DORA: Server Based VANETs and its Applications
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This document discusses a server-based vehicle communication system called DORA that aims to efficiently allocate network resources for vehicles uploading files to roadside access points. It formulates the problem as a finite-horizon sequential decision process and proposes algorithms like dynamic optimal random access and joint DORA to compute optimal transmission policies for vehicles at single and multiple access points. The performance is evaluated using simulations and shows efficiency over existing solutions. Key aspects covered are the system model, traffic and channel models, distributed medium access control, problem formulation and proposed optimization algorithms.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1432
DORA: SERVER BASED VANETs AND its APPLICATIONS
Gowshika.G1, Thenamuthan.R2, Amalorpavamary.S3,Dhineshkumar.M4
1,3B.E (Student), Department of Electronics and Communication Engineering, AMSEC, Namakkal, India
2,4Asst.Professor, Department of Electronics and Communication Engineering, AMSEC, Namakkal, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In this paper, we study Vehicle-to-vehicle (V2V)
and Vehicle-to-roadside (V2R) communications for vehicles
that aims to upload a file when it is within the APs’ coverage
ranges, where both the channel contention level and
transmission data rate vary over time. Dynamic optimal
random access (DORA) algorithm scheme achieves an upload
ratio 130% and 207% better than theheuristicschemesatlow
and high traffic densities, respectively. The problem with this
DORA is that it provides communication to all nodeswhenone
node request the service, this problem can be avoided by the
same vehicle based algorithm with server based manner. We
evaluate the performance of our system using the ns2
simulation platform and compare our scheme to existing
solutions. The result shows the efficiency and feasibility of our
scheme.
Key Words: medium access control, vehicular ad hoc
networks, dynamic programming, Markov decision
processes, Vehicle-2-Vehicle (V2V), Road Side Unit (RSU).
1.INTRODUCTION :
The concepts of the paper says about VEHICULAR ad hoc
networks (VANETs) enable autonomous data exchanges
among vehicles and road side access points (APs). Going to
implement the same vehicle communication with server
based manner and are essential to various intelligent
transportation system (ITS) applications. VANETs support
various ITS applications through different types of
communication mechanisms, including vehicle-to roadside
(V2R) and vehicle-to-vehicle (V2V)communications[3].V2R
communications involve data transmissions between
vehicular nodes androadsideAPs.V2Vcommunicationsonly
involve data exchanges among vehicular nodes. For both
types, we can further classify the communications as either
single hop or multi-hop. In this paper, we focus on analysing
V2R single-hop uplinktransmissions fromvehiclestoAPs. Due
to the limited communication opportunities between
vehicles and APs, efficient resource allocation (either
centralized or distributed) is crucial for the successful
deployment of V2R ITS applications. In the distributed
setting, the vehicles contendforthechannel fortransmission
based on the applications’ QoS requirements. The scenario
where the data packets are first distributed from the
roadside units (RSUs) to the on board units (OBUs). The
OBUs then bargain with each other for the missing data
packets, and exchange them using Bit Torrent protocol. The
medium accesscontrol (MAC)modulecollectsinformationof
local data traffic, and the routing module finds a path with
the minimum delay. The optimal pricing and bandwidth
reservation of a service provider is obtained using game
theory, and the optimal download policy of an OBU is
obtained using constrained Markov decision processes
distributed in nature and also we aim at designing an
optimal uplink resource allocation scheme in VANETs
analytically in this paper.
Consider the drive-thru scenario, where vehicles pass by
several APs located along a highway and obtain Internet
access for only a limited amount of time. We assume that a
vehicle wants to upload a file when it is within the coverage
ranges of the APs, and needs to pay for the attempts to
access the channel. As both the channel contention level and
achievable data rate vary over time, the vehicle needs to
decide when to transmit by taking into account the required
payment, the application’s QoS requirement, and thelevel of
contention in current and future time slots. Because of the
dynamic nature of the problem, we formulate it as a finite
horizon sequential decision problem and solve it using the
dynamic programming (DP).
