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VANET: A novel service for predicting and disseminating vehicle traffic
information: VANET- A novel service
ArticleinInternational Journal of Communication Systems · April 2020
DOI: 10.1002/dac.4288
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Received: 26 August 2018 Revised: 28 August 2019 Accepted: 21 October 2019
DOI: 10.1002/dac.4288
RESEARCH ARTICLE
VANET: A novel service for predicting and disseminating
vehicle traffic information
Sahraoui Abdelatif1Makhlouf Derdour1Nacira Ghoualmi-Zine2
Bouchra Marzak3
1Systems and Networks Laboratory,
University of Larbi Tebessi, Tebessa,
Algeria
2Systems and Networks Laboratory,
University Badji Mokhtar, University of
Annaba, Annaba, Algeria
3Information Processing Laboratory,
Department of Physical, University
Hassan II, Casablanca, Morocco
Correspondence
Sahraoui Abdelatif, Systems and Networks
Laboratory, University of Larbi Tebessi,
Tebessa, Algeria. 300 logts, Ouenza,
12003, Tebessa-Algeria.
Email:
abdelatif.sahraoui@univ-tebessa.dz
Summary
The arrival of cloud computing technology promises innovative solutions to
the problems inherent in existing vehicular ad hoc network (VANET) net-
works. Because of the highly dynamic nature of these networks in crowded
conditions, some network performance improvements are needed to antici-
pate and disseminate reliable traffic information. Although several approaches
have been proposed for the dissemination of data in the vehicular clouds, these
approaches rely on the dissemination of data from conventional clouds to vehi-
cles, or vice versa. However, anticipating and delivering data, in a proactive
way,basedonquerymessageoraneventdrivenhasnotbeendefinedsofarby
these approaches. Therefore, in this paper, a VANET-Cloud layer is proposed
for traffic management and network performance improvements during con-
gested conditions. For the traffic management, the proposed layer integrates the
benefits of the connected sensor network (CSN) to collect traffic data and the
cloud infrastructure to provide on-demand and automatic cloud services. In this
work, traffic services use a data exchange mechanism to propagatethe predicted
data using a fuzzy aggregation technique. In the evaluation phase, simulation
results demonstrate the effectiveness of the proposed VANET-Cloud layer to
dramatically improve traffic safety and network performance as compared with
recent works.
KEYWORDS
cloud service, data collection and prediction, data dissemination models, data exchange, handle
message mechanisms, traffic information
1INTRODUCTION
The vehicular ad hoc network (VANET) differs from other types of ad hoc networks because of their hybrid architecture
and high vehicle mobility. In the world of nowadays, modern vehicles are equipped with various embedded devices such
as the on-board unit (OBU), camera, event data recorder (EDR), radar, wireless communication devices, and GPS. These
high-tech devices build a substantial portion of the vehicle sensor network (VSN). The VSN allows the vehicles to be
intelligent objects in the urban areas, collaborate to detect the environment, provide countless levels of functionality, share
and store sensed data on the move with other vehicles, technologies (such as cloud services), base stations, etc. However,
the VSN collects traffic data by combining VANET communication models with integrated sensors installed in vehicles.
Int J Commun Syst. 2020;e4288. wileyonlinelibrary.com/journal/dac © 2020 John Wiley & Sons, Ltd. 1of16
https://doi.org/10.1002/dac.4288
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FIGURE 1 The convergence of different
technologies that lead to the emergence of
Intelligent Internet-of-vehicle
The collected data is transported from the vehicles to the control unit of the road unit (RSU). To achieve efficient data
collection, a data management system is required to reduce the amount of data to be transmitted via VSN.1
The cloud computing is a new paradigm based on Internet technology to provide numerous on-demand services,2such
as management service, computing utility, endless computing resources, and large storage. Recently, the cloud computing
technology has been involved in the vehicular environments to add value to entertainment/safety services. This conver-
gence is called vehicular cloud computing (VCC).3,4 The VSN and VCC are combined with each other via Internet of
things (IoTh) to converge conventional VANET networks to the next generation of wireless networks. In this regard, the
vehicles can use the potential cloud services (eg, storage and computing) to form mini-data centers on the move and share
the traffic information.
