#  Towards A Simple and Efficient VDTN Routing Protocol for Data Collection in
Smart Cities

Ngurah Indra Er∗ab, Kamal Deep Singhde, Christophe Couturierac, Jean-Marie
Bonninac
aIRISA / IMT-Atlantique, Rennes, France
bDepartment of Electrical and Computer Engineering, Udayana University, Bali,
Indonesia
cInria, Rennes, France
dLaboratoire Hubert Curien, Saint-Etienne, France
eUniversite Jean Monnet, Saint-Etienne, France

###### Abstract

Smart cities today can utilize Vehicular Delay Tolerant Networks (VDTN) to
collect data from connected-objects in the environment for various delay-
tolerant applications. They can take advantage of the available Intelligent
Transportation Systems (ITS) infrastructures to deliver data to the central
server. The system can also exploit multiple and diverse mobility patterns
found in cities, such as privately owned cars, taxis, public buses, and trams,
along with their Vehicle-to-Everything (V2X) communications capabilities. In
the envisioned convergence between the ITS and V2X, we believe that a simple
and efficient routing protocol can be deployed for the delay-tolerant data
delivery, contrary to the implementation of optimized solutions that might be
resource-demanding and difficult to standardize. In this paper, we analyzed
the performances of four baseline VDTN routing protocols, namely: Direct
Delivery, First Contact, Epidemic, and Spray & Wait, to understand their
strengths and weaknesses. Our simulation results highlighted the trade-off
between distinct approaches used by those protocols and pointed out some gaps
that can be refined. This study provides new interesting ideas and arguments
towards developing a simple, efficient, and high-performing routing protocol
for data collection in smart cities.

###### keywords:

Smart City , Data Collection , VDTN , ITS , V2X , Routing Protocol

##  1 Introduction

The current and future smart cities will require data collection solutions
from various connected-objects in their area. In turn, the new paradigm will
help cities manage their natural resources intelligently, safeguard
sustainable socio-economic development, and ultimately raise quality-of-life.
The data collection’s primary trend is to connect all sensors to a long-range
operated network such as the cellular network and the Low-Power Wide Area
Network (LPWAN). But as the number of sensors and the amount of data generated
grows exponentially, the utilization of a cellular network to collect all
kinds of data might not be economically feasible. At the same time, the use of
LPWAN might be impeded by insufficient bandwidth. Therefore, it is desirable
to keep such networks for only collecting data that has delay constraints,
whereas other economical solutions should be deployed to collect delay-
tolerant data.

Fig. 1: The VDTN-Based Data Collection Scheme in Smart Cities

Simultaneously, advances in vehicular technology have enabled the vehicle-to-
vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything
(V2X) communications to connected-objects in their surroundings. Vehicles with
current and future radio access networks will also be equipped to reach the
Internet. Moreover, the regulatory body will soon mandate the installation of
such communication capabilities in vehicles for safety-related applications
[1]. The cutting-edge trend is widely expected to elevate vehicles’ active
role in the smart cities’ ecosystem.

In terms of networking, Delay Tolerant Networks (DTN) enable communication
even when there are connectivity issues. Such issues could be infrequent and
intermittent connectivity, long and variable latency, high error rates,
extremely asymmetric data rate, and the absence of end-to-end connectivity
[2]. The extension of Vehicular Ad hoc Networks (VANET) with DTN capabilities
naturally leads to the concept of Vehicular Delay Tolerant Networks (VDTN),
where the network of vehicles will be able to cope with the inherent
intermittent connectivity during data delivery.

The Intelligent Transportation Systems (ITS), on the other hand, are advanced
applications dedicated to delivering innovative services related to different
modes of transport and traffic management, through vehicular communication, to
improve road safety and comfort for drivers and passengers [3]. Vehicles
equipped with wireless devices can collect data from the environment and share
traffic and road safety information with in-range vehicles, roadside units,
and other connected objects. These functionalities are known as the vehicle-
to-vehicle (V2V), vehicle-to-infrastructure (V2I), and the vehicle-to-
everything (V2X) communications [4]. Concerning the implementation of VDTN in
the ITS environment, we can expect the increasing availability of ITS
infrastructures to assist in the networks’ data exchange. One crucial
infrastructure element is the Road Side Unit (RSU), which can connect to the
Internet. Such units will be installed throughout the city, and they can
function as a Point-of-Presence (PoP) for accessing the Internet and
forwarding data from vehicles to the core network. In another scenario that we
do not consider in this paper, the RSU might not have an internet connection.
However, it can still participate in data forwarding as long as it has some
wireless communication capability.

Fig. 2: The Classical Routing Protocols Setup Comparison

The PoPs will be available in numbers and they will be strategically placed,
such as at traffic lights, road intersections, bus stops, and road lighting
posts. Therefore, there will be several locations in the city where vehicles
can offload the data (which they carry from connected-objects) to such PoPs,
instead of having only one specific destination. Consequently, this particular
setting would need an efficient data routing approach. This makes the routing
mechanism as an integral part of the data collection process, which can
crucially determine the data collection performance. Therefore, routing needs
to be studied extensively to make sure its suitability for the chosen
implementation. Before implementing the VDTN data collection system in the
smart city environment, as depicted in Figure 1, we need to recognize and
analyze the specific nature of the data forwarding and routing process and
assess the performance of some baseline VDTN routing protocols. Their
performances over defined benchmarks can then be examined to develop a better
routing protocol for this specific purpose.

The networking performances of four baseline VDTN routing protocols, namely:
_Direct Delivery_ , _First Contact_ , _Epidemic_ , and _Spray & Wait_ are
studied in this paper. These routing protocols implement the main routing
concepts used in VDTN. We categorized them as ’baseline’ based on the fact
that they use only a few parameters in their data forwarding decision. While
some other optimized routing solutions are available in the literature, the
study of these baseline protocols will give us the main trends. Interestingly,
these four baseline protocols have enough diversity of mechanism to forward
data from the source to the destination, from different numbers of copies of
data that they forward to varying sets of parameters that need to be
considered for making the forwarding decision. These diversities are
illustrated in Figure 2.

Indeed, the performance comparisons between those baseline routing protocols,
partially or in its entirety, have been presented in various works before.
Yet, we aim to study their behavior in a particular use case where the current
trend of the convergence between V2X communications and the Intelligent
Transportation Systems (ITS) allows vehicles to be more involved in the smart
city’s data collection. We believe there are three key differentiations that
necessitate us to compare them inline with our research goals. Therefore, the
main contributions of this paper are as follows.

  1. 1.

Most of the previous works consider the forwarding of data between moving
nodes, e.g., from vehicles to vehicles. In contrast, we compare them for a use
case of the data collection from stationary connected-objects to a central
server in smart cities. In the vision of convergence between V2X
communications and the ITS infrastructures, different intermediary nodes with
varying mobility profiles exist to assist in the forwarding process. In this
scenario, we discover several unique dynamics that show each baseline
protocol’s strengths and weaknesses that we believe none of the previous works
were able to highlight.

  2. 2.

