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Resource Scheduling in Vehicular Cloud
Network: A Survey
R. Ruhin Kouser,
Assistant Professor, Department of CSE, Kingston
Engineering College, Vellore, Tamil Nadu, India
ruhinkouser@gmail.com
T.Manikandan,
Professor, Department of ECE, Rajalakshmi
Engineering College, Chennai , India
manik andan.t@rajalakshmi.edu.in
Abstract —In recent ye ars, vehicular cloud computing is
considered as an emerging technology, which focuses on
computing and communication among the vehi cles by
sharing the resources li ke Processor, Central Processing
Unit (CPU) and Random Access Memory (RAM), etc. For
cooperation and safety betwee n vehicles in vehicular cloud
computing the resources are all ocated to vehicles on
demand, which can be done by proper scheduli ng of
resources . S cheduli ng of resources is done in two way
communications i.e. First way is Vehi cular to Vehicul ar
(V2V) communication and the second way is Vehicular to
Infrastructure (V2I) communication. We offered a paper
with comprehensive survey on services of vehicular cloud
computing and resource schedul ing in V2V communication.
In addition this paper focuses on limitations of current
technol ogies and challe nges associated with V2V
communication, also highlighti ng potential solutions.
Keywords: Resources scheduling, V2V, V2I, Peer to Peer
communications.
I. INTRODUCTION
A. Vehicular System
The main difficulties in automotive trans portation
system are accidents, traffic, driving regulations,
breakdown, bad weather, communication, congestion and
delays. Lots of researches were conducted to establish a
network of vehicles named Vehicular ad hoc network
(VANET).VANET is able to avoid the above mentioned
problems and fulfil the specific need of communication
between vehicles; Figure 1 describes using VANET
vehicles are able to share the resources like
computational, storage, bandwidth, QoS etc. [1]. The
probability to avoid traffic accidents and improve road
safety is accomplished through offering reliable and
effective communication support among vehicles via
V2V and road side units via V2I in VANET[3][11].
VANET provides best routing protocol for exchange of
data both in rural and urban areas [4]. This network has
been developed with great interface to have a
communication linkage between the vehicles, road side
units (RSU) and Road side Infrastructure. The Wi-Fi
standards like IEEE 802.11 standard (WLAN) with
routing and gateway are used for communication for the
benefit of drivers [2].Research has also been done to
control congestion when certified vehicles participate in
communication to send and receive messages. The
decrease of channel conges tion is made during V2V
communication by identifying the misbehaving of
vehicles in the road side unit and annuls them. This
creates secure network between only authentic vehicles
for passing messages jointly by roadside unit and vehicle
[5] [11]. Conges tion can als o be avoided by means of
traffic re-routing system. Relating centralized system the
applied hybrid re-routing system identifies the signs of
congestion and increases the user privacy. But VANET
still has network security and co-operative
communication issues [6].
Fig 1: Vehicular ad hoc network
B. Vehicular Cloud
A VCC brings the mobile cloud into a new network
with vehicles and it is named as vehicular cloud. This
network offers access of unlimited computing, storing,
and data resources through internet to compute, store,
download and upload [7]. The amalgamation of cloud
and VANET is Vehicular cloud Network [8][10]. In
VANET still there are underutilized resources,
requirements and services are also lagging. To meet
these requirements and services of VANET application
the co-operation between vehicles is introduced through
cloud network as shown in the figure 2. A VCC is a new
emerging technology which is used by clients to access
the resources on demand and run their application in the
vehicles and provide services. By openly using resources
such as computing, internet, and storage, the Vehicular
cloud computing has a high control over road traffic
management, road protection, road accidents etc. [9].
Fig 2: Vehicular Cloud
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
IEEE Conference Record # 45616; IEEE Xplore ISBN: 978-1-7281-0167-5
978-1-7281-0167-5/19/$31.00 ©2019 IEEE 621
Figure 3 describes the architecture of conventional
based vehicular cloud network. The network which
combines peer to peer network and th e cloud is stated as
peer to peer level cloud. Peer to peer level cloud is used
in the vehicular cloud network to have a communication
between vehicles to vehicle [13][27]. A datacentre is
composed or connected with networked computers and
storage that business or other organization used to
organize process, distribute and store vast amount of data
in the data centre. At least 0.5% of world’s total
electricity is consumed by servers of cloud data centres
and 1.2% when it is factored into equation. Apart from
this servers energy consumption is doubled every 4-6
years according to the survey. This results in large
amount of CO2 produced by burning fossil fuels [14].