Particularly, we can track back the roots of this convergence by observing several emerging technologies where each
sub-convergence leads to the introduction of new era of applications. Figure 1 in the following recapitalizes the conver-
gence of these technological fields that lead to the introduction of modern applications in VANET networks. On which,
VANET is a subclass of mobile ad hoc network (MANET). VANET is linked with intelligent transportation systems (ITS)
to form intelligent vehicular networks (IVN).5-7 At this stage, the attribution of a robust system of traffic management still
imposes a problem that persists. But with the advent of the cloud technology, the task of management will be anywhere,
anytime, and it performed according to three models: private clouds, public clouds, and hybrid clouds. This passage is
named vehicular cloud computing (VCC) where the vehicles have public and/or private access to the cloud services via
an Internet connection. Hence, Internet-of-vehicle (IoV) presents a particular class of IoTh to make the difference among
the vehicle concept in VANET networks and IoV in terms of software and hardware equipment. With this driving evolu-
tion, the future of the traffic management systems will employ the cloud computing and sensor networks to control the
traffic safety, ensure an effective cost, and provide public services via intelligent IoV.8-10
In this paper, a VANET-Cloud layer is proposed to increase the traffic safety, improve the traffic information, and optimal
network performance during congested conditions. For this, our main contributions are highlighted as follows:
•Propose a VANET-Cloud layer allows transmitting accurate traffic information to the drivers and improve network
performance under congested traffic conditions. These valuable information may be requested (eg, travel information)
by the drivers or periodically provided by the potential cloud services.
•Provide two types of traffic services (eg, on-demand services and automatic services) to ensure the delivery of data
based on the request message or based on the occurrence of an expected event in the road.
•Provide a data exchange mechanism that incorporates uplink and downlink data strategies for reliable data collection
and network performances.
Particularly, the architecture of our traffic management is based mainly on the cloud infrastructure and the prolifera-
tion of the connected sensor network around the cities. The sensor network is used to collect traffic data and the cloud
system is used to provide on-demand services (eg, vehicle travel time) and intelligent services (eg, intelligent signalization
system). These services use data aggregation and prediction mechanism to provide accurate traffic information for the
vehicles. Based on scenario analysis, the simulation results demonstrate the effectiveness of proposed traffic management
to dramatically reduce vehicle travel time and optimal performance compared with traditional VANET networks.
The remaining parts of this paper are arranged as follows. In Section 2, we present the related works. Section 3 describes
our VANET-Cloud architecture for the traffic management. Section 4 describes the network performances of the proposed
layer. Section 5 describes the discussion of our paper. While Section 6, concludes our paper.
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2RELATED WORKS
The VCC paradigm is put forward to provide a wide range of safety services to collect data, improve the network perfor-
mance, and reduce the time spent by the vehicles in traffic congestion. The traffic data collection is an efficient process
for traffic management, vehicle decision-making, and for providing accurate data on the real status of the traffic condi-
tions. Whilst, emerging safety applications use road detection technology (eg, loop detectors and camera) as backbone
for data collection, vehicular communication models for data dissemination, and the conventional clouds for traffic
data prediction. In this article, we mention the most usable approaches that deal with data dissemination and vehicle
decision-making issues in the next generation of vehicular networks.
The purpose of data dissemination on vehicular networks focuses on how to ensure that vehicles receive traffic infor-
mation correctly via one or more roadside units (RSUs) during their traveling. VCC technology can be integrated as a
solution to address the factors that affect data delivery performance, such as intermittent vehicle connectivity, channel
congestion, latency, and the limited computing and storage resources. From the network performance standpoint, vehic-
ular cloud frameworks for traffic data dissemination would be relevant for large urban areas instead of the safety data
provided by existing VANET networks.11 Kim et al12 tackle the problem of data dissemination by providing reliable traffic
information via RSU, where this data comes from traffic management services provided by cloud data centers. They design
a vehicle data prefetch system that optimizes the likelihood of successful data delivery when RSU storage resources are
limited and vehicle connectivity is intermittent. Liu et al13 propose a cloud-assisted downlink framework for traffic data
dissemination, which integrates the key benefits of wireless technology and cloud computing to efficiently deliver safety
messages. The proposed cloud-assisted scheme reduces network delays by delivering cloud server safety messages to a set
of vehicles acting as gateways on the road. This strategy effectively minimizes packet loss and redundancy caused by the
broadcasting. Qureshi et al14 propose clustering strategy, named cluster-based routing for sparse and dense networks, to
manage high vehicle mobility and establish reliable and efficient routing in dynamic topologies. The clustering strategy
for data dissemination includes a protocol in which it adopts routing metrics that are well-known for channel selection,
including traffic information such as traffic density, signal strength, and direction. Marzak et al15 combined clustering
routing protocol and fuzzy logic system (FLS) to improve the performance of the data dissemination by reducing message
delay, extend the stability of the cluster structure, the cluster formation, and cluster head selection.
From the vehicles decision standpoint, it is imperative to propagate the collected data to the Internet clouds for data
analysis and processing to address and eliminate various data issues such as consistency and redundancy.16,17 The preven-
tive traffic services, such as vehicle accident detection and route planning,18,19 use the analyzed and the processed data
by the internet clouds to improve the quality of the disseminated data to the vehicles. To support these kinds of services,
the big data platforms where it includes several data mining techniques are requested to perform various tasks, such as
data selection and aggregation20,21 to extract the vehicle decisions making.
The above approaches aim to provide reliable traffic data dissemination, improve vehicle decision-making, and provide
useful traffic data to drivers based on data collected by vehicles and road detectors. Although these approaches have
been proposed for the data dissemination from/to conventional clouds, the prediction and the dissemination of data, in
proactive way, based on query message or an event driven has not been defined so far by these approaches.