1.1 Optimal Access Policy Design:
In the case of a single AP with random vehicular traffic, we
propose a general dynamicoptimal randomaccessalgorithm
to compute the optimal access policy. We further extend the
results to the case of multiple consecutiveAPsandproposea
joint DORA (JDORA) algorithm to compute the optimal
policy.
1.2 Low Complexity Algorithm:
We consider a special yet practically important case of a
single AP with constant data rate. We show that the optimal
policy in this case has a thresholdstructure,whichmotivates
us to propose a low complexity and efficient monotone
DORA algorithm.
2. Proposed Framework
The proposed system model consider a drive-thru scenario
on a highway as shown in Fig. 1, where multiple APs are
installed and connected to a backbone network to provide
Internet services to vehicles within their coverage ranges.](https://image.slidesharecdn.com/irjet-v4i11261-171213070912/85/DORA-Server-Based-VANETs-and-its-Applications-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1433
We focus on a vehicle that wants to upload a singlefileofsize
S when it moves through a segment of this highway with a
set of APs J ={1, . . . , J}, where the vehicles pass through the i
the AP before the jth AP for i < j with i, j ∈ J. We assume that
the jth AP has a transmission radius Rj. We also assume that
the vehicle is connected to at most one AP at a time. If the
coverage areas of the APs are overlapping, then proper
handover between the APs will be performed [6]. For the
ease of exposition, we assume that the APs are set up in a
way that any position in this segment of highway is covered
by an AP. Our work can easily be extended to consider the
settings where the coverage areas of adjacent APs are
isolated from each other
Fig. 1. Drive-thru vehicle-to-roadside (V2R)
communications with multiple APs
2.1 Traffic Model
Let λ denote the average number of vehicles passing by a
fixed AP per unit time. We assume that the number of
vehicles moving into this segment of the highway follows a
Poisson process [7] with a mean arrival rate λ. Let ρ denote
the vehicle density representing the number of vehicles per
unit distance along the road segment, and ν be the speed of
the vehicles. From [8], we have
The relation between the vehicle density ρ and speed ν is
given by the following equation [17]:
where νf is the free-flow speed when the vehicle is moving
on the road without any other vehicles, and ρ max is the
vehicle density during traffic jam. The maximum number of
vehicles that can be accommodated
within the coverage range of the jth AP is given by
2.2 Channel Model
Wireless signal propagations suffer from path loss,
shadowing, and fading. Since the distance between the
vehicle and the AP varies in thedrive-thruscenario, wefocus
on the dominant effect of channel attenuation due to path
loss. The data rate at time slot t is given by
where W is the channel bandwidth, P is the transmit power
of the vehicle, dt is the distance between the vehicle and the
closest AP at time slot t, and γ is the path loss exponent. We
assume that the additive white Gaussian noise has a zero
mean and a power spectral density N0/2.
2.3 Distributed Medium Access Control (MAC)
We consider a slotted MAC protocol, where time is divided
into equal time slots of length Δt. We assume that there is
perfect synchronization between the APs and the vehicles
with the use of global positioning system (GPS) [9].Thetotal
number of time slots that the vehicle stays within the
coverage range of the jth AP is
When the vehicle first enters the coverage range of the jth
AP, it declares the type of its application to the AP. In return,
the jth AP informs the vehicle the channel contention in the
coverage range, data rate in all the time slots in the jth
coverage range, the price qj, and the estimated number of
vehicle departures from the coverage range.
3. Problem Formulation
Here, we formulate the optimal transmission problem of a
single vehicle as a finite-horizon sequential Decision
problem[10]. After that, we describe how to obtain the
optimal transmissionpoliciesinbothsingle-APandmultiple-
AP using finite-horizon sequential Dynamic Programming.
When the traffic pattern can be estimated accurately, we
consider a joint AP optimization.