The computing resources like (CPU, Memory, RAM etc)
are provide from the data centres which are utilized by
the cloud. Energy consumption of data centres increased
up to 0.8% in 2000 ,1.5% in 2005 and the assessed CO2
fabrication was 116.2 million metric tons in the year
2006[14].
Fig 3: Architecture of Vehicular cloud
C. Service types
There are three layers involves in vehicular cloud
such as cloud as a platform, cloud as a service, and cloud
as an infrastructure. Among this the cloud infrastructure
layer affords the services like computation services,
storage services for vehicular cloud computing. The three
cloud services offered by vehicular cloud are network,
cooperation, and storage as a service given in figure 4.
Inter communication among the neighbour vehicles
using internet is provided by network as a service. Large
number of storage to the vehicle is offered by Storage as
a service.V2V message passing is utilized to deliver
information and services to drivers by Cooperation as a
service. The association of vehicular cloud computing is
made static, when all the vehicles in either shopping mall
or parking areas persists static. For illustration consider a
reputed education institution which provides a good
education to the students. In such big organization many
vehicles of s tudents, faculties and other employees
endure idle in the parking area and also their
computational resource persists un-utilized. And
vehicular cloud is said to be dynamic, when all the
vehicles are traveling with high freedom of movement
and variations in the network, so possibly in the
formation of the vehicular cloud the agility plays a very
important role.[15].
Fig: 4 Service Types
D. Resources in Vehicular Cloud
Resource is a reserved source available to serve
whenever it is needed. Cloud computing act like an
interface between the datacentres and networks where it
dynamically allocates sources and run applications
needed by components. In particular vehicles are the
major components in vehicular cloud. To create a
connection on demand and utilize the resources, the
vehicles are considered with collaboration and
co-operation between them. Resources for vehicular
cloud can be divided into two types computing and
non-computing resources [21].The computing resource
are (CPU, Storage, Memory, RAM, Internet) which is
reserved for vehicles from data centres. Sometimes,
resources are not computable materials, such as data,
bandwidth and etc. Hence, we conclude a remark on the
resources that irrespective of the type of computability
they can be used in cloud.
II. VEHICULA R CLOUDS
A.Vehicular to Infrastructure
The speciality of vehicular to infrastructure is that
the communication model where it can specifically share
the information with the components in the road side unit
(RSU).Also communication is done between
infrastructures to vehicular. The components on the
roadside include RFID, camera, streetlights, Roadside,
parking, traffic lights [12]. Here the communication is
bidirectional and wireless which is shown in Figure 5.
VtoI uses DSRC (Dedicated short range communication)
frequencies to transfer the data. From infrastructure the
components are transferred through ad-hoc networks.
V2I communications can be used for all types of roads
and vehicles. By implementing algorithms data exchange
between vehicles and infrastructure components are
done. This can perform and identify the high risk
situation and give alerts to drivers. The signal phase and
Vehicular Cloud Computing
Application
Organization
Service Types
Urban
Surveillance
Traffic
Management
Disaster
Management
Data
Center
Dynamic
Static
Cooperation
as a service
Network as a
Service
Storage as a
Service
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
IEEE Conference Record # 45616; IEEE Xplore ISBN: 978-1-7281-0167-5
978-1-7281-0167-5/19/$31.00 ©2019 IEEE 622
timing (SPAT) information is the traffic signal system to
pass information to the vehicle in provision to deliver
active safety advisories and warning to drivers [17].
Fig 5: V2V and V2I communication
Vehicular to infrastructure systems contain the parts
like Vehicle on Board Unit or equipment (OBU),
Roadside Unit or Equipment (RSU or RSE), Safe
Communication Channel, etc. In V2I system the OBU
are the vehicle side and are used to define the vehicular
functions performed in the system. The OBU is the
collection of an application processor, GPS, and
interfaces to vehicle systems. It also provides the
interfaces among the vehicles, the vehicles and
RSU.OBU also transmit status messages to other OBU.