3VANET-CLOUD ARCHITECTURE FOR THE TRAFFIC MANAGEMENT
This section aims to describe our VANET-Cloud architecture for the traffic management, as shown in Figure 2. The
proposed architecture is intended to provide better utilization of the road infrastructure and reduce the number of anoma-
lies associated to the data detection, network delay, and the quality of disseminated data. Additionally, the architecture
integrates connected traffic detectors to capture traffic data in real time. It shows the evolution of the classic VANET archi-
tectures towards multi-layer services model. In particular, the architecture mentions three main layers including, traffic
data collection layer, infrastructure as a service layer, and the VANET-Cloud layer.
3.1 Traffic data collection layer
The traffic data collection layer consists of data describing the context of the traffic networks. However, it intended to
provide real-time traffic data suitable for several contexts of the traffic services in a large and complex system. The main
purpose of this layer is to reduce the number of anomalies confronted by the traffic data acquisition and the data gathering
mechanisms. Additionally, it helps to better understand the stability of the real-time traffic parameters (eg, vehicles speed,
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FIGURE 2 Vehicular ad hoc network
(VANET)-Cloud architecture for the
traffic management
road density, and the traffic flow), detect the congested regions, control the road segments and vehicles mobility, provide
the short- and the long-term historical traffic data, and adapts automatically to the unforeseen road changes. Particularly,
the traffic data collection layer includes two interfaces, sensing traffic data interface and communication interface. From
one hand, sensing traffic data interface is destined to pass captured data by the loop detectors to the cloud infrastructure
for an efficient storage and processing. In particular, the proliferation of connected traffic detectors around the specific
points in the city will increase the detection accuracy of the traffic congestion. In the other hand, the communication
interface uses Internet connection as backbone to provide an access to the real-time traffic services. The vehicles use the
cellular networks (3G, 4G, etc) as backbone to send and receive predicted traffic data by the cloud service (eg, real status
of the traffic flow). In addition, these predicted data offer advantages not only for the individual vehicles but the accuracy
of these data will optimize also the performance of the other VANET applications, such as the optimal route planning
and the traffic congestion applications.
3.2 Infrastructure as a service layer
The key elements of the proposed architecture include the infrastructure layer, which contains two sub-layers: the physical
resources sub-layer and the virtualized sub-layer. The first sub-layer contains a set of physical servers grouped accord-
ing to their characteristics (storage or computing). The storage servers are meant to store the real-time traffic data and
provide an access to the online traffic databases, while the computing servers are dedicated to insure high computing per-
formances. The virtualized sub-layer is designed to reduce the performances degradation confronted by legacy VANET
applications. For instance, during peak hours, the road congestion increases exponentially the number of user requests
for the road sections. In which, the vehicles pass to the region server their road claims. The server responses may take a
delay time in these periods because of the overload and available resources almost all occupied. The access to the server
resources becomes much easier with the advent of cloud computing, where the elasticity of the infrastructure will offer
the possibility to adapt to the vehicles demands as quickly as possible.
3.3 VANET-Cloud layer
The VANET-Cloud layer aims to process the traffic information in real time and optimize the performance of VCC appli-
cations by predicting real-time traffic data (eg, travel time). The traffic management operation spans from the traffic data
collection data aggregation technique to accurate prediction of the traffic data. The prediction mechanism can be adopted
in various contexts deserve a dynamic optimization such as vehicular communication and daily travel practices taking
into account the congested conditions. Particularly, the VANET-Cloud layer contains safety applications that primarily
use data captured from vehicles and the road detectors (eg, loop speed trap detectors) to control and manage the traf-
fic system. The traffic data prediction presents an essential process towards an effective traffic management and reliable
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FIGURE 3 Vehicular ad hoc
network (VANET)-Cloud layer
for on-demand and intelligent
services
decision-making for vehicles. The predicted data released by the cloud service include real-time information about the
traffic flow status, predicted travel times, the traffic speed, blocked roads, etc. In addition, the prediction process may
incorporate the historical traffic data to increase the accuracy of predicted data. The details of the data aggregation and
the prediction mechanism will be explained in the subsequent sections.
