Booysen machine 2012

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. X, NO. X, December201X 1
Copyright ⓒ 2011 KSII
Notes.
DOI: 10.3837/tiis.0000.00.000
Machine-to-Machine (M2M)
Communications in Vehicular Networks
M.J. Booysen1
, J.S. Gilmore1
, S. Zeadally2
, and G.-J.van Rooyen1
1
MIH Media Lab, Dept. of Electrical and Electronic Engineering,
Stellenbosch University, Stellenbosch, 7600, South Africa
[E-mail: {1, jgilmore,gvrooyen}@sun.ac.za]
2
Department of Computer Science and Information Technology,
University of the District of Columbia, Washington,
DC 2008, USA
[E-mail:szeadally@udc.edu]
Abstract
To address the need for autonomous control of remote and distributed mobile systems,
Machine-to-Machine (M2M) communications are rapidly gaining attention from both
academia and industry. M2M communications have recently been deployed in smart grid,
home networking, health care, and vehicular networking environments. This paper focuses on
M2M communications in the vehicular networking context and investigates areas where M2M
principles can improve vehicular networking. Since connected vehicles are essentially a
network of machines that are communicating, preferably autonomously, vehicular networks
can benefit a lot from M2M communications support. The M2M paradigm enhances vehicular
networking by supporting large-scale deployment of devices, cross-platform networking,
autonomous monitoring and control, visualization of the system and measurements, and
security. We also present some of the challenges that still need to be addressed to fully enable
M2M support in the vehicular networking environment. Of these, component standardization
and data security management are considered to be the most significant challenges.
Keywords: M2M, VANET, Protocol, Networking, Security

2 M.J. Booysen et al.: Machine-to-Machine (M2M) Communications in Vehicular Networks
1. Introduction
When two electronic systems communicate autonomously, that is to say without human
intervention, the process is described as Machine-to-Machine (M2M) communications. The
main goal of M2M communications is to enable the sharing of information between electronic
systems autonomously [1] [2]. Due to the emergence and rapid adoption of wireless
technologies, the ubiquity of electronic control systems, and the increasing complexity of
software systems, wireless M2M has been attracting a lot of attention from industry and
academia [3] [4] [5].
M2M communications include wired communications, but recent interest has focused on
wireless M2M communications [4] [6]. Moreover, the number of wireless devices (not
including mobile phones) which operate without human interaction (e.g. weather stations,
electricity meters, point of sales devices), is expected to grow to 1.5 billion by 2014 [7].
Recent research efforts on M2M communications have investigated areas that include network
energy efficiency and green networking, the tradeoff between device power consumption and
device intelligence or processing power, standardization of communications, data aggregation
and bandwidth, privacy and security, and network scalability [3] [4] [6] [7] [8].
Although "M2M architecture" can technically refer to any number of machines
communicating (e.g. based on the fairly broad definition given by the European
Telecommunications Standards Institute, ETSI), it is generally accepted that M2M principles
apply particularly well to networks where a large number of machines is used, even up to the
1.5 billion wireless devices of the future. This means that when an M2M application is
discussed, it is generally presented on a national or global scale with a multitude of sensors
that are centrally coordinated. Consequently, when we evaluate a vehicular network as a type
of M2M architecture, this evaluation is done for a large-scale network that potentially has
many other devices connected to it, some of which may not be related to vehicular networks.
In this work we explore how vehicular networks can leverage the M2M paradigm to support
vehicular communications. We present a brief overview of some of the most recent application
areas of M2M, namely smart grid technology, home networking, health care, and vehicular
networking. The M2M network communication layers in a vehicular network are then
explored. We identify five fundamental M2M concepts that have been reported in the
literature to address vehicular communication challenges, such as the support for large-scale
deployment of devices, cross-platform networking, autonomous monitoring and control,
visualization of the system, and security.
The remainder of the paper is organized as follows. Section 2 presents a generic M2M
architecture and describes some application areas where M2M support is being used. Section 3
presents the communication layers for vehicular networks and describes how these layers map
to the generic architecture presented in section 2. In section 4, we discuss specific M2M design
issues that need to be taken into consideration for future M2M-based vehicular networks.
Section 5 presents some M2M challenges that still need to be addressed to enable vehicular
networks to reap the benefits of M2M support. We make some concluding remarks in section
6.