Based on its memory and communication capacity the
data snapshots are stored. These old data’s are
overwritten after a period of time, the vehicle data along
with GPS data’s are gathered together as a series for
transmission to the RSU[22].RSU are fixed on the
roadside interfaces and interchanges like petrol stations
with in the range. DSRC or WAVE is radio transceivers
on the RSU and Interface to the V2I communications
network. An interface supports local infrastructure safety
applications using GPS. The Vehicular to Infrastructure
network is able to send the private data and also receive
data. RSU can manage the messages sent from the
vehicle (automotive original equipment manufacturer
(OEM)) and received to the vehicle by priority. The
priorities are set in OBU within its applications and RSU.
Entertainment, public and private network applications
has lowest priority. The application of V2I focus on
SPAT(which has the ability to coordinate the driving
speed ).It is also able to exploit the fuel economy with
less fuel consumption while start and stop using patterns
to traffic light by SPAT[21 ].
Disadvantages
The main disadvantage of V2I is creating
infrastructure on roadside. The problematic ques tion is
who will pay for such an infrastructure in the market of
V2I.And the answer could be valley of death.
Subsequently the roadside infrastructure includes surplus
installation costs . The V2I infrastructure needs to
influence on its large area coverage and needs more
feature augmentations for Vehicle Applications.
B. Vehicular to Vehicular
A vehicle can act as an interface between one
vehicle to other vehicle using dynamic wireless exchange
of data. The data’s are communicated between vehicles
by wireless network. Vehicles exchange information like
speed, location and resources among them. V2V
communication can also be used for enhancing GPS
accuracy, transmitting emergency information like
accidents and also passing the relevant information.
Perhaps V2V communication is more effective in sharing
the messages than the current (OEM) automotive original
equipment manufacturer for s ending and receiving
messages. This technology supports by creating a global
360-degree awarenes s of surrounding coercions and
proximity. To control the crash or threats in the vehicle
communication the right vehicle s oftware or safety
application can use messages from the nearby vehicles.
This technology also provides service like visual, tactile
and audible alerts to warn drivers, So that drivers have
the ability to avoid crashes. These V2V communications
can detect a danger which covers traffic, terrain or
weather. The radars, sensors and cameras are used to
detect and transmit the messages.
The communication between the vehicles takes
place by us ing device called (DSRC) Dedicated Short
range communication .It is a wireless communication
device which works in the 5.9GHz band with a
bandwidth of 75MHz which is only assigned for the
vehicular communication. The range of these devices
may be approximately 1000m.DSRC is also
communicated with road infrastructure as mentioned
earlier. To communicate the road signals and nodes are
installed by roadside.
Advantages
1. V2V decreases the quantity of data transferred from
vehicles to data centre and the resulting costs for
communication over the networks is also lessened.
2. Real time information about the traffic is delivered
from one vehicle to other, where driver receives the
information to reach the desired location.
3. It also able to reduce the traffic jam providing
various road safety systems like Crash avoidance
systems , predicting accident zone, preventing
uns afe driving etc.
4. The s ecurity systems such as anti-theft services,
snatched vehicle tracking services and also provide
diagnostics and maintenance services.
Drawbacks
1. The huge number of vehicles are not supported
simultaneously due to the lessen network frequency
bandwidth
2. Danger of hacking: In vehicular system the hacker
may change the information displayed.
III. Resource Scheduling in Vehicular networks
Resource is kept available in cloud networks
including vehicular cloud network whenever needed.
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
IEEE Conference Record # 45616; IEEE Xplore ISBN: 978-1-7281-0167-5
978-1-7281-0167-5/19/$31.00 ©2019 IEEE 623
Vehicular cloud reserves the resources on demand to
serve the vehicles in the network. A group of vehicles in
the cloud share the computation resources. It is necessary
to schedule the resources bas ed on the collaboration
between vehicles in the cloud network[22].The vehicular
network can manage to have several resources
such as storage , computation , communication among
vehicles with sensing capabilities. These resources of
vehicles are dynamically managed based on
collaboration among Vehicular to Vehicular and
Vehicular to Infrastructure communication [23].
