the mean, deviation and probability distribution of the MAC access delay. Comparison with the NS-2 simulations validates the accuracy of the proposed analytical model. In addition, we show that the exponential distribution is a good approximation to the MAC access delay distribution. Numerical analysis indicates that the QoS support in IEEE 802.11p can provide relatively good performance guarantee for higher priority messages while fails to meet the real-time requirements of the lower priority messages.
In this paper, we consider a sensory data gathering application of a vehicular ad hoc network (VANET) in which vehicles produce sensory data, which should be gathered for data analysis and making decisions. Data delivery is particularly challenging because of the unique characteristics of VANETs, such as fast topology change, frequent disruptions, and rare contact opportunities. Through empirical study based on real vehicular traces, we find an important observation that a noticeable percentage of data packets cannot be delivered within time-to-live. In this paper, we explore the problem of 3G-assisted data delivery in a VANET with a budget constraint of 3G traffic. A packet can either be delivered via multihop transmissions in the VANET or via 3G. The main challenge for solving the problem is twofold. On the one hand, there is an intrinsic tradeoff between delivery ratio and delivery delay when using the 3G. On the other hand, it is difficult to decide which set of packets should be selected for 3G transmissions and when to deliver them via 3G. In this paper, we propose an approach called 3GDD for 3G-assisted data delivery in a VANET. We construct a utility function to explore the tradeoff between delivery ratio and delivery delay, which provides a unified framework to reflect the two factors. We formulate the 3G-assisted data delivery as an optimization problem in which the objective is to maximize the overall utility under the 3G budget constraint. To circumvent the high complexity of this optimization problem, we further transition the original optimization problem as an integer linear programming problem (ILP). Solving this ILP, we derive the 3G allocation over different time stages. Given the 3G budget at each time stage, those packets that are most unlikely delivered via the VANET are selected for 3G transmissions. We comprehensively evaluate our 3GDD using both synthetic vehicular traces and real vehicular 3G traces. Evaluation results show that our approac- outperforms other schemes under a wide range of utility function deflations and network configurations.