We investigate the performance of baseline routing protocols in unique data
collection settings in smart cities, where multiple ITS infrastructure units
such as RSUs can function as Internet Point-of-Presences. Public transport,
such as public buses with their predetermined mobility, are also involved in
the data collection. The results strengthen our belief that instead of
designing complex routing protocols, we can develop a simple protocol that can
be efficient and high-performing. We believe in assessing the strengths and
weaknesses of those baseline routing protocols as a step towards the new
routing protocol.

  3. 3.

We assess those baseline routing protocols’ performance with respect to an
increasing number of vehicles, representing the progression from a sparse to
denser vehicular network. It is challenging to consider vehicles density in a
complex smart city scenario, which might explain why the critical detail was
commonly absent in the previous works. As our evaluation highlighted, there
are different dynamics in varying vehicular network density, which affect the
key performance indicators. Therefore, our work can give a broader and more
complete view of the trend in performances.

Our study aims to show how such VDTN solutions are efficient for collecting
sensors’ data. To investigate each of the strategy’s potential, we devise
scenarios where a sufficient number of cars are equipped with communications
and networking capabilities. We implement schemes where 37 static wireless
sensors are almost evenly spread out in the city. There are also 5 PoPs where
vehicles can offload the collected data from sensors and forward them to the
central server. We also explore the impact of different mobility patterns on
the networking performance of the data collection system. Two types of
vehicles: cars and buses, are used in the simulation. Cars represent random
vehicle movement, while buses moving along their predetermined route represent
a predictable mobility pattern typically available in smart cities. Ideally,
the routing protocol used in this kind of setting needs to consider these
different mobility types to exploit them for better performances. Due to their
simplicity, the four baseline routing protocols investigated in this study do
not recognize these factors. Yet, it is crucial to understand how they perform
under this unique scenario for future refinement. The Key Performance
Indicators (KPIs) used for comparison are Delivery Probability, Average
Latency, and Overhead Ratio, as commonly used in other previous works [5, 6,
7, 8, 9].

The rest of this paper is organized as follows. Section II provides a brief
history of vehicle-based data collection, an overview of the V2X Intelligent
Transportation Systems (ITS) environment, details on the baseline routing
protocols for VDTN, and the KPIs definitions. Section III discusses some
previous works already conducted within this subject and how our work gives
different contributions. Section IV discusses the performance evaluation,
which includes the simulation setup and analysis of results. Lastly, Section V
provides conclusions and possible future research.

##  2 Background

###  2.1 The Data Mule Concept

In sparse Wireless Sensor Networks (WSN), researchers have been proposing the
use of mobile data collectors to reduce the power consumption of sensors,
while at the same time achieving cost-effective connectivity. Data MULEs
(Mobile Ubiquitous LAN Extensions) are mobile nodes that pick up data in one
place and drop it elsewhere [10]. With the three-tier architecture, such
implementations aim to extend the network coverage and increase communications
opportunities [2]. A simulation study in [11] shows an interesting direction
to improve the energy efficiency of data collection with data mules, and
another work in [12] presents a Geographic Routing/Greedy Forwarding-based
protocol for data mules in DTN.

###  2.2 Car-Based Data Collection

The idea of using vehicles for data collection has been around since the end
of the last century. As reported in the year 2000, a project called ’Roads
towards the Future’ was conducted by the Dutch ministry of transport and
waterworks. The project introduced a field-trial of communication between
vehicles and the infrastructure to exchange what they called the Floating Car
Data (FCD) [13]. The data consist of the GPS-position of a sufficient number
of cars periodically communicated to the central site via GSM links. The
observer can then measures the vehicles’ travel times accurately. The system’s
applications range from real-time data collection for traffic management to
the compilation of very accurate Origin-Destination matrices complete with
travel times. Currently, research towards various utilization of FCD are still
continuing, as reported in [14] and [15], among others.

Another generic in-vehicle data collection is the Probe Data. In [16], ”Probe”
is defined as a vehicle or person equipped with a GPS receiver. They propose
an approach for evaluating the road network from travel time stability and
reliability using probe data from a bus. More recent work in [17] combines
vehicle probe data with machine learning techniques to estimate historical
hourly traffic volumes. Meanwhile, another study in [18] utilizes probe data
for traffic management strategies for special events.

###  2.3 The V2X Communications in The ITS Environment

The term “smart” can be identified for future cities if they will be based on
intelligent transportation solutions that embrace information and
communication technologies [19]. Subsequently, different stakeholders, from
governmental agencies to automotive manufacturers, have shown great interest
in vehicle-to-everything (V2X) communication as part of their development
efforts and deployment strategies for Intelligent Transportation Systems (ITS)
[20]. Moreover, in the vision for smarter cities, vehicles can connect and
exchange information with any devices. Ultimately, users, devices, and
vehicles will form an ecosystem of Cooperative Intelligent Transportation
Systems (C-ITS) [21].

In the following, we elaborate more on each of the vehicle’s communication
capability which relate to the ITS environment.

####  2.3.1 Vehicle-to-Vehicle Communications

V2V technology consists of wireless data communications between motor vehicles
[4], such as cars, taxis, buses, trucks, trams, and even trains. This
communication’s primary purpose is for accident mitigation, providing means
for travelling vehicles to transfer data on their position and speed within an
ad-hoc mesh network.

####  2.3.2 Vehicle-to-Infrastructure Communications

V2I communications allow interfacing between in transit vehicles with the road
systems, such as traffic lights, street lights, lane markers, cameras,
signage, and parking meters. The standard V2I communications are wireless,
bidirectional, and similar to V2V, utilizing Dedicated-Short-Range
Communication (DSRC) frequencies to transfer data [4].

####  2.3.3 Vehicle-to-Everything Communications

V2X extends the V2V and V2I communications described above and represents
generalization, where the data exchange takes place from vehicles to any
entity they can interact with. The vast technology covers other more specific
types of communications, namely Vehicle-to-Pedestrian (V2P), Vehicle-to-
Roadside (V2R), Vehicle-to-Device (V2D), and Vehicle-to-Grid (V2G) [4].

###  2.4 The Baseline Routing Protocols for VDTN

Here, we define some baseline VDTN routing protocols [22, 23, 24, 2, 3, 25]
that are studied in detail later.

####  2.4.1 Direct Delivery Routing Protocol

The Direct Delivery routing is a single copy forwarding approach at its
simplest form, where a node that has the data forward it directly to its
destination. We can use this routing protocol performance as a benchmark that
should be exceeded if more complexities are added to the designed routing
strategy. Due to its simplicity, we can expect minimal network and buffer
usage from this protocol.

####  2.4.2 First Contact Routing Protocol

The First Contact routing deploys a mechanism where a single copy of messages
is opportunistically forwarded from the originating node to the first node it
encounters. This single copy of messages continues to be transmitted to in-
range nodes until one reaches its destination. Nodes erase messages that they
already relayed to another node, consequently preserving only a single copy of
each message in the entire network. This strategy makes the routing protocol
very efficient in occupying buffering spaces.

####  2.4.3 Epidemic Routing Protocol

The Epidemic routing implements a multi-copies forwarding strategy where an
originating node will exchange all messages with another in-range node while
also keeping one copy of each message for the next encounters. Each node
receives messages that they do not already have, with their buffering capacity
as the only limitation. The strategy tries to ensure that at least one copy of
each message will reach its destination in the earliest possible time by
inundating the networks with a maximum number of messages’ copies. As a
benchmark, we can expect this routing as an upper limit in terms of network
and buffer utilization.