In the following, Figure 3 depicts our proposed VANET-Cloud layer for the traffic management system. The additional
layer provides an access to several functionalities for drivers and traffic information center, such as the ability to control
vehicle mobility in real time, improve response time in the peak periods, and reduce the travel time and the cost of the
trips. The core of the proposed layer is the data collection and the traffic data prediction. The data collection is based on
data provided by the road detectors such as loop detectors (LD). The proliferation of the traffic detectors in the city is
intended to monitor and capture an accurate traffic flow of roads sections. The loop detectors aim to capture raw traffic
data such as the traffic flow, the vehicles speed, and the road occupancy. The raw data are collected in data collection
equipment (DCE) and then forwarded to VANET-Cloud layer via UC-7420 series. The UC-7420 device is a network device
that allows connecting DCE(s) and traffic lights (TL) via UMTS systems. The main role of the UC-7420 device is to connect
the road detectors (LD) and the TL to the VANET-Cloud layer via heterogeneous cellular network (HCN). However, the
VANET-Cloud layer is based on the cloud infrastructure (infrastructure as a service layer [IaaS]) to provide scalability
features for different traffic services. In particular, the proposed layer provides two types of cloud services: the on-demand
services and the automatic services. The on-demand services like optimal route planning, route request, and the travel
time information system (IS). The automatic services are triggered when an event is detected by the road detectors or
by the integrated embedded sensors in the vehicles. For instance, when unexpected incident (eg, the slowdown in the
vehicles speed) is detected by the LD, an automatic traffic signal control system can be triggered to control the traffic
lights and the emergency system can be also involved in injury situation. In contrast, the vehicle can be notified by data
response upon their requests or by traffic information provided as periodic data.
3.3.1 Data aggregation and prediction
In this study, the data aggregation and prediction process are based mainly on the prediction method proposed in our
previous work.22 Since it has not defined so far in the literature, it presents an integral part of the proposed framework.
The data aggregation is aimed to tackle the problems of the traffic parameters accuracy. As key feature, from real-time data
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provided by the traffic detectors, the aggregation process identify the accuracy of the short-term traffic flow parameters
such as safety space gap, average speed, critical road segment, and entering rate. These traffic parameters are used after
that by the prediction process and the other potential cloud based-services. In particular, we use Choquet integral operator
to aggregate raw data collected in short interval of time where the fuzzy measurement 𝜇∶g(X)→[0,1]is the powerful
of the operator where it represents the interaction between the measurements. Let X={x1,x2, ..., xn}represent a set of
measurements collected in the interval of time j.Y(x1,x2, ..., xn)denotes the aggregated data by using Choquet integral
operator and further satisfying the following equation:
Y(x1,x2, ..., xn)=
n
i=1
(x(i+1)−x(i))𝜇(X).(1)
The prediction process uses the aggregated measurements to predict accurate traffic parameters. In particular, the traffic
data accuracy can be done by exploring the relationship between the traffic parameters. For instance, to predict the vehicle
travel time, the correlation level between the traffic speed and the road occupancy could be studied. To ample this purpose,
the regression analysis is applied to detect the correlation between the traffic parameters. Furthermore, detecting the
correlation level between these variables is expressed in accordance with the following general equation:
Y𝑗=b0+
n
i=1
(biXi)+𝜖, (2)
where Y𝑗presents the predicted traffic parameter; b0denotes a constant, Xirefers to the set of independent parameters
that is used to interpret the predicted traffic parameters Y𝑗,𝜖is usually denotes the noise (error), and biindicates the
regression coefficient.
3.3.2 Data dissemination models over vehicle-to-cloud communication
The potential cloud services offered by VANET-Cloud layer aim to control the vehicles mobility via three data dissem-
ination models: reactive, proactive and hybrid models. The proposed models are operated over vehicle-to-cloud (V2C)
communication. Each model allows the exchange of the traffic information between the vehicles and the cloud services,
and vice versa. However, the use of each dissemination model is depending on traffic information provided by the cloud
services. Particularly, the data dissemination models are defined as follows:
Reactive data dissemination model: the reactive data dissemination model (as shown in Figure 4A) enables to
receive, manage, and collect the traffic data and allow the vehicles to request on-demand services. This model is associ-
ated mainly on the request/response mechanism where the vehicle claims are considered individually. The reactive model
allows each vehicle to refresh their local data to become effective and more robustness in the road.
Proactive data dissemination model: the proactive data dissemination model is used when the traffic information
are concern all the vehicles in the road. In the proactive model, as shown in Figure 4B, the traffic information is dissem-
inated without considering the need of the vehicles and it released based on periodic mechanism. The vehicles use this
model to be aware and share the traffic information such as traffic jam, nearest location (station, restaurant, and parking),
predicted unexpected event, and road conditions.
Hybrid data dissemination model: as shown in Figure 4C, the hybrid dissemination model presents the combina-
tion of the previous models where the vehicles are involved in collecting the traffic data, detect the road incidents, and
collaborate with the cloud services to prevent the other vehicles. For instance, when an unexpected event occurs, the
vehicles send to the cloud services an event description message revealing an event or congested conditions. When this
message is received by the cloud services, the hybrid dissemination model is used to prevent a group of vehicles about the
new traffic information.