3.1 Single AP Optimization with Random Vehicular
Traffic.
Since we are considering one AP in this subsection, we drop
the subscript j for simplicity. Although the exact traffic
pattern (i.e., the exact number of vehicles in the coverage
range of the AP in each time slot) is not known, the vehicles
arrive according to a Poisson process with parameter λ.Now](https://image.slidesharecdn.com/irjet-v4i11261-171213070912/85/DORA-Server-Based-VANETs-and-its-Applications-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1434
propose the general dynamic optimal randomaccess(DORA)
algorithm in Algorithm1 to obtain the optimal policy
3.2 Joint AP Optimization with Deterministic Vehicular
Traffic.
Now, consider the optimization problem in a single AP. So,
we extend the result to the case of multiple APs, where we
assume that the traffic pattern (i.e., the exact number of
vehicles in the coverage ranges of the APs in each time slot)
can be estimated accurately. The traffic pattern can be
estimated in various ways, such as by installing a traffic
monitor at a place before the first AP to observe the actual
traffic pattern when the vehicles pass by (e.g., using
computer vision [11] and pattern recognition [12]). If the
traffic flow reaches the steady state the estimation of the
number of vehicles at time can be accurate.
The JDORA algorithm for joint AP optimization is given in
Algorithm 3. In Algorithm 3, the vehicle first needs to obtain
the values of t , ∀ t ∈ T, from the traffic monitor. In the
Planning phase, for each s ∈S and t ∈ T, the optimal decision
rule δ∗t (s,t ) is the action that minimizes the expected total
cost , where the expected total cost ψt (s, t ,a) for all possible
actions is calculated based on vt+1 obtained in theprevious
iteration t+1.Afterthe process is repeated for all t ∈ T and s
∈S, we obtain the optimal policy π ∗ . In the transmission
phase, the transmission decision in each time slot is made
according to the optimal policy π ∗ ,and it follows the MAC
protocol.
Now, we first compare Algorithms 1 and 3 with three
heuristic schemes using the traffic model forboththesingle-
AP and multiple AP scenarios. The three heuristic schemes
that we consider are as follows. The first heuristic scheme is
a greedy algorithm, in whicheachvehiclesendstransmission
requests at all the time slots if its file upload is not complete.
That is, the greedy algorithm aims to maximize the total
uploaded file size. The second heuristic scheme is the
exponential bakeoff algorithm that is similar to the one used
in the IEEE 8 02.11. We have slightly modified it for the
system that we consider as follows. Each vehicle has a
counter, which randomly and uniformly chooses an initial
integer value cnt from the interval [0, cw), where cw is the
contention window size. The value of cntisdecreasedbyone
after each time slot. When cnt = 0, the vehicle will send a
request. If the vehicle has sent a request in a time slot, the
size of cw ∈ [cwmin, cwmax] will change according to the
response from the AP: If an ACK is received from the AP, cw
is set to cwmin. Otherwise, cw is doubled until it reaches
cwmax. For the DORA, JDORA, greedy, and exponential
schemes, we assume that the APs allow the vehicles to share
the channel with an equal probability. Therefore, pt
succ=1/nt.
The third heuristic scheme is the MAC protocol in the multi-
carrier burst contention (MCBC) scheme [13]. Similar to the
greedy scheme, a vehicle will send a request if it has data to
send in each time slot. However, the vehicles need to
undergo K rounds of contention in each time slot. First, in
round r, a vehicle survives the contention with probability
pr. Each of these vehicles will choose a random integer in {1,
. . ., F}. Vehicles that have chosen the largest number can
proceed to round r +1. The transmissionissuccessful ifthere
is only one vehicle left in round K. Otherwise, packet
collision will occur. For the evaluations of all the schemes,
we use the convex self-incurred penalty function.