####  2.4.4 Spray & Wait Routing Protocol

The Spray & Wait routing imposes a limited multi-copies forwarding mechanism,
where the number _L_ can be assigned to the protocol to specify the upper
limit of the number of copies for a message that can be generated by a node,
as explained in [24]. Furthermore, the routing protocol has two operation’s
modes: _standard_ and _binary_. In binary mode, if a node is the origin of the
data, it will logically hold _L_ copies of the data specified initially (e.g.,
6 is the default value). In standard mode, if a connection is established with
another node that does not have a copy of the data, only a single copy is
forwarded (the _spray_ phase), and _L-1_ copies continue to be held by the
originating node. The originating node then can forward the remaining copies
of the data to each node that it encounters next. The process continues until
the originating node only hold the last copy of the data, when it stop
forwarding copies to another node and only forward the data to its destination
(the _wait_ phase). In binary mode, on the other hand, _L/2_ (rounded up)
copies are forwarded each time the originating node meets another node without
the data copy, while it will keep _L/2_ (rounded down) copies. As in the
standard mode, the forwarding mechanism continues until the originating node
has only a single copy of the data when it goes to the wait phase, which is
basically similar to the direct delivery routing.

###  2.5 The Key Performance Indicators (KPIs)

The performances of DTN, and consecutively VDTN, can be measured by several
Key Performance Indicators (KPIs). In this study, we utilize three commonly
used KPIs in previous studies on this topic [5, 6, 7, 8, 9]. We discuss on
each of them in the following subsections.

####  2.5.1 Delivery Probability

The Delivery Probability (DP), or also referred to as the Delivery Ratio, is
the total number of messages successfully delivered to their destination
divided by the total number of generated messages at the originating nodes as
defined in Equation 1.

$\scriptsize\textrm{DP}=\frac{\textrm{Number of delivered
messages}}{\textrm{Number of generated messages}}$ (1)

The ideal condition is for the maximum delivery probability value of 1, where
all the generated messages are also successfully delivered to the
destinations.

####  2.5.2 Average Latency

Also referred to as the Average Delivery Delay, the Average Latency (AL) is
the average time it takes from the messages’ creation at the sources to the
time they are successfully delivered at the destination, as defined in
Equation 2.

$\scriptsize\begin{split}\textrm{AL = }\frac{\sum_{n=1}^{N}\textrm{Message
arrival time}-\textrm{Message creation time}}{\textrm{\emph{N} delivered
messages}},\text{if }N\geq 1\\\ \emph{cannot be defined},\text{if
}N=0\end{split}$ (2)

The desired condition is for messages to instantaneously reach their
destination, i.e., the average latency of close to zero, particularly for
critical applications. Yet, in the intermittent connectivity of vehicular
networks, the ideal condition of very low latency is almost impossible to
achieve. Therefore, they are more suitable for delay-tolerant applications,
whereas data with an average latency in the order of seconds, minutes, or even
hours, can still be useful. Nevertheless, the goal is to keep the average
latency as low as possible.

####  2.5.3 Overhead Ratio

The Overhead Ratio (OR) shows the ratio of the total number of transmitted
messages in the entire network compared with the total number of delivered
messages, as shown in Equation 3.

$\scriptsize\begin{split}\textrm{OR = }\frac{\textrm{\emph{T} transmitted
messages}-\textrm{\emph{N} delivered messages}}{\textrm{\emph{N} delivered
messages}},\text{if }N\geq 1\\\ \emph{cannot be defined},\text{if
}N=0\end{split}$ (3)

The ideal value for the Overhead Ratio is 0, which happens only when the
source of a message or messages directly deliver them to their receiver. If
intermediary nodes are involved in the delivery, relaying messages through
those intermediaries is considered as excesses or overheads. Hence, relaying
copies of messages that do not eventually reach the destination will also be
counted as overheads, including copies being rejected by the receiver. In most
cases, the receiver only accepts a unique message once, i.e., the first time
it arrives. The parameter was also referred to as the Network Overhead Ratio
in some references because it directly affects the network’s resource usage,
such as energy consumption for processing and communications, as well as
bandwidth allocation. A low Network Overhead Ratio is a vital characteristic
of an efficient and scalable data collection system.

Fig. 3: The Simulation Map

##  3 Previous Works

There are two forefronts of research in data collection for smart cities that
our works explore and aim to contribute. The first forefront is on the type of
wireless networking that the data collection from stationary connected-objects
can utilize, emphasizing its implementation efficiency and scalability,
particularly for delay-tolerant applications. The second forefront is on which
routing protocol to deploy in the VDTN-based data collection system to deliver
the data to the central server, aiming for its efficiency, high-performance,
and scalability. Here we present some previous works in both fields and
describe how our works relate.

As we described briefly in the introduction, the main trend for data
collection in smart cities is to wirelessly link all connected-objects to the
long-range operated network such as the cellular network and the LPWAN [26,
27]. Yet, in [27], they also highlighted the high cost of implementing a
cellular-based network such as the Narrowband-Internet of Things (NB-IoT),
which might be unsustainable economically as the connected-objects population
grows exponentially in the coming years. Meanwhile, the LPWAN solution such as
the Long Range Wide Area Networks (LoRaWAN) has a limited bandwidth and duty-
cycle [28], which might also impede their implementation for the collection of
data with larger sizes and more frequent delivery. Therefore, it is necessary
to diversify the networking technology to handle specific kinds of data for
particular applications. In the field of data collection for delay-tolerant
applications, we believe that a VDTN-Based scheme, as illustrated in Figure 1,
can provide an alternative solution for some issues mentioned previously.

In the field of routing protocol development for data collection, the trend is
to design optimized solutions, as we already covered in the previous chapter.
Among others, the strategy ranges from acquiring a preloaded digital map,
exchanging the current position of vehicles through the GPS’s utilization, to
providing vehicles with apriori macroscopic and microscopic mobility
information. Even though all those technologies are common to today’s modern
vehicles, their utilization for the data routing will definitely increase
resource usage and processing complexity.

The work in [29] presented the public- transportation-assisted data delivery
scheme for VDTN. It recognizes the specific characteristic of common public
transports such as buses, trams, and taxis: their driving trajectories are of
foreknowledge. The scheme assumes the use of GPS to obtain the current
location, the availability of a preloaded street-level digital map that
describes road topology, traffic light period, traffic density, and average
vehicle speed on streets at any different time of the day. Essentially, all
public transports need to exchange their driving paths with each other and
provide information about their driving path and destination.

A similar approach, in terms of the vehicles’ information frequent updating,
Moreira et al. in [30] proposed the Spray and Locate routing protocol for
VDTN. It is a location-based routing protocol that includes a node location
system named VDTN-Locate that provides each vehicle in the network with a
dictionary of other vehicles’ last known location, direction, and speed,
updated every encounter. Its simulation results highlighted that the proposed
protocol has a better average delivery rate and average latency than other
routing protocols being compared.