A. Resource Management and Scheme
Efficient distribution of cloud resources among
several users is termed as Resource management .This
technique in cloud is used to design the applications for
computing and allocating the resources with intensive
applications that have different parameters. To the
efficient usage of the computational resources by
different users all the resources are virtualized. A virtual
infrastructure management (VIM) can be used to manage
these virtual resources through multiple physical servers.
Virtual server instances are created for these physical
servers. Hyper visor is used to create multiple instances
by VIM and managed to allocate a virtual server.
Vehicular cloud is crew of vehicles which share
resources in the cloud. This resource must be reserved on
demand among the coordination of vehicles and the
roadside unit(RSU). Managing of resources is very
crucial for this kind of cloud [19] [16]. Tasks that are
implemented by the resource management system
includes managing the resource templates, allocating and
releasing the resources virtually, coordinating, enforcing
usage and security policies, monitoring the operational
conditions. Cloud resource administrators access the
resource management systems functions .Thes e
administrators are employed by the cloud provider and
can directly acces s the resource management system
[16][17].
B. Resource search
Resource search or resource discovery is an act of
searching a suitable service, node or any other computing
elements. Resource discovery protocol is used to search
the resource and also to forward it to other nodes by
sending a search query. Using this search query a success
message with acknowledgment is sent to the node when a
node is found with the resource.
C. Resource Scheduling
Resource Scheduling denotes the algorithm which
decides the procedure to assign resources in each
computing system. To allocate the resources such as
memory, I/O devices, CPU cycles, secondary storage
space, and network bandwidth among the users the
algorithms are designed. In virtual environment service
providers used to deliver and allocate various resources
implementing the resource scheduling algorithms. The
principle is “The resources are very limited so users do
not own the allotted resources ” .In spite they are allotted
by the currently required resources using this scheduling
algorithm. Scheduling algorithms used varies according
to the needs of service provider. This algorithm has no
standards hence mathematical algorithms are being
developed by the vendors [20].
D. Resource Allocation
Lots of business, organizations and even
educational institutions are requesting for the resources
in cloud. Cloud computing provide such resources when
available on demand. The process of assigning the
available resources on demand, by various cloud
applications for rent through internet is called resource
allocation [28]. When resource allocation is not managed
properly the services provided is famished. Hence to
manage the individual module of resources and to solve
the problem, resource provisioning provides the service
provider to manage the resources.
IV. Vehicular to Infrastructure Communication in Cloud
When vehicles share information with the
components on the road side then it is called as vehicle to
infrastructure (v2i) communication model. This model is
wireless communication and bidirectional. The
components include cameras, traffic lights, sensors,
streetlights, parking meters; etc. To transfer the data and
communicate between vehicular and infrastructure
devices, it uses DSRC. Data and information from the
infrastructure components are delivered to vehicles over
an ad hoc network [7].
A. Architecture of Network based on Vehicular Cloud
(VCN)
The (VCN) is a categorized network and it is
illustrated with three tier architecture. This three tier
architecture consists of following levels as s hown in the
figure 6.
Fig: 6 VCN hierarchical networks.
Level 1: Tier 1-Vehicular Cloud (VC)
Level 2: Tier 2 -Infrastructure Cloud(IC)
Level 3: Tier 3- Back End Cloud (BEC)
In level 1, Vehicle resources like storage and
computation are pooled among cluster of vehicles to
increase the efficacy of the vehicular network. Level 2 is
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
IEEE Conference Record # 45616; IEEE Xplore ISBN: 978-1-7281-0167-5
978-1-7281-0167-5/19/$31.00 ©2019 IEEE 624
Infrastructure cloud which is started with road side unit
.This RSU is accessed by the other vehicles provided by
the cloud. Local servers here act as a communication
medium between two different infrastructure clouds. This
is formed only for s hort period of time. Level 3 Back end
cloud is the internet cloud which is one of the biggest
cloud in the vehicular network. This has large number of
resources us ed by vehicles for wide range of storage and
computation [8][27].
V. Vehicular to Vehicular communication in cloud
The data are communicated between vehicles by a
wireless network. Vehicles exchange information like
speed, location, and resources among them. V2V
communication can also be used for enhancing GPS
accuracy, transmitting emergency information like
accidents and also passing the relevant information.