4. Figures and the Tables
In this section, the performance of Server Based DORA in
VANET and its Applications is evaluated using Network
Simulator. The performance of V2R and V2V is compared
with the various parameters. This analysis can be obtained
by varying the parameters such as throughput, drop
performance, packet delay andpacketdelivery ratiowiththe
simulation time
Fig 2: Packet delivery ratio
The results are demonstrated using more nodes. Here Fig2
represents the Packet Delivery Ratio (PDR). Fig 2 saysabout
both existing system and the proposedsystem.Theredcolor
explain about the PDR of existing system and Green color
explain about the (PDR) proposed system. Here the
proposed system says the constant level of delay
performance. Next, we are going to see about throughput
performance of V2R and V2V in DORA concepts..
Fig 3 Throughput performance
While comparing the throughput, V2V has been increasing
throughput. As a result, a larger file size is uploaded to
reduce the penalty. Depending on the QoS requirements of
different applications, different valuesshouldbechosenthat](https://image.slidesharecdn.com/irjet-v4i11261-171213070912/85/DORA-Server-Based-VANETs-and-its-Applications-3-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1435
tradeoff the total uploaded file size and total payment to the
AP by a different degree.
Fig 4 Drop performance
Next, we going to see the drop performance of proposedand
existing systems. Since the DORA algorithm takes into
account the varying channel contentionlevel anddata ratein
determining the transmission policy,it is cost effectiveand
achievesthe highestuploadratio. In Fig. 8, we canseethatthe
JDORA scheme with perfect estimation achieves the highest
upload ratio. In particular, it achieves an upload ratio 130%
and 207% better than the exponential backoffschemeatlow
and high traffic density
Fig.5 Delay performance
Finally, we going to see about delay performance of existing
and proposed systems. Here the delay has beendecreased in
V2V communication. Fig 5. Upload ratio versus traffic
density ρ for file size S = 500 Mbits with five APs. The JDORA
scheme with perfect estimation to achieves the highest
upload ratio as compared with three other heuristic
schemes. Moreover, a lower upload ratio is achieved when
the precision of the estimation reduces
5. Conclusion
The paper has implemented by using same vehicle with
server based manner in V2V and V2R communication.
Vehicles to provides communication between requested
nodes and Do not give a chance to data drop and Easy to
manipulate data-base.Then, uplink transmission from a
vehicle to the APs in a dynamic drive-thru scenario, where
both the channel contention level and data rate vary over
time. Depending on the applications’ QoS requirements, the
vehicle can achieve different levels of trade off between the
total uploaded file size and the total payment to the APs by
tuning the self-incurred penalty and Path Trafficdensitywill
be enhanced.
References
[1] H. Hartenstein and K. P. Laberteaux, “A tutorial survey
on vehicular ad hoc networks,” IEEE Commun.Mag.,vol.
46, no. 6, pp. 164–171, June 2008.
[2] Y. Toor, P. M¨uhlethaler, A. Laouiti, and A. de La
Fortelle, “Vehicle ad hoc networks: Applications and
related technical issues,” IEEE CommunicationsSurveys
& Tutorials, vol. 10, no. 3, pp. 74–88, third quarter 2008.
[3] D. Niyato, E. Hossain, and P. Wang, “Optimal channel
access management with QoS support for cognitive
vehicular networks,” IEEETrans.MobileComputing,vol.
10, no. 5, pp. 573–591, Apr. 2011.
[4] D. Hadaller, S. Keshav, and T. Brecht, “MV-MAX:
Improving wireless infrastructure access for multi-
vehicular communication,” in Proc. ACM SIGCOMM
Workshop, Pisa, Italy, Sept. 2006.
[5] J. Ott and D. Kutscher, “Drive-thru Internet: IEEE
802.11b for automobile users,” in Proc. IEEE INFOCOM,
Hong Kong, China, Mar. 2004.
[6] J. Choi and H. Lee, “Supporting handover in an IEEE
802.11p-based wireless access system,” in Proc. ACM
International Workshop on VANETs, Chicago, IL, Sept.
2010.