The two strategies above are examples of a typical geometry-based approach,
which requires a frequent update to their knowledge of the network that leads
to tremendous overhead in a large-scale implementation. Zhang et al. in [31]
proposed to alleviate those challenges by introducing a mobility-aware geocast
algorithm (GeoMob) for data delivery to a specified region with the inclusion
of taxis and buses movement patterns. GeoMob employs an apriori node mobility
information at the macroscopic and microscopic levels. Macroscopic mobility
describes all vehicles’ traffic trends in a city, while microscopic mobility
captures individuals’ mobility patterns. The two mobility levels are extracted
from the real-world GPS traces of taxis and buses in a city.

On the other hand, Giannini et al. in [7] was more focused on collecting data
from smart buildings for environmental and resource consumption monitoring by
utilizing bus mobility. They aimed to directly design and evaluate the Sink
and Delay Aware Bus (S&DA-Bus) routing protocol for collecting data in the
smart city. It defines a centrality metric that considers the “social role” of
the sink. The protocol also considers the Inter-Contact-Time between buses and
the sink to evaluate the subsequent contact delay. Their simulation results
show performance improvement with the deployment of the S&DA-Bus approach if
compared with the benchmark routing protocols.

Some previous works above show interest in utilizing vehicles and public
transportation to collect and deliver data in the city. However, nearly all of
them focus on the in-vehicle data collection for delivery to another vehicle.
Meanwhile, our work is more focused on data collection from connected objects
in the environment. Only the work in [7] closely resembles our settings where
the data were collected for environmental and resource consumption monitoring
from smart buildings.

Moreover, all those works focused on developing optimized solutions which
required apriori knowledge of the network or spent computational resources for
social networking analysis. We differ in our approach by firstly assessing
some baseline routing protocols’ performance thoroughly as a step towards
developing a simple and efficient routing protocol. We aim to provide a
detailed evaluation of each baseline routing protocol’s strengths and
weaknesses in the unique ITS infrastructures-assisted data collection scheme
before moving forward with necessary refinements. Moreover, our evaluations
include scenarios where the number of cars involved in the data collection
gradually increases from a sparse to a denser network, often absent in most
previous works. By doing so, we can give a broader and more dynamic view of
the baseline routing protocols’ performance trend.

##  4 Performance Evaluation

The Opportunistic Networking Environment (ONE) simulator [32] was used to
evaluate the VDTN-based data collections’ networking performances. As we
mentioned previously, we apply three KPIs for the performance evaluation,
namely Delivery Probability, Average Latency, and Overhead Ratio [5, 6, 7, 8,
9].

We utilize a route-based movement for buses and random-waypoints movement for
cars as mobility models in a real-world city map in the simulation. In this
paper, our main goals are to have the performance comparisons between the
baseline routing protocols and understand the trends as the density of the
vehicular networks increases, which can be achieved even with these simplest
mobility models. As discussed in [33], even though trace-based mobility models
give realistic movement patterns, a complete set of traces for the newly
investigated scenario, such as in our case, may not be available. Moreover,
they produce a static scenario where the number of nodes stays the same,
limiting our possibility of observing the effect of a growing vehicular
network. However, we are considering to conduct simulations using suitable
location-based-traces for evaluations in our future work.

###  4.1 The Simulation Setup

The city of Helsinki’s map, a standard map in the simulator, with a land area
size of approximately nine square kilometers, was used to set up the city data
collection scenario. We proposed a delay-tolerant environmental monitoring
system that acquires data from 37 wireless sensors which spaced almost evenly
every 500 meters to each other in the city. Data from those stationary
wireless sensors in the environment need to reach one of 5 available Internet
Point-of-Presences (PoPs) strategically placed along the bus routes. The
mapping of the simulation is shown in Figure 3. We assume the wireless sensor
to be limited in its power capability, i.e., battery-powered or energy
harvesting. Therefore, it can only perform a basic data forwarding mechanism,
such as the First-Contact routing, to transmit data only to the first in-range
vehicle.

With several sets of simulation scenarios, we increase the number of cars
involved in the data collection, while the number of buses remain the same.
The cars involved are from 3 to 90, and 2 buses for each route make up the
total of 4 buses assisting in the data collection. One of the PoP is
positioned at the city center where most of the traffic converged, while four
PoPs are positioned at each end of the two bus routes. In this setting, a bus
will pass at least two PoPs as they move along their route, while there are no
guarantee that cars will encounter one. On the other hand, the benefit of
utilizing cars is that they can go to various places in the city and
opportunistically pick up data from in-range sensors, which is not the case
for buses. With the understanding of the trade-off, we aim to observe some
baseline routing protocols’ networking performance in this setting and find
the gap for future refinement.

Each sensor has a ZigBee wireless link profile with a 10 m communications
range and data rate of 250 kbps. We choose 10 m as a conservative value for
ZigBee’s communication range to include the possibility of Non-Line-of-Sight
(NLOS) signal propagation condition between sensors and vehicles and even
mimic indoor sensor placements. The sensors generate 10 Bytes of data every 5
minutes over the first 7 hours of the 12 hours total simulation duration. The
data have a Time to Live (TTL) of 5 hours. The 10 Bytes of data is for a
single environmental data measurement described in [34]. We choose the ZigBee
communication technology as an archetype of a short-range, low-power sensor
node operating on battery for months, even years. Each car, which can be
connected wirelessly to the sensor via their identical ZigBee link profile,
has a pseudo-random initial placement and mobility. For its V2X
communications, the car also has an ITS-G5 wireless link profile, with a 300 m
communications range and data rate of 6 Mbps. The PoPs have an identical
ITS-G5 connection with the cars for data retrieval, and each also has a direct
link to the destination server. We implement 5 MB of buffer size for cars and
buses, which enable unconstrained evaluation of the networking performance.
The number of cars in the scenario varied from 3 to 90, representing an
average cars density of 0.33 to 10/km2. All parameters and values used in the
simulation are provided in Appendix A of this paper.

Fig. 4: The Probability of Delivery Comparison

###  4.2 Results and Discussion

In all the scenarios, the first in-range vehicle (car or bus) to the sensor
will pick-up the available data. The vehicle then carries and forwards the
data based on the routing protocol that they implement. We need to remember
that the cars’ possibility to reach the PoP is uncertain due to its random
mobility setting, while the buses will always encounter at least 2 PoPs along
their route. In terms of delivery probability, data carried by buses are
desirable, but it may experience a longer waiting time at the sensor, which
increases its latency. On the other hand, cars may pick up data from the
sensor more often, as statistically more cars will be available in the city.
But if the data is only carried by cars, then the delivery probability will
suffer, as there is the uncertainty whether it will reach one of the PoP.

Furthermore, we need to emphasize on the dynamic proportion of vehicles
involved in the data collection, as it crucially relates to the KPI results’
interpretation. In our scenario, buses are considered the mobile network’s
backbone, as PoPs are placed along their routes. A constant number of buses, 4
in total, are deployed in all scenarios, while the number of cars increases
from 3 to 90. Hence, more buses are involved in the network in the scenario
with the least number of cars. The proportion of cars in the network then
gradually increases while the number of buses remains the same, changing the
ratio between vehicles with random and predetermined mobility. The effects of
this dynamic, combined with the different mechanisms that each of the baseline
routing protocol deploys, are explained in the following discussion.