[2][18]. Nowadays cloud provides resources such as
computation and storage etc… for vehicles. Cloud as an
infrastructure is used to provide the information among
the vehicles. This communication is V2V communication
in a cloud. Based on the type of data the data’s are
sensed, classified and stored in the storage unit. Vehicles
utilize the computational resource from the cloud to
analyse the stored data. A group of vehicles within a
cloud access the tools hosted in the cloud platform. The
cloud for these vehicles offers service as a network as
service, cooperation as service and storage as service.
These services are provided on demand and according to
the requirement of the vehicles in the vehicle to vehicle
communication [9].
V2V communication permits the vehicles to
interconnect with neighbouring vehicles, and form a
cloud enabled vehicular network. The cloud assisted
vehicular networks offer low price computational
services to vehicles [25].
V2V are mostly us ed in transmitting emergency
messages, increasing GPS exactness and exchanging
associated information. VRU is used in toll collection,
transmitting localized important data regarding the local
territory and traffic situations and provides access to
transportation system linked services.
A. Peer-to-Peer communications
In this literature survey there are certain works have
spoken based on the s cheduling of resources for
vehicular to vehicular communications using peer to peer
protocol and also compared the results of services
provided by this communications.
Meneguette et.al. [21] proposed a contrivance for
resource s earch and management in vehicular cloud-
connected network. The writers tried implementing this
mechanism without any provision on a roadside unit.
Peer to peer protocol has been adapted to create
relationship and cooperation among vehicles so that it
establishes the connection and provide resource
parameters to the vehicles in the network. The
architecture for this sys tem is distributed into two major
modules resource management and routing. Resource
management is cross-layer module which is respons ible
to overlay the system and also to manage and allocate the
resources which are shared among the vehicles in the
cloud. This done by using a Gnutella protocol. In order to
pick the controller and gateway nodes, the resource is
demanded by the vehicle which is positioned with the
relation of the vehicles in a communication area, where
both vehicles travel in the same direction. Further request
resource and controller management are the common
operations of the entire process. To search and allocate
resources vehicle is used as a parameter. Furthermore,
the authors create gateways to improve the standard of
Gnutella protocol as it can use the same path for a reply
and send the result of a requested mes sage. Authors
adapt to work the same scenario in urban areas in future.
Meneguette et.al.[22] proposed an adaptive and
cooperative resource scheduling mechanism among
vehicles in the vehicular cloud network. This scheduling
mechanism is done in V2V communication based on
peer to peer, where the cloud ensures not to depend on
the cloud-side infrastructure. Vehicles needed to create a
erection to provide a service and manage the resources
using CARESS mechanism in the vehicular cloud
network. It creates an efficient resource scheduling
mechanism which provides service that fulfils the
expectation of drivers and also considers the necessity of
requested service. Associating smart this mechanism
takes the advantage of smart, by using cache mechanism
the authors tried to ease the discovery of resources in the
vehicular cloud (using λ beacons). CARESS cons iders
the type of resource accessible, remotenes s and linking
time between vehicles to order its row in the cache
mechanism (resources available in adjacent and
connected vehicles).For the resource scheduling process;
they used the greedy algorithm which cons iders the time
of resources the vehicles used and also how long it takes
for communication between the vehicles. Their future
work is to operate CARESS in the highway scenarios
developing fault mechanism.
Zheng et al [23] proposed best reckoning and
resource allocation scheme. Using this scheme author
tried to maximize the prize of the VCC system for the
long-term period; this reward is derived based on two
considerations like returns and price of the VCC s ystem.
This scheme is proposed based on the infinite horizon
Semi-Markov decision process (SMDP).To obtain the
optimal scheme the authors defined and analysed the four
state stages of VCC system, which determines the deed
taken under a certain state. They are state space, action
space, reward model and the transition probability
distribution of VCC system. The state of the system is
planned to the resources and request states in the
vehicular cloud this is the stage of state space. The action
space represents the movements of the sys tem which is
used based on the current system. The sum of the reward
is computed and analysed by the cost of the system
which gives the income to the s ystem and also allows to
realistic discounted model. Using the s pecific action the
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
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transition probability state of the s ystem changes to
another state. An iteration algorithm is used to solve
SMDP in order to exploit the long lasting expected total
return of the vehicular cloud. They also stated that
SMDP created sys tem performs additional two allocation
schemes like simulated annealing and greedy allocation
for optimal results.