3.4 The data exchange mechanism
To enable the traffic information to be disseminated in each model, we propose several types of messages to be exchanged
between the vehicles and the cloud services. The main purpose of the exchanged data is to provide a simple mechanism
allowing vehicles to avoid traffic congestion, effective in different traffic scenarios during peak hours or suddenly when an
event occurs on the road. However, we define several types of messages supported by the previous dissemination models:
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FIGURE 4 The data dissemination models in vehicular ad hoc network (VANET)-Cloud
1. Event description message (EDM): is a valuable message released by witnessed vehicles or by the witness traffic
detectors in the vicinity to report the occurring of unexpected event on the road. Their description includes the event
location, event data description, and event start time. The event location data is identified by GPS system. Event data
description refers to data relating to sudden occurring problems on the road (eg, slowdown speed) or that are happened
inside the vehicle system. For instance, the installed event data recorder (EDR) device inside the vehicles is destined
to record specific data problems related to engine performances, velocity change, etc. This type of message will inform
the cloud service about sudden occurring events. In particular, each EDM must contains the following basic features
to advertise the accruing event info:
<VehicleIndex >: Vehicle Index field include the unique connected Vehicle Sender ID.
<EventPosition >: The location of the current unexpected event.
<EvtStartTime >: Typical starting event time.
<EvtDescription >: The occurring event description.
2. On-demand message (ODM): is a message sent by the vehicle to request traffic information from the cloud services,
where each ODM includes the vehicle information and its claims. An ODM message includes the following fields:
<VehicleIndex >,<On −demandData >,and<VehPosition >.
3. Data collection message (DCM): The cloud services release this message type to collect individual vehicle infor-
mation (eg, source, destination, and speed) and increase the accuracy of the predicted traffic information. The data
collection messages are disseminated in the vehicular network via V2V and V2I communications. To collect vehicle
data, two types of DCM are used:
(a) Downlink data collection message (DDC): is a request message disseminated by the cloud service to collect
vehicles information from a group of vehicles. The DDC is used to control the vehicle mobility when predicted
events by the cloud service are detected.
(b) Uplink data collection message (UDC): is a response message sent by the vehicles to the cloud service. The
message includes individual vehicle information, such as average speed, current position, source, destination, and
road line.
4. Traffic information message (TIM): a message includes the traffic information for vehicles. This message is released
by the cloud service to maintain the safety of the individual vehicles. It includes: Vehicle Index (VI), vehicle position
(VehPosition), and the essential traffic data (ETD) (e.g. change road, change line, etc.). Moreover, the vehicles that
received this message must adjust their travel path based on these data. In case of the traffic information requires
triggering intelligent system, intelligent cloud services are incorporated to control the traffic lights.
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5. Periodic message (PM): is a message which is periodically sent by the cloud service when the accruing event still
persists. This message type contains traffic information about the rate of congestions, new directions, average traffic
speed, etc.
6. Event-driven message (EDvM): this message is released by the potential cloud services to incorporate the intelligent
cloud services to contribute in the traffic safety.
3.4.1 Handle message algorithm
The Handle message algorithms described in this section are aimed to provide an accurate vehicle reaction in detected
congested regions. The congestion regions are detected and identified through the event description sent by the vehicles
or predicted by the loop detectors. In the synchronized traffic conditions, the real status of the traffic flow can be changed
to the congested flow. In these conditions, only the witness vehicles are allowed to disseminate the event description
message. If a vehicle receives an event description message, it sends the message to the cloud service through nearest
vehicles via V2V communication, V2I communication, or via vehicles that act as gateway to the cloud service.
FIGURE 5 Vehicle traffic
information flowchart
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3.4.2 Data exchange flowchart
Figure 5 presents the flowchart of the proposed algorithms for handling the received safety messages. The flowchart
illustrates the interactions among the cloud service and the vehicles where the outputs are aimed to disseminate traffic
information for the individual vehicle, group of vehicles, triggering intelligent systems or generating traffic reports. In
overall, the traffic report is generated automatically when an accident occurs. In this case, the cloud service will activate
a voice channel between the witness vehicles and the health emergency system to specify precise prevention for the
situation. In reactive dissemination model, the cloud service sent the traffic information on the specific vehicle gate. In
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FIGURE 6 Scenario description
a proactive model, where the traffic information is intended for all the cloud gates, there is timely dissemination if these
data are practical for the elapsed time of broadcast and other broadcast time in which these data are not suitable for
elapsed time. If the traffic information are event-driven related, the service will trigger an intelligent system to control
the vehicle mobility. Otherwise, the traffic information will be sent as a traffic report to the traffic information center.
3.4.3 Scenario description
In recent decades, the number and the scaling of the road congestion in Algeria are generally increasing, where a simple
trip can take an hour of time, especially in the big cities and surrounding. In addition, our analytical scenario aims to
achieve the goal of the Transportation Department to solve the problem of daily road congestion that is concerns the
Tébessa main road (N10, length: 19.1 km). In which, in the rush hours, the congested conditions in this road lead to
prolong the travel time of the vehicles. The purpose of the Transportation Department is detecting the congested regions
of the main road and improving the traffic flow delivered on it from the arteries roads. For this, we use Omnet++23
simulator to modulate and simulate the aforementioned problem by using the proposed data exchange mechanism. From
this way, the scenario description is based on the assumption that is shown in Figure 6. The vehicles are equipped with
a broad range of embedded devices, such as connected sensors (RFID, connected camera, vehicle event detection) and
recent cellular networks (3G, GPRS, and LTE) to deliver seamless internet connection. Embedded devices such as GPS
device, EDR, OBU, and wireless communication devices. Thus, our scenario includes two intersections that connect the
main road to the arteries roads N1 and N2, respectively.