Characters VALUE
Number of APs J 1, 5
AP’s transmission radius R 100 m
Free-flow speed νf 110 km/h
Vehicle jam density ρmax 100 veh/km
Duration of a time slot Δt 0.02 sec
Duration for data transmission in a time
slot Δt data
0.018 sec
Channel bandwidth W 20 MHz
Transmit signal-to-noise ratio P/N0 W 60 dB
Path loss exponent γ 3
Payment per time slot q 1
Contention window cw ∈ [cwmin,cwmax] [1, 8]
MCBC parameter K (used in[11]) 3
MCBC parameter [p1 p2 ,p3 ] (used in
[11])
12, 0.77, 0
MCBC parameter F (used in[11] 15](https://image.slidesharecdn.com/irjet-v4i11261-171213070912/85/DORA-Server-Based-VANETs-and-its-Applications-4-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1436
[7] R. J. Cowan, “Useful headway models,” Transportation
Research, vol.9, no. 6, pp. 371–375, Dec. 1975.
[8] Y. L. Morgan, “Notes on DSRC & WAVE standards suite:
Its architecture, design, and characteristics,” IEEE
Commun. Surveys & Tutorials, vol. 12, no. 4, pp. 504–
518, fourth quarter 2010.
[9]. L. Puterman, Markov Decision Processes: Discrete
Stochastic Dynamic Programming. New York, NY: John
Wiley and Sons, 2005.
BIOGRAPHIES
G. GOWSHIKA is persuing B.E. in the
discipline of Electronics and Communication
Engineering in Annai Mathammal Sheela
Engineering College at Namakkal
R.THENAMUTHAN is currently working as
an Assistant Professor in the Department of
Electronics and CommunicationEngineering
at Annai Mathammal Sheela Engineering
College at Namakkal. He received his B.E. -
Electronics and CommunicationEngineering
S.AMALORPAVAMARY is persuing B.E, in
the discipline of Electronics and
Communication Engineering in Annai
Mathammal Sheela Engineering College at
Namakkal
M. Dhineshkumar is currently working as
an Assistant Professor in the Departmentof
Electronics and Communication
Engineering at Annai Mathammal Sheela
Engineering College at Namakkal He
received his B.E. - Electronics and
Communication Engineering in Annai
Mathammal Sheela Engineering College under Anna
University, Chennai and M.E. – Communication Systems in
Pavendar Bharathidasan College of Engineering and
Technology at Trichy
in Annai Mathammal Sheela EngineeringCollegeunderAnna
University, Chennai and M.E. – Communication Systems in
M.Kumarasamy College of Engineering and MBA in
Annamalai University.](https://image.slidesharecdn.com/irjet-v4i11261-171213070912/85/DORA-Server-Based-VANETs-and-its-Applications-5-320.jpg)
DORA: Server Based VANETs and its Applications
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1432 DORA: SERVER BASED VANETs AND its APPLICATIONS Gowshika.G1, Thenamuthan.R2, Amalorpavamary.S3,Dhineshkumar.M4 1,3B.E (Student), Department of Electronics and Communication Engineering, AMSEC, Namakkal, India 2,4Asst.Professor, Department of Electronics and Communication Engineering, AMSEC, Namakkal, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this paper, we study Vehicle-to-vehicle (V2V) and Vehicle-to-roadside (V2R) communications for vehicles that aims to upload a file when it is within the APs’ coverage ranges, where both the channel contention level and transmission data rate vary over time. Dynamic optimal random access (DORA) algorithm scheme achieves an upload ratio 130% and 207% better than theheuristicschemesatlow and high traffic densities, respectively. The problem with this DORA is that it provides communication to all nodeswhenone node request the service, this problem can be avoided by the same vehicle based algorithm with server based manner. We evaluate the performance of our system using the ns2 simulation platform and compare our scheme to existing solutions. The result shows the efficiency and feasibility of our scheme. Key Words: medium access control, vehicular ad hoc networks, dynamic programming, Markov decision processes, Vehicle-2-Vehicle (V2V), Road Side Unit (RSU). 