Meneguette et.al.[24] framed a resource allocation
system established with semi-Markov decision process
(SMDP) in vehicular to vehicular communication system
without any infrastructure or roadside support using peer
to peer protocol. This scheme is proposed to exploit the
prize of the vehicular cloud and also increase the quality
of involvement of vehicles. Vehicles with embedded
resources, cooperation and association among vehicles in
order to dynamically create a vehicular cloud .Vehicular
cloud are framed as a cluster; cluster head controls and
succeeds the cloud. Authors formulated SMDP (Semi
Markov Decision Process) for allocation and
management of resource problem in dynamic cloud and
it is s olved by using an iteration algorithm that is
working by using an ideal plan.
Rong et.al [25] proposed mechanism based on
cooperative resource management in a cloud network.
This mechanism is implemented by sharing the ideal
resources among vehicles that are V2V communication
using peer to peer protocol. Vehicles s hare their
resources like energy, storage and network bandwidth
among each other to protect, s tore, transmit and focused
network. In such a network there is a problem in resource
sharing and bandwidth. So the propos ed work is mainly
focused on bandwidth and computing resource
cooperation. Coalition game technique is used to
improve the QoS of the applications. Here the resource
sharing is done among diverse service providers in cloud
permitted vehicular networks; this sharing is accepted in
two stages. First stage the Service provider estimates the
revenue and adopts either to work alone or it will
integrate the cloud market. Later in the second stage
service provide will either rent or lease its resources to
the other SPs. This mechanism increases the revenue and
also increases the count of users accessing the service.
This method should consider some preferences like: i)
Utility improvement is mentioned by the service provider
participating in a coalition technique, ii)utility is
improved when service provider changes the coalition
like leaving coalition A and joining coalition B and
service provider may work single in order to improve the
utility. The authors implemented a Pareto optimality to
increase and not to decrease their utility.
Jun et al. [26] considered a non-cooperative cloud
resource allocation method mainly to reducing the
calculation time of the Nash Equilibrium Point (NEP)
preferably for urban s cenarios. Gauss seidal iteration
method is used for the resource allocation. The authors
tried to rally the repetition of the flow by precision
control method and also tried modelling the trans ition
behaviour of the vehicle nodes using game theory.
B. Known Res ul ts
S.n
o
Methodology
&Author(s)
Techniq
ue
Results
1
Mechanism for
resource search and
management in the
vehicular cloud-
connected net work.
Meneguett e et.al.
Peer to Peer
technique
0.5 ms search time
to pursue resources
in one hop and 0.9
ms for more than
one. High
availability of
resources about 87%
2
An adaptive and
cooperative resource
scheduling
mechanism.
Meneguett e et.al.
Peer to Peer
technique,
CARESS
mechanism
Highest service ratio
approximately 95%
and 93% of average
service quality wit h
0.6 ms service
delay.
3
Best computat ion
and resource
allocat ion scheme.
Zheng et al
Peer to Peer
technique,
Semi-
Markov
decision
process
Mechanism
7% performance
gain
4
A resource
allocat ion syst em
creat ed on semi-
Markov decision
process (SMDP) in
vehicular to
vehicular.
Meneguett e et.al.
Peer to Peer
technique,
Block rate 20%.
Maximize the
utilization of
available resource.
5
Co-operative
resource
management in a
cloud net work.
Rong et.al
Peer to
peer,
Coalition
game
technique
Improve
exploitation and rise
75% QoS of the
applications t han
that without
cooperation
6
Non-cooperative
cloud resource
allocat ion Jun et al.
Peer to
Peer,
Gauss
seidal
iteration
method
Nash Equilibrium
Point (NEP), guides
the proposal of more
advanced cloud
resource allocation
schemes.
VI. Conclusion
In this paper, we overviewed several works
addressing resources scheduling in vehicular cloud. We
organized the efforts by representing about VANET and
information passing in vehicular adhoc network. Then
depicted taxonomy based on infrastructure, data centre
and service types of vehicular cloud. Further resources of
vehicular cloud and V2V and V2I were described.