In the showing scenario, the mechanism of exchanged data uses a set of events to avoid the detected congestion, where
the set of events are ordered from the event description to the decision event. The first event (Evt1: Send EDM) is triggered
by vehicles when a slower speed is detected by vehicles or by the loop detectors. The applications of the on-board units
of the witness vehicles generate immediately the EDM that specifies the location of the event (EventPosition), event
start time (EvtStartTime), and the event description (EvtDescription). Once the EDM message is received to the cloud
service, event 2 (Evt2: Send DDC) is triggered to predict the real status of the traffic network by using data collected from
vehicles and the road detectors. However, the vehicles will involve in event 3 (Evt3: Sending UDC) where each vehicle
will generate the UDC the includes the following contextual data: VehicleIndex, VehicleSource, VehicleDestination, and
VehiclePosition. In event 4 (Evt4: sending traffic information), the cloud service will disseminate essential data of each
vehicle by using TIM.
4SIMULATION AND EVALUATION
Our evaluation was conducted based on three basic scenarios over the previous three dissemination models. On which
for each dissemination model, we estimate the data delivery rate, the communication performance and the latency of
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Parameters Value
Road length 2 km
Frequency nand 5.9 GHZ
Rsu/vehicle data rate 16 Mbps
Rsu/vehicle delay 20 ms
Cloud/Rsu data rate 10 Gbps
Cloud/Rsu delay 1.25E-4us
Transmission range 20 km
Max number of vehicles/h 2200
TABLE 1 Simulation parameters settings
the proposed data exchange mechanism. The first scenario is intended to assess the reactive dissemination model (the
on-demand model) where only reactive messages are involved by the exchange mechanism. The second scenario is aimed
to evaluate the proactive dissemination model where the traffic information is destined to inform a group of vehicles. In
this model, only proactive messages are considered by the data exchange mechanism. The third scenario is aimed to test
the hybrid model of the data exchange mechanism by involving both reactive and proactive models. Instead, this inten-
tional model is dedicated to assign automatically vehicle traffic information to either reactive model, proactive model,
or both.
4.1 Data delivery ratio
In order to analyze the results, the same number of the vehicles is contributed to simulate each scenario separately. The
simulation parameters are defined in Table 1. Figure 7 shows the performance of the three proposed scenarios, where
each result concerns a specific model and each point in the figure represents the success rate of the data delivery. The
arranged simulation time on the X-axis is reported with the delivery rate. In the reactive scenario, we use the formula
(3) to estimate the rate of exchanged reactive messages. The performance results of this scenario show that the success
rate of the delivery data is stable during the simulation time. This means that the success rate of the delivery will remain
unchanged even if the number of ODMs increases. In the proactive scenario, we use the formula (4) to estimate the rate
of the data delivery by the cloud service. However, we found that the rate of successful delivery began with 90%. The
performance results of the proactive model go down incessantly with the simulation time. In which, the data delivery rate
is influenced by the data collection and data dissemination. In the hybrid model, the successful rate of the data delivery
reaches 91%. The performance simulation performance of this model is better than the performance achieved by the
proactive scenario. As a result, the simulation performances of the previous models prove the potential of our approach
for distributing, automatically and in assured manner, the traffic information by the cloud service.
ReactiveDR =ReactiveDataMsgs
RecievedMsgs ∗100.(3)
ProactiveDR =ProactiveDataMsgs
RecievedMsgs ∗100.(4)
Figure 8 below shows the data delivery performance against the Slotted 1-Persistence Protocol (S1PD),24 Adaptive Data
Delivery Protocol (AddP),25 and Secure Vehicle Protected Communication (SSVC).26 The results show that the hybrid
model provides reliable data dissemination under congestion conditions compared with the SSVC protocol. Alghout SSVC
support is effective by connecting to the cloud for emergency message delivery to vehicles, traditional data dissemination
models in networks affect the dissemination of data. On the other hand, the dissemination of the data from our model is
greater than that of the SSVC protocol due to the control of the network by the cloud services. In addition, the S1PD and
AddP protocols perform poorly compared with our model although these protocols are adaptive for data dissemination.
This means that the S1PD and AddP protocols are not suitable for disseminating cloud data under congestion conditions.