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 3
2. A Generic M2M Architecture and M2M Application Areas
Gateway
Gateway
Gateway
Gateway
Internet
Aggregators
Gateways
Backhaul
fully distributed
subnet
client-server subnet
cooperative
subnet
Gateway coverage area
Gateway
boundary
Gateway
boundary
Data collectors
Subnets
Figure 1.Generic M2M communication architecture.
A generic M2M communication architecture is shown in Figure 1. To support the high number
of M2M devices (billions to trillions) that are expected to be part of an M2M network,
hierarchical communication architectures have been proposed [4]. At the highest level, the
goal of M2M architectures is to aggregate information from data collectors, and to apply some
decision-making function to this information to produce decisions which are then executed.
Several data collectors (e.g. temperature sensors, location sensors or heart rate monitors) are
used to collect information from multiple locations. Data collectors gather data in areas that
are small compared to the total area covered by the M2M architecture. The data collectors are
usually separated in physical space and can collect information from various types of sources.
For example, one data collector could record the ambient temperature in one location, and
another could record the current consumed by a television set in another distant physical
location.
In M2M architectures, data collectors of the same type are connected to small networks (e.g.
Body Area Networks (BANs), Zigbee, and Bluetooth), called subnets. Each subnet uses a
network technology appropriate for the type of information to be collected and distributed.

The network technology which is employed determines the subnet architecture. Three types of
subnets (fully distributed, client-server and cooperative) are shown in Figure 1. In a fully
distributed network, all nodes (e.g. various computers on a home Wi-Fi network) are
connected as peers and share data amongst themselves. One of the nodes (e.g. a router’s Wi-Fi
module) acts as a super-peer that has the ability to connect through some gateway (e.g. a
router’s ADSL connection) to the Internet. In a client-server network, all clients only
communicate with the server; an example would be portable media players connecting to a
media server. The server then relays appropriate information to other clients (e.g. wireless
speakers). The server also has the ability to connect to the Internet through some gateway (e.g.
a home router). A cooperative network is not, strictly speaking, a subnet. None of the nodes
(e.g. BAN sensors) communicate directly with each other as is the case on a subnet, but rather
via the gateway (e.g. a cellular phone).
The collected data is then aggregated at possibly multiple layers of aggregation points. The
number of aggregation layers depends on the expected number of M2M devices and how these
devices are logically grouped. At each aggregation layer, data from multiple data collectors
can be aggregated by applying some intelligence to the data. This means that the aggregation
function is not merely assembling the data, but it can also reduce the amount of data
retransmitted. This can be achieved, for example, through filtering data based on relevance, or
by extracting higher-level information from aggregated data. Data aggregation is used to allow
M2M devices to have low cost, consume little power and have a limited operating area. This is
required to enable a system of billions to trillions of these devices.
Multiple local subnets using different communication standards can communicate with each
other using some gateway which provides Internet connectivity or a similar backhaul network.
The gateway typically interfaces with at least one device on each subnet to which it is
connected, and is also connected to other gateways. An end-user can connect to a server to
access information collected from the M2M data collectors. This server may be connected to
the Internet or some other backhaul network. The function of the M2M server is to perform
final processing tasks on the collected data, to store the data, log the transactions that occurred
and to make the data available online to the various users of the M2M system.
M2M technology has been deployed in various application areas recently. These include smart
power grids, home networking, health care, and vehicular networking. Below, we briefly
describe how M2M principles are used to support the selected areas (as depicted in Figure 2
and Figure 3).

Figure 2 M2M networking applied in smart grid, home networking, and health care.
2.1. Smart Grid
The smart grid is defined as an electrical grid which is specifically designed to improve the
efficiency of power transmission, enhance the quality of service to utility users, and to reduce
the economic and environmental cost of power generation, distribution and consumption. The
smart grid is a combination of a power network and an information network. The distributed
nature of an electrical grid, the sensors required to aggregate usage information, and the
various tiers of decision making (from home energy management to power generation
scheduling) makes M2M principles particularly applicable to the smart grid. For this reason,
the smart grid is considered one of the strongest driving forces for M2M communications [1].
In the smart grid, all devices in every home monitor their power consumption, aggregate the
data and send it to the servers of the power utility. Multiple aggregators exist in the smart grid.
One aggregator is present in each home to aggregate home device data and to optimize local
electrical power consumption. Neighborhood aggregators can also exist to aggregate data
from multiple homes. After neighborhood aggregators send their collected data to the power
utility servers, the power utility company knows how much and when power is consumed by
each house and which devices are consuming the power. This allows the utility company to
preemptively increase power production to meet transient power requirements, turn off
devices that are putting too much strain on the network, monitor the health of all devices in the
power network and to improve billing for power usage [7].