We first evaluate each routing protocol’s delivery probability and show their
comparison in Figure 4. It shows that when the cars density was low, i.e.,
sparse network, the two single-copy approaches, the First Contact and the
Direct Delivery, achieved comparable delivery probability to the two multi-
copy routing protocols, the Spray & Wait and the Epidemic. But as the cars
density increases, i.e., the network becomes denser, their delivery
probability begins to lag. Initially, we expected that the Epidemic routing
protocol would always outperform the other three routings with its maximum
copies approach. Yet, surprisingly, the Spray & Wait’s delivery probabilities
are comparable to the Epidemic’s in all network densities. In the larger part,
it was because most of the undelivered messages were never transferred from
sensors to in-range vehicles until they ran out of TTL and dropped from the
sensor’s buffer. In a smaller portion, the cause was the unsuccessful messages
transfer from vehicles. It turned out that in the intermittent connection with
moving vehicles (vehicles with vehicles or vehicles with stationary nodes),
the limited contact duration and the number of messages queuing for
transmission in the buffer diminished the advantage of having maximum copies
of messages in the network. It is possible that when vehicles’ buffer becomes
flooded by Epidemic messages: there is a lower probability that a particular
message can be transferred in a brief window of contact between nodes
(sensors, cars, buses, and PoPs). Moreover, the faster the vehicle move, the
narrower the transferring window becomes, as previously studied in [35]. The
dynamic highlighted that flooding the network with unlimited data copies seems
to give minor gain, if not none, in terms of delivery probability in our
scenario.

In the Direct Delivery routing, vehicles will keep all data they received from
sensors and only do the forwarding if one of the PoP is in the communication
range. Therefore, in our scenario, only data picked up by buses will have
guaranteed delivery due to the PoPs placement, while data collected by cars
will have none of the certainty. It explains why this routing protocol’s
delivery probabilities are the lowest, and even more so as the cars density
increases.

On the other hand, in the First Contact routing, the vehicle with the data
will always forward them to the first node (car, bus, or PoP) that it
encounters. In this approach, two mechanisms with opposite effects can take
place in the data collection. The first one is when the car forwards the data
to an in-range bus, i.e., a condition of guaranteed delivery, which is
desirable. The second one is when the bus needs to ’give up’ its data to a
nearby car, i.e., cancellation of guaranteed delivery, which is undesirable.
Yet, despite this challenging dynamic, the results show that the First Contact
performs better than Direct Delivery in its probability of delivery. It turned
out that data picked up by cars from sensors will have a better chance of
reaching one of the PoP if it is opportunistically forwarded to the next in-
range vehicle rather than to being kept for direct delivery. The advantage
becomes more significant if the car is stationary for a long time; offloading
the data would increase its delivery chance.

Fig. 5: The Average Latency Comparison

In the Spray & Wait and Epidemic routing, with their multi-copies approach,
the vehicle with the data will forward them to all in-range nodes while also
keeping the original for direct delivery to the destination. It resulted in a
higher delivery probability as more paths are possible for the data to reach
the PoPs. The results also give one crucial point: the Spray & Wait’s Delivery
Probabilities are comparable to those of the Epidemic’s, hinting to the
advantage of forwarding limited copies and the inefficiency of deploying
maximum copies in the scenario.

Figure 5 compares the routing protocols in terms of the average latency. It
shows two opposite trends for majority of the routing protocols in relation to
the available number of cars in the network. The Direct Delivery routing
protocol is the only exception to the trend.

For the Direct Delivery routing, increases in the cars density are also
accompanied by increases in the average latency until it reaches around 99
minutes at a cars density of 4 per km2. At that point, the average latency
seems to come to its maximum value, where it remains below 99 minutes, even
with the increase in the cars density. In the minimum car density of 0.33 per
km2, Direct Delivery’s average latency is also the lowest at 59.45 minutes. In
interpreting this result, we need to relate to their low delivery probability
shown in Figure 4, of around 0.31 only, where the average latency is
calculated only from the successfully delivered data. There are only 3 cars in
the entire network in this cars density, less than the number of buses, which
is 4. Therefore, there is a high probability that the delivered data were
previously picked up by buses, which then carry them to one of the PoPs along
their route, causing the lower average latency. As the cars density and the
delivery probability increase, more cars pick the data instead of buses.
Consequently, the average latency is also going up due to data being kept
longer in cars with the Direct Delivery routing protocol. The results also
suggest a particular maximum value of average latency exists for the data
collection with this routing protocol, although it will undoubtedly depend on
the car’s mobility. Yet, with about 0.82 delivery probability at the highest
cars density in our simulation, there is merit for its implementation in
denser networks for applications that can tolerate a longer latency and an
incomplete set of time-series data.

For the First Contact routing, although it has a different forwarding
mechanism than the Direct Delivery, the average latency trend is quite similar
at the lower cars density, where increases in the density also lead to
increases in the average latency due to the same mechanism. The difference is
that the trend reverses at a cars density of 6 per km2 when the average
latency starts to decrease with an increase in the cars density. One important
note in the minimum cars density of 0.33 per km2; the First Contact average
latency of 64.52 minutes is higher than the Direct Delivery. The higher
average latency might be due to the undesirable effect of buses ’giving up’
its data to in-range cars, which we explained earlier. A more advantageous
mechanism would be for buses to keep their data until they reach one of the
PoPs, a gap that can be refined for a better routing protocol.

The two multi-copies routing protocols, the Spray & Wait and Epidemic, also
possess a similar trend to the First Contact routing. They differ in that they
have lower average latency and the reverse in the trend for them starts
earlier at a cars density of 2 per km2. Figure 5 also shows that the Spray &
Wait only lagging the Epidemic slightly in terms of its average latency,
particularly at the lower cars density, even though it only deploys up to 6
copies of each data in the forwarding process. It is a crucial advantage that
will be highlighted further when discussing its Overhead Ratio comparison with
the Epidemic later on.

If we observe Figure 5 further and compare the Average Latency of the First
Contact and Spray & Wait, we can find that the First Contact’s Average Latency
is always higher than Spray & Wait’s in all of the cars densities. It is
primarily due to the higher number of data copies that Spray & Wait allowed to
forward, which increases the possibility that one of the copy will reach a PoP
sooner.

Fig. 6: The Overhead Ratio Comparison

Interestingly, the dynamic is also influenced by the inclusion of buses in the
data collection network and how the Spray & Wait routing ’accidentally’
harnesses their potential. At a specific condition where a bus received a copy
or copies of the data, the two routing protocols behave differently. When a
bus with First Contact routing received a single copy of data, there is no
guarantee that it will keep the data until it reaches one of the PoPs. As we
pointed out earlier, there is the possibility that the bus will have to
forward the data to an in-range car before it encounters any PoPs. Contrary,
when a bus with Spray & Wait routing received a copy or copies of data, it
will at least keep a copy for direct delivery, consequently elevating the
delivery probability and potentially minimizing the average latency. The
’accidental’ behavior is a strength that needs to be embedded into a better
routing strategy for the specific data collection purpose.