Moreover overview on resource search, allocation,
scheduling and management in vehicular networks were
illustrated. We identified that resource scheduling plays a
vital role in vehicular to vehicular cloud network,
scheduling resources in V2V network and maintaining
the availability of resources among the vehicles is a big
challenge. Finally, we reviewed resource scheduling in
V2V cloud network mostly using peer to peer
communications. From the literature, we conclude that
the resource availability and service ratio between the
vehicles in V2V cloud network using peer to peer
communication technique is only up to 87% and 95%
Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019]
IEEE Conference Record # 45616; IEEE Xplore ISBN: 978-1-7281-0167-5
978-1-7281-0167-5/19/$31.00 ©2019 IEEE 626
with 0.6 ms delay. In future, the resource availability and
service ratio in V2V cloud network may improve by
potential graph theory approaches.
References
[1] SabihurRehman , M. Arif Khan, Tanveer A. Zia, LihongZheng.
Vehicular Ad-Hoc Net works (VANETs) - An Overview and
Challenges.Journal of Wireless Networking and Communications.
Journal of Wireless Networking and Communications, 3(3): 29 -38,
2013.
[2] L. Nassar, A. Jundi, K. Golestan, F. Satt ar, F. Karray, M. Kamel, and S.
Boumaiza. Vehicular Ad-hoc Networks(VANET s):Capabilities,
Challenges in Context-Aware P rocessing and Communication
Gateway.Springer-Verlag Berlin Heidelberg, 42–49, 2012.
[3] Elias C. Eze, Sijing Zhang and Enjie Liu. Vehicular Ad Hoc Networks
(VANETs): CurrentStat e, Challenges, Pot entials and Way Forward.
Int ernational Conference on Automation & Computing, Cranfield
University, Bedfordshire, UK, 12-13 September 2014.
[4] MayadaAbdelgadir , Rashid Saeed and AbuaglaBabiker. Vehicular Ad-
hoc Networks (VANETs) Dynamic P erformance Estimation Routing
Model for City Scenarios. IEEE, 08 December 2016 .ISBN: 978-1-
5090-3546-5
[5] SulataMit ra, AtanuMondal. Joint Congestion Cont rol St rategy during
V2V Communicat ion Among Authentic Vehicles in VANET. Springer
Science Business Media New York,November 2014, Volume
79, Issue 1, pp 43–67
[6] Juan (Susan) Pan, IulianSanduPopa, and CristianBorcea. DIVERT: A
Distributed Vehicular Traffic Re-routing System for Congestion
Avoidance. IEEE, 1536-1233, Volume: 16 , Issue: 1 , Jan. 1 2017
[7] Euisin Lee, Eun-Kyu Lee, and Mario Gerla. Vehicular Cloud
Networking:Architecture and Design Principles. IEEE Communications
Magazine,Volume: 52 , Issue: 2 , February 2014
[8] Farhan Ahmad, Muhammad Kazim, and AsmaAdnane.Vehicular Cloud
Networks: Architecture and Securit y. Springer International Publishing
Switzerland, December 2017, Volume 51, Issue 8, pp 907–914
[9] T esnimMekkia, IssamJabrib, AbderrezakRachedic, Maher ben Jemaaa.
Vehicular Cloud Networks: Challenges, Architectures, and Future
Directions.Elsevier, December 21, 2016.Volume 9, Pages 268-280
[10]ChengmingLiy, ShiminGongy, Xiaojie Wang, Lei Wangz,
QingshanJiangy, and Koji Okamurax. Secure and Efficient Cont ent
Distribution in Crowd sourced Vehicular Content-Centric Net working,
IEEE, 2017.VOLUME 6.Page(s): 5727 - 5739
[11]GiulioGrassi, DavidePesavento, Giovanni Pau, Rama Vuyyuru,
Ryuji Wakikawa, Lixia Zhang.VANET via Named Data
Networking.IEEE, 2014.ISBN 978-1-4799-3088-3
[12] Md. Whaiduzzaman ,MehdiSookhak , AbdullahGani , RajkumarBuyya.