2.2. Home Networking
The main purpose of home networking is media distribution, but home networking can also
include elements of the smart grid as described earlier. Media distribution systems include
media storage (media server), media transportation (Wi-Fi, Bluetooth, Ultra-WideBand
(UWB) and media consumption (High Definition TeleVision (HDTV), smart phones, tablet
computers, desktop computers). Home networking is currently receiving significant attention
as an M2M network [4]. A home network is composed of various smaller home device
sub-networks. Each sub-network can contain an aggregator that in turn connects to the Internet
gateway (router). Examples of such sub-networks are Zigbee sub-networks (electrical
appliances, air conditioner), Wi-Fi sub-networks (laptop, printer, and media server), UWB
sub-networks (HDTV, camcorder), smart grid sub-networks (smart meters, smart thermostat,
smart switch), body area sub-network (smart phone, monitoring instrument, body sensors) and
Bluetooth sub-network (music center, portable audio player) [9]. Possible aggregators include
a cellular phone for the BAN subnet and power meters for the smart grid subnet.
Devices exist in the home that can be connected to the Internet to provide extra services to
consumers. One example where the M2M paradigm might be employed is where a fridge in a
home forms part of an M2M network. The fridge is able to collect data about the number and
state of items that it contains, for example the number of eggs that remain and the amount of
milk a container has. Many fridges can then be connected, via the Internet and their respective
home routers, to report on stock numbers and states. The reporting can be done to a grocery
store chain, which can run a dispatch chain that will replenish food items in all the houses that
it oversees.
2.3. Health Care
Health care M2M networks are sub-networks within home networks. They are used to monitor
peoples’ health and inform those being monitored, as well as possibly their doctors, of any
abnormal conditions that might occur. Data collectors in a health care network are body
sensors to monitor various measures of good health, including blood pressure, temperature,
heart rate, and cholesterol. Body sensors are connected to an on-person gateway, such as a
smart phone, which also acts as the aggregator for all data collectors. Sensors send data to the
smart phone which sends data over the Internet to health monitoring servers. Applications run
on the servers that monitor the health of patients.
The M2M paradigm allows for the health of an entire population to be monitored in real time.
Ambulances can be immediately dispatched to accident scenes and patients can be monitored
at their homes just as effectively as in hospitals. A patient’s doctor can also immediately be
informed if her patient suffers a heart attack, for example. Health care M2M can also help to
track the progression of a virus outbreak by monitoring specific symptoms of the population.
Patients that are suspected of being infected by the epidemic can be notified to seek medical
care.
2.4. Vehicular Networks
Several research efforts have been investigating M2M communications support for vehicular
networks [10] [11] [12]. In a vehicular network, vehicles communicate with other vehicles

(Vehicle-to-Vehicle (V2V)) or with an infrastructure (Vehicle-to-Infrastructure (V2I)).
Applications for vehicular networks can be divided into four broad categories, namely safety
and collision avoidance, traffic infrastructure management, vehicle telematics, and
entertainment services and Internet connectivity [13].
For the most part, previous vehicular network research has focused on point-to-point
communications or support for a limited set of vehicles. From a network architecture point of
view, this focus constitutes a bottom-up approach, where focus is initially placed on
developing routing protocols, PHYsical layer (PHY), Medium Access Control (MAC) layer,
and broadcasting [13]. M2M networks use centralized and autonomous monitoring, and
control of data-collecting devices that are spatially distributed, using a hierarchical top-down
approach. The M2M paradigm can therefore improve vehicular networks' capacity to support
features such as cross-platform networking, autonomous monitoring and control, and
visualization of the devices and information in the M2M network. Given the potential that
M2M holds for vehicular networks, we focus on M2M communications for vehicular
networks in this work.
To explain how vehicular networks are used, and to better understand the benefits of M2M
vehicular networks, four application areas for vehicular networking are presented below.
2.4.1. Safety and Collision Avoidance
When driving a non-networked vehicle, a driver makes decisions based only on information
within the driver’s Line of Sight (LoS). One of the fundamental reasons for using wireless
M2M communication between vehicles is to avoid accidents by overcoming the LoS
limitation [13]. In the event of an emergency, information from emergency-detecting sensors
(such as accelerometers and the braking system) is sent to other vehicles and to the road-side
communication infrastructure within its communication range. To avoid an accident or further
accidents, emergencies need to be detected and warning notifications need to be delivered fast
enough to allow the driver to respond. Since this message transmission needs to occur in as
little as tens of milliseconds, a Quality of Service (QoS) guarantee and low latency is
paramount for the communication between the notifying vehicle and the multiple receiving
vehicles [13]. Since the messages are only a few bytes, convey only the most critical
information [14], and are only sent during emergencies, the required bandwidth is low and
continuous connections are optional.
2.4.2. Traffic and Infrastructure Management
Road congestion is a common problem faced by many drivers around the world every day. The
number of vehicles on many countries' roads has quickly outgrown the ageing infrastructures.
The impact of congestion is not limited to personal discomfort. Congestion leads to increased
fuel consumption, which in turn increases costs, and increased emissions, which leads to an
increase in pollution [15]. Conversely, a better managed and tightly controlled infrastructure
improves productivity and reduces costs and pollution. This provides a strong incentive for
society to use road infrastructures more efficiently.
Vehicular networking aims to address this need by providing bidirectional M2M connections
between vehicles and the infrastructure that conveys traffic information [11]. Four
applications that could be based on traffic information are pay-as-you-drive tolling, real-time