Figure 6, on the other hand, captures each routing protocol’s networking cost
in terms of their overhead ratio. It shows contrasts between each routing
protocol in the single-copy and the multi-copies groups. In the single-copy
group, the Direct Delivery routing produces the lowest overhead ratio in all
cars densities, a constant value of 2, as the single-copy data forwarding only
happens from sensors to cars or buses, and then from cars or buses to one of
the PoPs. On the contrary, the First Contact routing showed an increase of
overhead ratio as the network becomes denser. The strategy of always
forwarding the data to the next vehicle in-range makes the overhead ratio goes
up as more vehicles become available to receive the data. There is no
restriction on how many data forwarding processes can take place, until
eventually the data reach one of the PoPs. There is also no distinction on
which vehicles to forward the data to, which make the data transfer from buses
to cars also possible. As described earlier, it would be more efficient if
there is a distinction on which kind of vehicles to forward to, e.g., buses
which have higher delivery probability should not forward data to cars. The
added logic will lead to lower average latency and overhead ratio, as well as
higher delivery probability.

We can also observe both similarity and disparity of the Overhead Ratio trend
in the multi-copies routing pair. Spray & Wait and Epidemic both experience an
increase in the Overhead Ratio as the number of cars grows in the network. The
difference is that the Spray & Wait’s Overhead Ratio seems to reach its
saturation value of around 8 at a cars density of 2 per km2 and only has minor
increases as the cars density goes up. The dynamic occurs because the Spray &
Wait routing implements a limited number of copies and, consequently, a
limited number of forwarding strategy. Vehicles only forward a limited number
of data copies to other vehicles. Each vehicle then does a direct delivery to
one of the PoPs when only a single copy of the data remains in their
possession. Therefore, the so-called ’wait’ mode limits the communication hops
and consequently reduces the Overhead Ratio. The opposite happens to the
Epidemic, where its Overhead Ratio goes up sharply as the cars density
increases. The Epidemic routing deploys unlimited data copies and
communication hops strategy at the expense of inundating the network with more
overheads, even though it manages to achieve the lowest Average Latency in a
denser network. Indeed, the trade-off will be a concern to its scalability in
an ever-growing network and further emphasizes the advantage of Spray & Wait’s
limited-copies strategy.

Finally, from Figure 6, another revealing dynamic can be observed when we
compare the Overhead Ratio of First Contact and Spray & Wait routing. In a
sparse vehicular network, i.e., cars density of 0.33 to 2 per km2 in our
scenario, First Contact’s Overhead Ratios are lower than the Spray & Wait’s.
While in a denser network, First Contact’s Overhead Ratios surpass Spray &
Wait’s, which only increases slightly. The dynamic can be explained as
follows.

Table 1: Summary of The Baseline Routing Protocols’ Strengths and Weaknesses Routing Protocol |  Strengths |  Weaknesses
---|---|---
Direct Delivery |  * Fairly high Delivery Probability and showing an upper-limit of Average Latency in a denser network; might be applicable for certain applications.
* Constant Overhead Ratio and the lowest among the baseline routings, offering the most efficient network usage. |  * Lowest Delivery Probability and highest Average Latency among the baseline routings.
First Contact |  * Fairly high Delivery Probability and shows a decreasing Average Latency as the network become denser. |  * Continuous increases in Overhead Ratio as the network become denser.
* It does not have a mechanism to recognize vehicles with predetermined mobility; therefore, the single-copy of data might be offloaded to cars that have a lower probability of delivering them.
Spray & Wait |  * Tied-highest Delivery Probability (with Epidemic) among the baseline routings and shows a decreasing Average Latency as the network become denser.
* Moderate Overhead Ratio with minor increases as the network grows.
* The _wait_ phase, when a forwarding vehicle keeps at least a copy of the data, ’accidentally’ ensures the direct delivery of the data by vehicles with predetermined movement. |  * It does not have a mechanism to recognize vehicles with predetermined mobility; therefore, data that is already relayed to vehicles with predetermined mobility continue to be forwarded to cars with a lower probability of delivering them, consequently increasing the Overhead Ratio.
Epidemic |  * Tied-highest Delivery Probability (with Spray & Wait) and the lowest Average Latency among the baseline routings. |  * Extremely high Overhead Ratio, which will continue to increase in a growing network.

The First Contact deploys an unlimited amount of forwarding, i.e., number of
hops, until one of the PoPs is reachable. There are limited vehicles at a
lower cars density to forward the data. When one becomes in-range, only a
single-copy is forwarded, leading to a lower Overhead Ratio. Contrary, more
vehicles are available at a denser vehicular network to forward the single-
copy of data and combined with the unlimited forwarding strategy resulting in
a higher Overhead Ratio. The Spray & Wait, on the other hand, starts with 6
copies of each data available to immediately forwarded to in-range vehicles.
Thus, even though the number of cars is limited, each vehicle can forward more
than one copy of the data in their ’spraying’ phase and causing a higher
Overhead Ratio if compared to the First Contact. A reversal happens as the
cars density increases: vehicles with Spray & Wait strategy have a limited
number of hops, even though plenty of vehicles are available to receive data
from them. As we might recall, vehicles with this routing strategy can only
forward copies until they left with the last copy of each data, when they stop
’spraying’ and start their ’wait’ phase and directly deliver to one of the
PoPs. The limited hops dynamic resulted in Spray & Wait’s lower Overhead Ratio
if compared to First Contact’s in denser networks.

To summarize, Table 1 shows the strengths and weaknesses of the four baseline
routing protocols when applied to the proposed data collection scheme. All the
results above point out that the baseline routing protocols evaluated in this
study do not have a mechanism to exploit the full potential of having vehicles
with predetermined mobility, such as buses, in the data collection process.
Therefore, the advantage of having ITS infrastructures to assist in the system
is also overlooked. Our performance evaluation indicates the need to develop a
routing protocol that recognizes and utilizes the advantage of different kinds
of mobility in the field for data collection, such as the work on a
hierarchical VDTN routing protocol named DC4LED presented in [36] and [37]. We
believe that for the specific purpose of data collection that we already
discussed, a simple and efficient routing protocol can be deployed for the
delay-tolerant data delivery, contrary to the implementation of optimized
solutions that might be resource-demanding and difficult to standardize.

##  5 Conclusion and Future Works

This paper presented the performance comparison of four baseline VDTN routing
protocols in a unique ITS-assisted data collection setting in smart cities. In
the VDTN-based data collection scenario, several Internet Point-of-Presences
are available and strategically located in the city to assist in the data
delivery from connected-objects to the application server. We assessed the
four routing protocols in this specific scenario to see gaps that can be
refined. These protocols have sufficient diversity of mechanisms to forward
data, giving plenty of insight into their impact on the Key Performance
Indicators. Furthermore, the inclusion of two types of vehicles, each with
their distinct mobility pattern, into the simulation also reveals some crucial
forwarding dynamics.