4.2 Bandwidth performance
Additionally, we estimate the bandwidth metric between the cloud infrastructure and the road infrastructure. In which,
the end-to-end connectivity operates 10 Gbps of data transmission. Based on data collection performance and the message
size, Figure 9 shows the required bandwidth for each model by the specifying the message size in kilobytes. The proactive
model requires 3.7 Gbps of bandwidth for long message size (5 KB) and 0.9 Gbps for lower message size (1 KB). The
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FIGURE 7 Data decision delivery rate by the
cloud for each model
FIGURE 8 Data delivery
performance of the proposed algorithm
FIGURE 9 Cloud communication
performances of released data models
reactive model requires 30.8 Mbps over (5 KB) of message size and 8.817 Mbps over lower message size (1 KB). In the
hybrid model, we assess 1.6 Gbps over long message size (5 KB) and 40 Mbps over lower message size (1 KB).
Figure 10 below presents the performance of the proposed algorithm compared with the vehicular dynamic algorithm
(VDA).27 Indeed, the VDA algorithm is proposed to achieve efficient cloud data storage in Vanet-Cloud environments.
This can be done by improving the quality of service (QoS) in terms of bandwidth utilization, power consumption, and
response time. The results show that the bandwidth of the proposed algorithm is greater than that of the VDA algorithm.
This means that the proposed algorithm is dynamically adapted to the cloud data dissemination under congested traffic
flow conditions while the VDA algorithm is used for the bandwidth allocation process.
SAHRAOUI ET AL.13 of 16
FIGURE 10 The bandwidth performance of
the proposed algorithm
FIGURE 11 The cloud service latency over
the V2C and the IEEE 802.11p standards
4.3 Latency measurements
In Figure 11, the latency of the proposed data exchange mechanism has been examined over two communication stan-
dards: the IEEE 802.11p and the Long-Term Evolution (LTE). However, the latency over the LTE is plotted versus the
latency over IEEE 802.11p communication, which is varied from 1 to 16 KB of message size. The simulation is conducted
in order to experiment the previous hybrid model scenario. We observe that the packet size has great impact on latency
time on the previous communication standards. Also, we notice that the latency performance of the hybrid model using
the IEEE 802.11p standard outperforms the latency performance by using the LTE. Consequently, we conclude that the
performances of the proposed data exchange mechanism over LTE offer better performance rather than the IEEE 802.11p
standard.
5DISCUSSION
The proposed approaches are aimed to improve the dissemination of traffic and decision-making data for vehicles. How-
ever, due to the sporadic nature of vehicle networks, intermittent vehicle networks are often frequent because of the high
mobility and distribution of vehicle nodes on the roads. Such a factor will effectively affect data delivery performance. As
a result, the vehicle decision-making process will not be able to significantly increase road safety. While, if the distribution
of vehicles on the road is high, vehicles attempt to make a fast decision by collecting traffic data from their neighbors and
set the distribution criteria accordingly. The amount of data collected and the latency of the network pose serious data
dissemination problems that affect vehicle decision-making, which are essential in a large number of saturated traffic
scenarios.
We have proposed a VANET-Cloud layer to increase the traffic safety and improve network performance and the accu-
racy of the vehicle decision-making during congested conditions. Particularly, the architecture of our traffic management
is based mainly on the cloud infrastructure and the proliferation of the connected sensor network around the cities. The
14 of 16 SAHRAOUI ET AL.
sensor network is used to collect traffic data and the cloud system is used to provide on-demand services (eg, vehicle travel
time) and intelligent services (eg, intelligent signalization system).
The proposed cloud services control the vehicles mobility using V2C communication in order to provide three dissemi-
nation models: reactive, proactive, and hybrid models. The cloud services use these models to define the tolerable reaction
in dynamic traffic scenario. Furthermore, whenever the traffic data are requested by vehicles on the road, the vehicles
may join to the nearest RSU or initiate a request to on-demand cloud services. The potential cloud services decline the
prediction process based on collected data from within the prediction services and replays, in a reactive way, incoming
requests from vehicles dynamically. In addition, traffic information can be broadcast without considering the needs of
the vehicles. Using proactive data dissemination model, intelligent cloud services launch a periodic mechanism to inform
vehicles by periodic traffic data such as warning messages, the nearest location, and unexpected road event. While, if a
road incident is detected by the vehicles on the road, the witnessed vehicles can broadcast EDM to the cloud services.
The data prediction cloud services process annunciate EDM to implement the appropriate decision plan for the vehicles.
Where, the stability of the vehicles distribution in main and arterial roads may be controlled by the cloud service such
as intelligent signal control. Instead of cooperation between vehicles and with base stations to escape such a situation,
cloud services implement the appropriate decision plan without the need to deploy and manage limited vehicle resources
in congested conditions. In such case, the hybrid dissemination model is vital to simplify the prevention and increase the
elasticity of the cloud service in congested conditions.