management of the transport infrastructure, law enforcement, and planning of transportation
infrastructure. The positional information required to enable these applications does not
require sample rates of more than once every few seconds due to the slow rate of change for
typical driving [16]. Since only time, coordinates, speed, and car identification need to be
captured, only a few bytes are required per sample.
2.4.3. Vehicle Telematics
Vehicle telematics is used to monitor and control vehicles remotely, for example to track or
manage vehicle fleets, or to recover stolen vehicles. Physical properties of the vehicle are
recorded and transmitted to a central coordinating agent. These properties include Global
Positioning System (GPS) information, internal engine parameters such as engine
temperature, or video captured by an externally mounted camera. Bandwidth requirements are
not high when video capture is not required, and second-generation cellular data services (e.g.
General Packet Radio Service (GPRS) and Short Messaging Service (SMS)) are sufficient for
simple tracking applications. There is, however, a growing trend to support video telematics.
Preliminary studies show that image capture and video streaming is possible from a vehicle
with access to a high-bandwidth wireless technology such as WiMAX or Wi-Fi [17].
2.4.4. In-car Entertainment and Internet Services
The ubiquity of wireless area networks (e.g. Wi-Fi), cellular broadband networks (e.g.
3rd
-Generation (3G) networks) and metropolitan area networks (e.g. WiMAX) makes it
possible to reliably deliver online content and entertainment to vehicles. The wireless network
requirements are dependent on the application, which can broadly be categorized as:
 static low-bandwidth (e.g. text-based e-mail, news, RSS feeds, social media);
 static high-bandwidth (e.g. Google Street View, Torrent-based file sharing); and
 streaming video and audio, including videoconferencing (e.g. Skype).
Static content is less susceptible to bandwidth fluctuations and in some cases can be tolerant of
substantial transfer delays. Streaming content requires steady high bandwidth with quick
handover and a large receiving buffer, but the occasional loss of packets is acceptable.

3. M2M Communication Layers in Vehicular Networks
Figure 3 The M2M network architecture in a vehicular context.
Figure 3 shows the M2M network architecture for a vehicular network. Each component in
Figure 3 maps to a generic component described in Figure 1.
In a vehicular network, the instruments and sensors in the car are the data collectors. Examples
of data collectors are the airbag controller system and the Automatic Braking System (ABS).
Communications between instruments in a car is designated as intra-car communications and
generally occurs on a wired Controller Area Network (CAN) or fiber-optic Media Oriented
Systems Transport (MOST) ring [18]. Non-entertainment components are hosted on the CAN
bus and entertainment components are hosted on the MOST ring. An On-Board Unit (OBU)
acts as the data aggregator and is connected to the CAN bus. The MOST ring contains a bus
master that acts as a media data aggregator.
OBUs of different vehicles connect wirelessly to each other (inter-car) and to RSUs (extra-car)
using the Wireless Access in the Vehicular Environment (WAVE) standard, which is the de
facto standard for OBU and RSU communications for emergency and traffic management
information [19] [20]. The MOST ring master allows media components in the ring to connect
to available infrastructure though high-bandwidth wireless technologies (extra-car), such as
cellular, WiMAX, and Wi-Fi, to provide video, audio and other entertainment and Internet

connectivity.
The access points of these high-bandwidth wireless technologies and the RSUs act as
gateways to the Internet and to an Intelligent Transportation System (ITS) network as
backhaul services. ITS networks endeavor to put in place intelligent and cooperative
transportation infrastructures that include electronic tolling and traffic monitoring using
road-based sensors [21]. The ITS network and the Internet allow all vehicles connected to a
gateway to communicate with back-end servers. Although ITS in its broadest sense refers to
any communications infrastructure support to transportation networks, this is another use case
where the added benefit of applied M2M principles is clear.
Servers and databases connected to the Internet allow end-users to remotely access various
services of the vehicular network. The transportation authority or other third party service
providers can provide centralized management of the infrastructure and vehicles from servers
and databases connected to the ITS network. ITS information can also be uploaded to the
Internet to allow wide-spread accessibility for end-users.
Table 1 summarizes the communication areas shown in Figure 3, along with the most popular
standards that are currently used in those areas.
Table 1: Popular technologies in vehicular networks
Area Standard Description Ref.
Intra-car(wired)
CAN The Controller Area Network (CAN) connects the various electronic
components of a car, including the display cluster, electronic control
unit, accident sensors, GPS and OBU over an electronic wired network.
CAN supports high speed transmissions of small amounts of data,
which makes it ideally suited for safety applications.
[22]
[23]
MOST A Media Oriented Systems Transport (MOST) ring is used to connect
intra-car media devices. A MOST ring has a high communications cost
per node, compared to CAN, but can transmit at higher speeds than
CAN (25Mbps compared to 125Kbps).
[24]
V2VandV2I(wireless)
WAVE The WAVE standard contains a suite of protocols that make up the
WAVE communications protocol stack. The WAVE protocol suite
contains PHY and MAC layer standards as well as higher level support
for IPv6 and the WAVE short message protocol, designed for low-
latency transmission of short messages. There are, however, some areas
where WAVE is not ideal for V2V communications, such as a short
coverage range, the lack of high mobility support, and the lack of
support for extreme multipath effects.
[19]
[20]
[25]
[26]
[27]
Wi-Fi Wi-Fi or IEEE 802.11 is a wireless communications standard that
covers a relatively large area (250 m outdoors and 70 m indoors) and is
able to transmit at high speeds (54 Mbps for 802.11g to 600 Mbps for
802.11n).
[28]
[29]
[30]
WiMAX WiMAX (IEEE 802.16) covers a much larger area than Wi-Fi (6.5 to 16
km), but at a reduced speed (128 Mbps down and 56 Mbps up). The
IEEE 802.16e standard also adds mobility support to WiMAX. The
mobility support, high speed and long range makes WiMAX well suited
[31]