The comparison between the baseline routing protocols showed that generally
multi-copy strategies perform better by offering higher delivery probability
and lower average latency. Their drawback was on the high overhead ratio that
will burden the network in a dense environment such as smart cities. Moreover,
we also observed different trends in varying vehicles density, wherein a
sparse network the single-copy approaches actually have a comparable
performance with the multi-copy strategy. It was only when the network became
denser that the advantage of forwarding limited multi-copies data became
apparent, suggesting implementing a different routing strategy based on the
vehicular network density. Our work also emphasizes some gaps in the routing
strategies that can be refined for better performances.

Our future works will include refining the DC4LED routing protocol to
incorporate the advantage of the limited-copies approach highlighted by the
Spray & Wait routing performances in this paper. The DC4LED routing protocol
already consists of a mechanism that recognizes and utilizes specific mobility
patterns from various vehicles available in the city. It forwards data
hierarchically to maintain efficiency and scalability. The protocol will also
need to be tested in a more realistic vehicle’s mobility pattern provided by
real-world GPS traces to increase the KPI values’ accuracy.

## Acknowledgements

This research was supported by a Doctoral Scholarship from the Indonesian
Endowment Fund for Education (LPDP) of the Indonesia’s Ministry of Finance,
under Contract Number PRJ-43/LPDP.3/2016.

##  Appendix A Parameters and Values in The Simulation

Parameters |  Values
---|---
Map size |  4.5 km x 3.4 km
Land area |  approximately 9 km2
Simulation time |  12 hours
Simulation warm up time |  200 s
Message generation window |  7 hours
Message Time-to-Live (TTL) |  5 hours
Messages created by sensors |  3071
Sensors |
Number of sensors |  37
Movement model |  Stationary
Message size |  10 B
Message generation interval |  5 minutes
Buffer size |  64 KB
Interface type |  ZigBee link profile
Transmission range |  10 m
Transmission rate |  250 Kbps
Cars |
Number of cars |  3; 6; 9; 18; .. 72; 90 (corresponds to cars density of 0.33 to 10 per km2)
Movement model |  Random Waypoints & Shortest-path Map-based
Movement speed |  10 - 50 km/h
Stationary time at |
waypoints |  between 1 - 120 min.
Buffer size |  5 MB
Interface#1 type |  ZigBee link profile
Transmission range |  10 m
Transmission rate |  250 kbps
Interface#2 type |  ITS-G5 V2V link profile
Transmission range |  300 m
Transmission rate |  6 Mbps
Buses |
Number of buses |  4 (2 for each route)
Movement model |  Fixed Waypoints & Shortest-path Map-based
Movement speed |  10 - 30 km/h
Stationary time at |
waypoints |  between 10 - 20 sec.
Buffer size |  25 MB
Interface#1 type |  ZigBee link profile
Transmission range |  10 m
Transmission rate |  250 kbps (Zigbee)
Interface#2 type |  ITS-G5 V2V link profile
Transmission range |  300 m
Transmission rate |  6 Mbps
Internet Point-of-Presence (PoP) |
Movement model |  Stationary
Buffer size |  100 MB
Interface#1 type |  ITS-G5 V2I link profile
Transmission range |  300 m
Transmission rate |  6 Mbps

## References

  * [1] Z. MacHardy, A. Khan, K. Obana, S. Iwashina, V2x access technologies: Regulation, research, and remaining challenges, IEEE Communications Surveys & Tutorials 20 (3) (2018) 1858–1877.  doi:10.1109/comst.2018.2808444.
URL https://doi.org/10.1109/comst.2018.2808444

  * [2] P. R. Pereira, A. Casaca, J. J. P. C. Rodrigues, V. N. G. J. Soares, J. Triay, C. Cervello-Pastor, From delay-tolerant networks to vehicular delay-tolerant networks, IEEE Communications Surveys & Tutorials 14 (4) (2012) 1166–1182.  doi:10.1109/surv.2011.081611.00102.
URL https://doi.org/10.1109/surv.2011.081611.00102

  * [3] N. Benamar, K. D. Singh, M. Benamar, D. E. Ouadghiri, J.-M. Bonnin, Routing protocols in vehicular delay tolerant networks: A comprehensive survey, Computer Communications 48 (2014) 141–158.  doi:10.1016/j.comcom.2014.03.024.
URL https://doi.org/10.1016/j.comcom.2014.03.024

  * [4] F. Arena, G. Pau, An overview of vehicular communications, Future Internet 11 (2) (2019) 27.  doi:10.3390/fi11020027.
URL https://doi.org/10.3390/fi11020027

  * [5] Agussalim, M. Tsuru, Comparison of DTN routing protocols in realistic scenario, in: 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE, 2014.  doi:10.1109/incos.2014.80.
URL https://doi.org/10.1109/incos.2014.80

  * [6] S. A. Hadiwardoyo, A. J. T. Santos, Deploying public surface transit to forward messages in DTN, in: 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, 2015.  doi:10.1109/iwcmc.2015.7289275.
URL https://doi.org/10.1109/iwcmc.2015.7289275

  * [7] C. Giannini, P. Calegari, C. Buratti, R. Verdone, Delay tolerant network for smart city: Exploiting bus mobility, in: 2016 AEIT International Annual Conference (AEIT), IEEE, 2016.  doi:10.23919/aeit.2016.7892779.
URL https://doi.org/10.23919/aeit.2016.7892779

  * [8] E. Spaho, L. Barolli, V. Kolici, A. Lala, Evaluation of single-copy and multiple-copy routing protocols in a realistic VDTN scenario, in: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), IEEE, 2016.  doi:10.1109/cisis.2016.43.
URL https://doi.org/10.1109/cisis.2016.43

  * [9] R. Hernández-Jiménez, C. Cardenas, D. M. Rodríguez, Modeling and solution of the routing problem in vehicular delay-tolerant networks: A dual, deep learning perspective, Applied Sciences 9 (23) (2019) 5254.  doi:10.3390/app9235254.
URL https://doi.org/10.3390/app9235254

  * [10] R. C. Shah, S. Roy, S. Jain, W. Brunette, Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks, Ad Hoc Networks 1 (2-3) (2003) 215–233.  doi:10.1016/s1570-8705(03)00003-9.
URL https://doi.org/10.1016/s1570-8705(03)00003-9

  * [11] G. Anastasi, M. Conti, M. D. Francesco, Data collection in sensor networks with data mules: An integrated simulation analysis, in: 2008 IEEE Symposium on Computers and Communications, IEEE, 2008.  doi:10.1109/iscc.2008.4625629.
URL https://doi.org/10.1109/iscc.2008.4625629

  * [12] S. Medjiah, T. Taleb, T. Ahmed, Sailing over data mules in delay-tolerant networks, IEEE Transactions on Wireless Communications 13 (1) (2014) 5–13.  doi:10.1109/tw.2013.123013.120398.
URL https://doi.org/10.1109/tw.2013.123013.120398

  * [13] S. Turksma, The various uses of floating car data, in: Tenth International Conference on Road Transport Information and Control, IEE, 2000.  doi:10.1049/cp:20000103.
URL https://doi.org/10.1049/cp:20000103

  * [14] V. Astarita, V. Giofré, D. Festa, G. Guido, A. Vitale, Floating car data adaptive traffic signals: A description of the first real-time experiment with “connected” vehicles, Electronics 9 (1) (2020) 114.  doi:10.3390/electronics9010114.
URL https://doi.org/10.3390/electronics9010114