A survey on vehicular cloud computing. Journal of Network and
Computer Applications.Elsevier, Volume 40, April 2014, Pages 325-
344
[13] Francisco de AsísLópez-Fuentes, Gerardo García-Rodríguez.
Collaborative Cloud Computing based on P2P Networks. IEEE,
2016.ISBN: 978-1-5090-2461-2
[14] AwadaUchechukwu, Keqiu Li, YanmingShen. Energy Consumption in
Cloud Computing Data,International Journal of Cloud Computing and
Services Science (IJ-CLOSER)June 2014.Vol 3,No.3,
[15] KayhanZrarGhafoor, Kamalrulnizam Abu Bakar, Marwan Aziz
Mohammed, Jaime Lloret . Vehicular Cloud Computing: T rends and
Challenges.IEEE, 2012.Pages: 13
[16] NicolaCordeschi∗, DaniloAmendola, MohammadShojafar,
EnzoBaccarelli. Distributed and adaptive resource management in
Cloud-assisted Cognitive Radio Vehicular Net works with hard
reliability guarantees. Elsevier,Volume 2, Issue 1, January 2015, Pages
1-12
[17] Kakan Chandra Dey ,AnjanRayamajhi,MashrurChowdhury ,
ParthBhavsar ,James Martin. Vehicle-to-vehicle (V2V) and vehicle-to-
infrastructure (V2I)communication in a heterogeneous wireless
network – P erformance evaluation.Elsevier,2016.
[18] Mrs.Vaishali D. KhairnarDr. S.N. Pradhan. V2V Communicat ion
Survey - (WirelessTechnology).Int.J.Computer Technology
&Applications.Vol 3 (1), 370-373,JAN-FEB 2012.
[19] SaadMustafa , Babar Nazir , Amir Hayat , Att a urRehman Khan ,
Sajjad A. Madani. Resource management in cloud computing:
Taxonomy, prospects, and challenges. Elsevier, Volume 47, October
2015, P ages 186 -203.
[20] NeerajMangla, Manpreet Singh, Sanjeev Kumar Rana. Resource
Scheduling In Cloud Environmet: A Survey. Advances in Science and
Technology Research Journal Vol. 10 (30), 2016.
[21] Rodolfo I. Meneguett e, AzzedineBoukerche ,Robson de Grande.
SMART: An Efficient Resource Search and Management Scheme for
Vehicular Cloud-connected System. IEEE, 2016 . ISBN: 978-1-5090-
1328-9
[22] Rodolfo I. Meneguettezy, AzzedineBoukerchey.A Cooperative and
Adaptive Resource Scheduling for Vehicular Cloud.IEEE,
2017.ISBN: 978-1-5386-1629-1
[23] KanZheng, HanlinMeng, PeriklisChatzimisios. An SMDP -Based
Resource Allocation inVehicular Cloud Computing Systems. IEEE,
Volume: 62 , Issue: 12 , Dec. 2015
[24] Rodolfo I. Meneguett e, AzzedineBoukerche, Adinovam H. M.
Pimenta, MessiasMeneguette. A Resource Allocation Scheme Based on
Semi-Markov Decision Process for Dynamic Vehicular
Clouds.IEEE,Volume: 62 , Issue: 12 , Dec. 2015
[25] Rong Yu, Member, Xumin Huang, Jiawen Kang, Jiefei Ding,
SabitaMaharjan. Cooperative Resource Management in Cloud-Enabled
Vehicular Networks.IEEE, Volume: 62 , Issue: 12 , Dec. 2015
[26] Jun Tao, Ziyi Zhang, FuqinFeng, Jian He, YifanXu. Non-cooperat ive
Resource Allocation Scheme for Data Access in VANET Cloud
Environment. IEEE, 2015ISBN: 978-1-4673-8537-4
[27] AzzedineBoukerche, t Rodolfo I. Meneguett e. Vehicular Cloud
Network: A New Challenge forResource Management Based Syst ems.
IEEE, 2017ISSN: 2376-6506
[28] MahmudunNabi, Robert BenkocziSherinAbdelhamid, and Hossam S.
Hassanein. A Utility-based Resource Allocat ion scheme in Cloud-
assisted Vehicular Network Architecture.IEEE, 2015.ISSN: 2164-7038
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