for vehicular communications. A disadvantage of WiMAX is that it is
expensive to deploy.
Cellular Cellular data (all 3GPP standards) includes GPRS, Enhanced Data for
Global Evolution (EDGE), 3G, High Speed Packet Access (HSPA),
Long Term Evolution (LTE) and LTE-Advanced. All these standards
have been designed with high mobility and large coverage areas in
mind. The latest cellular data standards offer high bandwidth
communication capabilities at low latencies. This makes cellular data
well suited for media distribution applications. Efforts also exist to add
functionality to the 3G standard to allow for ad-hoc operation, which
will allow for V2V communications.
[32]
4. M2M Support for Vehicular Networking Environments
Vehicular networks and M2M communications are complementary growing fields of research
that have developed independently. However, recent technological trends demonstrate that
vehicular communications could significantly benefit from the progress made in M2M
communications [16]. The focus of vehicular network research is on the lower layers in the
communication stack of point-to-point communications. M2M research focuses on centralized
and autonomous monitoring, with a spatially-distributed multitude of devices in a hierarchical
network [33] [34] [35] [36] [37].
The working group on communication patterns of the Dagstuhl Seminar recently presented a
report evaluating future trends in vehicular communication patterns [38]. This report
recognized that vehicular networks are a part of the emerging field of M2M communications
research. The group suggested that existing vehicular technologies, such as IEEE 802.11p
could also be adapted for the purpose of M2M communications.
In this section we review recent research efforts which have focused on leveraging M2M
support to improve communications and information transfer for vehicular networking. A few
of the goals of these recent projects are listed in Table 2 to provide some context of the works
discussed in the rest of this paper.
Table 2 Literature relevant to M2M and vehicular networking.
Goals of the project Ref
Greenwood et al. applied an M2M approach to develop a method for real-time road
transportation optimization. The goal of the research is to improve transportation time
and transportation costs in real-time.
[16]
Kapsalis et al. described an M2M networking platform designed to support real-time
monitoring and autonomous control of a vehicle fleet used in the transportation of
flammable and dangerous goods.
[10]
Lequerica et al. extended the features introduced by M2M to enable social networking
services and media distribution with vehicles as the network nodes.
[11]
In the project from [39], not limited to vehicles, the goal is to provide a complete M2M
asset management system through highly visual and embeddable middleware called
[39]

SMART.
Cha et al. explored ways to ensure trust and security in a vehicular M2M environment. [12]
Galetić et al. described a generic M2M architecture and presented examples of
different M2M architectures. Amongst these examples, the Ericsson CoCar project is
presented.
[37]
4.1. M2M Support for Diversity and Multitude of Devices
Due to the number of vehicles on the road (255 million registered vehicles in the USA in 2011
[40]), and the variety of devices present in each, a vehicular network will have to be able to
sustain a large number of devices. Moreover, the vehicular network is not fixed in size; it
needs to be scalable to adapt to the continuously changing network topology due to the nodes'
high mobility [13]. The variety of communications and sensing devices further compounds the
challenge for vehicular networks. Since the challenges of multitude and variety of devices and
communication standards are a common problem in M2M networks [37], other authors [16]
[10] [11] have proposed M2M solutions for vehicular communications. M2M supports
diversity and multitude of devices [35] [36] [33] in a vehicular network at the Intra-car level of
the network architecture illustrated in Figure 3.
In [16], the problem of multiple devices is addressed with a solution from the M2M paradigm,
by using the On-Board Units (OBUs) to aggregate, consolidate and filter measurements from
the multiple sensors in a vehicle, including the toll collection units, tachographs, on-board
management units, and load-status sensors. The consolidated information is stored in the OBU
to be transmitted only when required. The approach effectively reduces the number of devices
in the M2M network by substituting the devices in the vehicle with one representative OBU
aggregator, which consolidates information from the devices it represents in the vehicular
M2M hierarchy.
To address the challenges associated with heterogeneity and the large number of end-user
devices and communication buses on which these units reside, the vehicular M2M in [10]
connects to the devices through an Object Linking and Embedding (OLE) for Process Control
(OPC) server that provides a generic Application Programming Interface (API) to all
components. The server is set up to use an extended set of generic Input and Output (IO)
channels that are made available for end-user devices. The approach in [10] therefore ensures
a generic access method for different devices in the vehicular M2M network, and also
simplifies management of a large number of devices through the dedicated server.
To overcome the problems associated with the heterogeneity of communications and sensing
devices, to keep devices simple, and to keep device power consumption low, the authors of
[11] re-allocate as much as possible of the intelligence traditionally found in the device itself,
to higher tiers of the vehicular M2M network. Moreover, applications are executed as remote
services on the M2M server, rather than on the device. Software modules are developed to
provide standard APIs to the devices, which further support diverse device types. This
M2M-based approach facilitates easy software porting between devices, simplifies
management across the range of devices, and enables the authors to use a single
communication stack to access all devices in the same way.