  * [15] C. Colombaroni, G. Fusco, N. Isaenko, Analysis of road safety speed from floating car data, Transportation Research Procedia 45 (2020) 898–905.  doi:10.1016/j.trpro.2020.02.078.
URL https://doi.org/10.1016/j.trpro.2020.02.078

  * [16] N. Uno, F. Kurauchi, H. Tamura, Y. Iida, Using bus probe data for analysis of travel time variability, Journal of Intelligent Transportation Systems 13 (1) (2009) 2–15.  doi:10.1080/15472450802644439.
URL https://doi.org/10.1080/15472450802644439

  * [17] P. Sekuła, N. Marković, Z. V. Laan, K. F. Sadabadi, Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A maryland case study, Transportation Research Part C: Emerging Technologies 97 (2018) 147–158.  doi:10.1016/j.trc.2018.10.012.
URL https://doi.org/10.1016/j.trc.2018.10.012

  * [18] C. Villiers, L. D. Nguyen, J. Zalewski, Evaluation of traffic management strategies for special events using probe data, Transportation Research Interdisciplinary Perspectives 2 (2019) 100052.  doi:10.1016/j.trip.2019.100052.
URL https://doi.org/10.1016/j.trip.2019.100052

  * [19] G. Pau, A. Severino, A. Canale, Special issue “new perspectives in intelligent transportation systems and mobile communications towards a smart cities context”, Future Internet 11 (11) (2019) 228.  doi:10.3390/fi11110228.
URL https://doi.org/10.3390/fi11110228

  * [20] F. Arena, G. Pau, A. Severino, A review on IEEE 802.11p for intelligent transportation systems, Journal of Sensor and Actuator Networks 9 (2) (2020) 22.  doi:10.3390/jsan9020022.
URL https://doi.org/10.3390/jsan9020022

  * [21] R. Silva, J.-M. Bonnin, T. Ernst, Opportunistic Networking for ITS, in: ITS 2016 : Intelligent Transport Systems: Past, Present and Future Directions, Nova publisher, 2017, pp. 59 – 86.
URL https://hal.archives-ouvertes.fr/hal-01635357

  * [22] S. Jain, K. Fall, R. Patra, Routing in a delay tolerant network, in: Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications - SIGCOMM ’04, ACM Press, 2004.  doi:10.1145/1015467.1015484.
URL https://doi.org/10.1145/1015467.1015484

  * [23] A. Vahdat, D. Becker, Epidemic Routing for Partially-Connected Ad Hoc Networks, Tech. rep., CS-200006, Duke University (2000).
  * [24] T. Spyropoulos, K. Psounis, C. S. Raghavendra, Spray and Wait: An Efficient Routing Scheme for Intermittently Connected Mobile Networks, in: Proceeding of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking - WDTN ’05, ACM Press, New York, NY, USA, 2005\.  doi:10.1145/1080139.1080143.
URL https://doi.org/10.1145/1080139.1080143

  * [25] T. Abdelkader, K. Naik, A. Nayak, N. Goel, V. Srivastava, A performance comparison of delay-tolerant network routing protocols, IEEE Network 30 (2) (2016) 46–53.  doi:10.1109/mnet.2016.7437024.
URL https://doi.org/10.1109/mnet.2016.7437024

  * [26] F. Ghavimi, H.-H. Chen, M2m communications in 3gpp LTE/LTE-a networks: Architectures, service requirements, challenges, and applications, IEEE Communications Surveys & Tutorials 17 (2) (2015) 525–549.  doi:10.1109/comst.2014.2361626.
URL https://doi.org/10.1109/comst.2014.2361626

  * [27] K. Mekki, E. Bajic, F. Chaxel, F. Meyer, A comparative study of LPWAN technologies for large-scale IoT deployment, ICT Express 5 (1) (2019) 1–7.  doi:10.1016/j.icte.2017.12.005.
URL https://doi.org/10.1016/j.icte.2017.12.005

  * [28] F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melia-Segui, T. Watteyne, Understanding the limits of LoRaWAN, IEEE Communications Magazine 55 (9) (2017) 34–40.  doi:10.1109/mcom.2017.1600613.
URL https://doi.org/10.1109/mcom.2017.1600613

  * [29] Y. Feng, K. Liu, Q. Qian, F. Wang, X. Fu, Public-transportation-assisted data delivery scheme in vehicular delay tolerant networks, International Journal of Distributed Sensor Networks 8 (12) (2012) 451504.  doi:10.1155/2012/451504.
URL https://doi.org/10.1155/2012/451504

  * [30] E. Moreira, N. Magaia, P. R. Pereira, Spray and locate routing for vehicular delay-tolerant networks, in: 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST), IEEE, 2018.  doi:10.1109/itst.2018.8566757.
URL https://doi.org/10.1109/itst.2018.8566757

  * [31] L. Zhang, B. Yu, J. Pan, GeoMob: A mobility-aware geocast scheme in metropolitans via taxicabs and buses, in: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, IEEE, 2014\.  doi:10.1109/infocom.2014.6848116.
URL https://doi.org/10.1109/infocom.2014.6848116

  * [32] A. Keränen, J. Ott, T. Kärkkäinen, The ONE simulator for DTN protocol evaluation, in: Proceedings of the Second International ICST Conference on Simulation Tools and Techniques, ICST, 2009.  doi:10.4108/icst.simutools2009.5674.
URL https://doi.org/10.4108/icst.simutools2009.5674

  * [33] J. Dede, A. Forster, E. Hernandez-Orallo, J. Herrera-Tapia, K. Kuladinithi, V. Kuppusamy, P. Manzoni, A. bin Muslim, A. Udugama, Z. Vatandas, Simulating opportunistic networks: Survey and future directions, IEEE Communications Surveys & Tutorials 20 (2) (2018) 1547–1573.  doi:10.1109/comst.2017.2782182.
URL https://doi.org/10.1109/comst.2017.2782182

  * [34] L. Kang, S. Poslad, W. Wang, X. Li, Y. Zhang, C. Wang, A public transport bus as a flexible mobile smart environment sensing platform for IoT, in: 2016 12th International Conference on Intelligent Environments (IE), IEEE, 2016.  doi:10.1109/ie.2016.10.
URL https://doi.org/10.1109/ie.2016.10

  * [35] N. I. Er, K. D. Singh, J.-M. Bonnin, Contact strategy for VDTN data collection in smart cities, in: 2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), IEEE, 2018.  doi:10.1109/icsgteis.2018.8709131.
URL https://doi.org/10.1109/icsgteis.2018.8709131

  * [36] N. I. Er, K. D. Singh, J.-M. Bonnin, DC4LED: A hierarchical VDTN routing for data collection in smart cities, in: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, 2019\.  doi:10.1109/ccnc.2019.8651757.
URL https://doi.org/10.1109/ccnc.2019.8651757

  * [37] N. I. Er, K. D. Singh, J.-M. Bonnin, CityView: A vehicle based image and data collection for smart cities, in: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE, 2019.  doi:10.1109/wimob.2019.8923547.
URL https://doi.org/10.1109/wimob.2019.8923547