The purpose of the proposed algorithm is to define the simplest way to exchange data between vehicles, the group of
vehicles, or with cloud services. Considering the traffic data collected by the vehicles and wireless sensor network, the
exchange data mechanism implements such type of data dissemination models and according to the abnormal situations
in the road, as previously described. This mechanism allows vehicles to avoid traffic jams and sudden traffic events based
on downlink and uplink data collection strategies, request on-demand data, decline event description message, receiving
traffic data through TIM, or by periodic messages (PM). Moreover, vehicles can automatically avoid such a situation
through called intelligent signal control systems declined by EDvM. The downlink strategy is used by the cloud services
to collect data from a vehicular network, while vehicles use an uplink strategy to send their data (eg, current position,
source, and destination data) to cloud services.
Simulation results show that the proposed VANET-Cloud layer substantially improves the vehicles decision-making
in congested conditions compared with existing VANET networks. The effectiveness of the proposed data dissemination
mechanism that we propose, in terms of dissemination delay, as compared with the proposed cloud-assisted Message
Downlink Distribution Scheme (CMDS).13. According to the results of the simulation, the CMDS strategy depends on the
distribution of buses on the roads, which means that increasing the distribution of buses will reduce the dissemination
delay in the network. In fact, pre-established bus routes and schedules may vary depending on different urban scenarios.
As a result, the CMDS dissemination strategy is limited to reduce the dissemination delay in different urban scenarios.
By looking more closely at the results of our dissemination mechanism, our approach is not limited to such types of
vehicles. Therefore, the distribution of the vehicles cannot affect the dissemination delay and the cost of the data dissem-
ination depends on the quality of the communication model (V2V, V2I, etc). This proves the effectiveness of the proposed
mechanism to generalize the network performance in different urban scenarios rather than the proposed CMDS strategy.
In addition, we evaluative the scalability of our data dissemination compared with the performances obtained in pre-
vious studies.28-30 Although increasing the distribution of vehicles on the road will improve network connectivity and
reduce packet loss caused by intermittent connectivity, these approaches show that the dissemination of data is effective
in sparse networks. But once the network becomes much dense, the data delivery will be reduced exponentially. Looking
more closely at the performance of the delivery models we propose, the reactive model shows stable performances in pro-
viding data in sparse and dense networks compared with the clustering and probabilistic broadcasting (CPB) scheme,28
Sensor-Enabled Secure Vehicular Communication (SSVC),29 and analysis of car-to-cloud data traffic.30 While data deliv-
ery performance in both proactive and hybrid models is linearly reduced, compared with the exponential reduction by
these approaches as the density of the network becomes larger. It is clear that the performances of data delivery are
preserved thanks to the fuzzy aggregation function and its aggregation power of traffic data.
6CONCLUSION
This paper defines a VANET-Cloud layer aims to deliver countless functionalities to the drivers by the predicting and
disseminating the real-time traffic information such as the travel time, the vehicles speed, and the real status of the traf-
SAHRAOUI ET AL.15 of 16
fic flow. The proposed layer is intended to deliver two types of traffic service: the potential cloud services (on-demand
services) and automatic cloud services. The provided cloud services use the traffic prediction mechanism to tackle inher-
ent problems of the traffic data accuracy like adjusting the traffic parameters in the prediction process. Additionally, the
traffic services use the data exchange mechanism where several types of messages are proposed to disseminate the traf-
fic information between the vehicles and the cloud services. The traffic information is disseminated according to three
models: reactive, proactive, and hybrid models. The reactive data model is interested when on-demand data decision
are requested by the vehicles, while proactive data model is attracted to use periodic messages when new vehicles are
attributed in cloud coverage area. In the simulation stage, the hybrid data dissemination model is useful for more elastic-
ity by involving both reactive and proactive models on situations when an expected event occurs. Thus, our simulation
experiments and results remain in the fact the impact to closeness of the hybrid model in the some way of the reactive
or proactive data dissemination model. According to the simulation results, the VANET-Cloud services promote, in the
congested condition, optimal network performances in terms of data delivery, bandwidth utilization, and latency. Also,
the network performances of the proposed layer by using the data exchange (V2C) mechanism outperform the network
performances over IEEE 802.11p standard.
ACKNOWLEDGMENTS
The proposal of the paper is supported General of Scientific Research and Technological Development (GSRTD), DGRSDT
in frensh the main objective is developing VANETs applications that are “suspected” of spatiotemporal context, so to
generalize the exchange of V2V informations, V2I informations, and even V2X, and support all communication types
(radio, internet, etc) in their activities.
ORCID
Sahraoui Abdelatif https://orcid.org/0000-0001-7254-6828
Makhlouf Derdour https://orcid.org/0000-0001-6622-4355
Nacira Ghoualmi-Zine https://orcid.org/0000-0002-2752-570X
Bouchra Marzak https://orcid.org/0000-0002-5047-7013
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How to cite this article: Abdelatif S, Makhlouf D, Ghoualmi-Zine N, Marzak B. VANET: A novel
service for predicting and disseminating vehicle traffic information. Int J Commun Syst. 2020;e4288.
https://doi.org/10.1002/dac.4288
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