4.2. M2M Support for Autonomy and System Management
The vehicular networking applications considered in this paper, namely safety and collision
avoidance, traffic infrastructure management, and vehicle telematics, require autonomous data
collection, aggregation of collected data, transmission and distribution of aggregated data, and
storing and reporting of information. Although autonomy is fundamental to vehicular
networking applications (for example, it is essential for collision avoidance), autonomy has
received little attention in the literature on vehicular networking. System autonomy is one of
the distinguishing features of an M2M network [33], and is important in several areas of the
vehicular M2M architecture illustrated in Figure 3, including the centralized management
system, connectivity, security, and sensing devices. We now investigate how system
autonomy can be achieved by exploring recent vehicular M2M networking literature.
The system in [16] applies M2M principles to a vehicular network by implementing
rules-based autonomy on all layers of the architecture shown in Figure 3. To autonomously
select between available communication connections (cellular, WiMAX or WAVE), the
vehicles (nodes) follow a rule-based system that uses information such as the vehicle's
location, connection availability and signal strength, transmission cost, and the priority and
size of the task to execute (e.g. whether new firmware must be downloaded, or whether GPS
location information must be transmitted). The authors hypothesize that this approach could
reduce communications costs for vehicles in the network. A similar rule-based Event Control
Action (ECA) mechanism is introduced in [10] to support autonomous control of vehicle
routes and early detection of critical situations in a vehicular M2M network. These
configurable rules are deployed in either the centralized control center or in the vehicles, or in
both, to provide adaptable autonomy.
In the vehicle-based M2M network described in [11], social networking information is
generated by drivers and other information is automatically logged by the sensing devices in
the vehicles. To support autonomous data acquisition and processing by the management
software, the vehicles are polled with a data query and the information is stored in the
centralized database. The system operator configures reports (traffic flow, road loading, etc.)
to be automatically generated periodically. Anomalies (accidents, breakdowns, etc.) are
defined by the operator as events, which are automatically flagged, and, more importantly,
autonomously distributed to and shared with other vehicles.
4.3. M2M Support for Visualization and Reporting
In a large and complex system which is managed centrally, such as a vehicular network, it is
beneficial to simplify visual representation of the entities, make device control easy, and to
automate reporting. We describe below how the M2M networking concept supports
visualization and reporting for vehicular communications at the Servers and Consumers level
of the network architecture illustrated in Figure 3.
To improve the interpretation and visualization of measurements obtained from the wide range
of sensing devices and nodes in the vehicular M2M network discussed in [10], a generic visual
presentation is used for all devices. This presentation is created with a representative graphical
visualization of the vehicle-based sensing devices (e.g. speedometer, OBU, etc.). A
user-configurable model is constructed for every type of entity in the vehicular M2M network.
For example, a model could be built for a generic airbag system and another model for a

generic wheel pressure gauge. To provide easy interpretation of information, the authors of
[39] propose highly visual data models with configurable dials and controls built in. For
example, a generic model for a location device (e.g. a GPS navigation device) is built to
indicate where the node is on a map, and to plot its speed. When speed control is required for
fleet management, this generic model can then be configured for individual vehicles to also
plot the speed limit, to report when this limit is exceeded and to control actuators (e.g. an
alarm) on the vehicle. The software is built on Google's OpenSocial Applications
Programming Interface (API), which can be embedded into a web page to provide ubiquitous
access [41] to the vehicle-based devices in the M2M network. Similar approaches are
presented in [42] and [43]; however, these works do not provide APIs that can be embedded
into a web page, and their visualization tools are not user-configurable, but predefined.
4.4. M2M Support for Cross-platform Networking and Connectivity
Vehicles are mobile by definition and the positional distribution of vehicles is variable.
Wireless communications, which is central to vehicular communications, has limited coverage,
and a variety of wireless technologies may be required to interoperate. Connectivity, of
vehicles to infrastructure and to other vehicles, and cross-platform networking are therefore
challenges for vehicular networking environments. A fundamental aspect of M2M
communications is the underlying network it uses, and the associated connectivity of remote
devices to this network. M2M supports the cross-platform networking and connectivity for
vehicular communication at the inter-car and extra-car levels of the network architecture
illustrated in Figure 3.
To support virtually ubiquitous backhaul connectivity and location information in a vehicular
M2M network, the vehicles described in [10] are equipped with GPRS cellular modems and
GPS devices. The CoCar project referenced in [37] investigates the feasibility of using only
existing 3rd-Generation (3G) cellular technology in an M2M network, to enable both
vehicle-to-vehicle and vehicle-to-infrastructure networks. The project envisions a
fully-connected vehicular network, where all vehicles are connected via road infrastructures to
support safety and media distribution applications. Cellular communication (HSPA) is also
used by SMART in [39]. SMART provides a network-agnostic interface to support devices
(without connectivity to the Internet) on any cellular network. To do this, cellular providers
were co-opted to make an access point available and host services for the project. The mobile
service providers were willing to do this because of the potential data revenue. Coverage of
cellular networks is near ubiquitous for the transportation infrastructure, which makes it suited
for connectivity in a vehicular M2M network with a distributed multitude of devices.
In [16], an M2M network is used for remote navigation and vehicle-routing by using various
mobile-technology modems that include WAVE, Wi-Fi, cellular connections and satellite
services. The same connections are used to disseminate routing instructions back to the
vehicles. Selection of the appropriate connection is based on the vehicles' location, the local
connection availability and signal strength, transmission cost, and the task to be executed.
4.5. M2M Support for Security and Privacy
Many applications envisaged for vehicular networks require secure and private
communications [12]. In collision-avoidance applications, for example, data tampering could

be fatal, while privacy is important when considering vehicle tracking and electronic tolling.
Security in a VANET can be compromised by four types of attacks: unauthorized access,
malicious modification, denial of service, and repudiation [45]. Several conventional
approaches have been proposed to protect VANETs against these security attacks; a detailed
analysis of the threats and proposed solutions can be found in [46]. The conventional
centralized network security model is too rigid and is therefore unsuitable for vehicular M2M
architectures. Decentralized and scalable security models are therefore required in a vehicular
network, where the components are dispersed and often unguarded [12]. We explain how the
M2M concept supports security and privacy for vehicular communications at all levels of the
network architecture illustrated in Figure 3. M2M support for security in VANET stems from
the strong architectural definition of M2M networks.
In [11], third-party software, called Identity Provider (IdP), runs on the centralized server of
the vehicular M2M network. IdP is used to obscure the identity of the drivers in shared
information (e.g. location information) and to hide secure information (e.g. bank details) from
other users. The software works by requiring every node to sign in to IdP from a remote
location in the vehicular network in order to be authenticated. After authentication IdP
registers the identity information on a Drive and Share (DaS) server. After the identity
registration the vehicle sends encrypted information to the DaS via the M2M network without
compromising its own identity.
The authors of [12] evaluate autonomous, remote and semi-autonomous validation and
security enforcement in a vehicular M2M framework. Autonomous validation is beneficial
because of the ability to establish assurance in a device independent of the network, thus
overcoming the problem of unreliable connectivity. The drawback of autonomous validation
is the need for the M2M network to keep track of the state of every device, and always keep the
security components of the whole network synchronized. Remote validation overcomes the
requirement of state awareness, but makes security dependent on network coverage. The
vehicular network described in [12], uses semi-autonomous validation. This approach
balances device-centric trust and traditional security enforcement by relegating key security
functions to the individual vehicles. The vehicular network described in [12] uses an M2M
approach to ensure a secure system, based on the concept of a trust boundary. To implement a
distributed trust system, every device on the vehicle is equipped with a logically separate
entity called the TRust Environment (TRE). The TRE hides sensitive data and software (e.g.
encryption functions and keys, TRE identification, security policy functions etc.) from
untrusted access. The TRE forms a trust boundary upon a secure boot and is able to
communicate this trust to other devices and expand the trust boundary to other devices in the
vehicle, and to other vehicles using validation of expected TRE states. Hardware security
measures are centered on a Root of Trust (RoT) set of fundamentally trusted functions, which
are used to protect the trust anchor and to prevent tampering with it. The RoT secures internal
operations of its device and is able to convey sensitive information to external entities. A
configurable set of security functions is kept centrally in the M2M network.
5. M2M Challenges and Issues for Vehicular Networking
In this section we discuss some of the remaining challenges that need to be addressed to enable
the deployment of M2M-based vehicular networks.

5.1. Diversity and Multitude of Devices
Although progress has been made to overcome problems associated with the broad range of
devices in a vehicular M2M network, Lequerica et al. report that more work needs to be done
to adequately address technical limitations and cost ineffectiveness of the existing solutions
[11]. This concern relates to the rapid rate of change of consumer electronics that must be
integrated into vehicular networks, such as smart phones, tablets, and navigation devices.
5.2. Autonomy, System Management, and Applications
Autonomy is required at several layers of the M2M architecture, namely centralized
management systems, dynamic connectivity, security, and sensing devices. However,
substantial research is still needed to apply M2M autonomy in vehicular networks [10] [16].
For example, Greenwood et al. [16] state that legacy sensing-device and OBU technology
have limited processing capability. This limits such devices’ ability to be used as autonomous
data sources and aggregators in a vehicular M2M networks. They recommend that further
research should be undertaken to make vehicular M2M systems more autonomous by
including presence-announcing and auto-negotiating features on the OBUs and combining this
with RFID technology [16].
Another anticipated M2M-related contribution to vehicular networks, is the use of centralized
intelligence to more efficiently share information in the network. The usefulness of
inter-vehicular communication is hamstrung by the apparent random behavior of neighboring
nodes. A central agent which is in contact with all vehicles, and aware of their locations and
headings, could use knowledge of data supply and demand to improve the coordination and
management of information transfer among nodes. For example, a centralized agent could use
a cellular or WiMAX network to orchestrate inter-vehicle information sharing, which in turn
uses Wi-Fi or WAVE technologies.
5.3. Security and Privacy
Innovative solutions are needed to ensure the privacy of drivers and passengers in a vehicular
M2M network, especially in applications where vehicles are tracked or driver behavior is
monitored (e.g. for fleet management and traffic reporting). Cha et al. [12] highlight other
security challenges that relate to the method employed to validate the state of the TRE during
autonomous node validation. For example, when using autonomous node validation, the
labeling of rogue devices is impossible and remote management is difficult. Also, the network
needs to be aware of the maintenance state of every device, which is not practically feasible
[12]. Further work needs to be done to address these shortcomings [12].
In [11], Lequerica et al. note that more work needs to be done to address privacy concerns.
Users need to be able to set levels of anonymity for different types of shared information.
Security in general is also highlighted as a remaining challenge in [10].
6. Conclusion
In this paper we described concepts key to the development of M2M communications systems.
We gave a brief overview of four prominent M2M applications, namely health care, home

networking, smart grid, and vehicular networking. We then focused on vehicular networking,
introducing an M2M perspective on vehicular communications, and highlighted the specific
applications, requirements and protocols relating to M2M-based vehicular networks. To
establish where M2M communications could add value to vehicular networks, we provided a
thorough survey of research and development projects that intersect both technologies. Five
areas were identified: Multiplicity, Autonomy, Connectivity, Visualization and Security.
Finally, we performed an analysis of the remaining challenges faced by vehicular M2M
systems. The most significant challenge is the standardization of communication interfaces in
a network with exceedingly high mobility and variability of components. Managing security
and privacy in such a dynamic network requires further attention.
7. Acknowledgements
We thank the anonymous reviewers for their constructive feedback which greatly helped us to
improve the quality and presentation of this paper. This research was partially funded by MIH
Holdings through the Media Lab at Stellenbosch University. Sherali Zeadally was partly
funded by a District of Columbia NASA Space grant.
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Gert-Jan van Rooyen is Senior Lecturer in Telecommunications
at Stellenbosch University, and holds a PhD in Electronic
Engineering from the same institution. He is the director of the
MIH Media Lab, an interdisciplinary research laboratory that
focuses on the broad application of emerging communications
media. His research interests include software radio, digital signal
processing and vehicular networks.
John S Gilmore obtained his Bachelor's and Master's degrees in
Electrical & Electronic Engineering with Computer Science from
Stellenbosch University in 2007 and 2010 respectively. He is
currently pursuing his PhD at the MIH Media Lab at Stellenbosch
University. His research interests include peer-to-peer massively
multiuser virtual environments, computer networks, distributed
systems and communication protocols.
Sherali Zeadally is an Associate Professor in the Department of
Computer Science and Information Technology at the University
of the District of Columbia, Washington DC, USA. He currently
serves on the editorial boards of over 15 peer-reviewed scholarly
journals. He has Guest-edited over 15 special issues of refereed
scholarly journals. He is a Fellow of the British Computer Society
(FBCS) and a Fellow of the Institution of Engineering Technology
(FIET).
Marthinus J Booysen is currently pursuing a PhD at the MIH
Media Lab, and is a Lecturer at the Electrical & Electronic
Engineering Department at Stellenbosch University, South Africa.
He is a Member of the Institution of Engineering Technology
(MIET) and a Chartered Engineer (CEng) at the Engineering
Council, UK. He has ten years’ experience in the aerospace and
automotive industries with companies that include SunSpace,
Rolls-Royce, Boeing, BMW, and Jaguar Land Rover.

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