1.INTRODUCTION : The concepts of the paper says about VEHICULAR ad hoc networks (VANETs) enable autonomous data exchanges among vehicles and road side access points (APs). Going to implement the same vehicle communication with server based manner and are essential to various intelligent transportation system (ITS) applications. VANETs support various ITS applications through different types of communication mechanisms, including vehicle-to roadside (V2R) and vehicle-to-vehicle (V2V)communications[3].V2R communications involve data transmissions between vehicular nodes androadsideAPs.V2Vcommunicationsonly involve data exchanges among vehicular nodes. For both types, we can further classify the communications as either single hop or multi-hop. In this paper, we focus on analysing V2R single-hop uplinktransmissions fromvehiclestoAPs. Due to the limited communication opportunities between vehicles and APs, efficient resource allocation (either centralized or distributed) is crucial for the successful deployment of V2R ITS applications. In the distributed setting, the vehicles contendforthechannel fortransmission based on the applications’ QoS requirements. The scenario where the data packets are first distributed from the roadside units (RSUs) to the on board units (OBUs). The OBUs then bargain with each other for the missing data packets, and exchange them using Bit Torrent protocol. The medium accesscontrol (MAC)modulecollectsinformationof local data traffic, and the routing module finds a path with the minimum delay. The optimal pricing and bandwidth reservation of a service provider is obtained using game theory, and the optimal download policy of an OBU is obtained using constrained Markov decision processes distributed in nature and also we aim at designing an optimal uplink resource allocation scheme in VANETs analytically in this paper. Consider the drive-thru scenario, where vehicles pass by several APs located along a highway and obtain Internet access for only a limited amount of time. We assume that a vehicle wants to upload a file when it is within the coverage ranges of the APs, and needs to pay for the attempts to access the channel. As both the channel contention level and achievable data rate vary over time, the vehicle needs to decide when to transmit by taking into account the required payment, the application’s QoS requirement, and thelevel of contention in current and future time slots. Because of the dynamic nature of the problem, we formulate it as a finite horizon sequential decision problem and solve it using the dynamic programming (DP). 1.1 Optimal Access Policy Design: In the case of a single AP with random vehicular traffic, we propose a general dynamicoptimal randomaccessalgorithm to compute the optimal access policy. We further extend the results to the case of multiple consecutiveAPsandproposea joint DORA (JDORA) algorithm to compute the optimal policy. 1.2 Low Complexity Algorithm: We consider a special yet practically important case of a single AP with constant data rate. We show that the optimal policy in this case has a thresholdstructure,whichmotivates us to propose a low complexity and efficient monotone DORA algorithm. 2. Proposed Framework The proposed system model consider a drive-thru scenario on a highway as shown in Fig. 1, where multiple APs are installed and connected to a backbone network to provide Internet services to vehicles within their coverage ranges.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1433 We focus on a vehicle that wants to upload a singlefileofsize S when it moves through a segment of this highway with a set of APs J ={1, . . . , J}, where the vehicles pass through the i the AP before the jth AP for i < j with i, j ∈ J. We assume that the jth AP has a transmission radius Rj. We also assume that the vehicle is connected to at most one AP at a time. If the coverage areas of the APs are overlapping, then proper handover between the APs will be performed [6]. For the ease of exposition, we assume that the APs are set up in a way that any position in this segment of highway is covered by an AP. Our work can easily be extended to consider the settings where the coverage areas of adjacent APs are isolated from each other Fig. 1. Drive-thru vehicle-to-roadside (V2R) communications with multiple APs 2.1 Traffic Model Let λ denote the average number of vehicles passing by a fixed AP per unit time. We assume that the number of vehicles moving into this segment of the highway follows a Poisson process [7] with a mean arrival rate λ. Let ρ denote the vehicle density representing the number of vehicles per unit distance along the road segment, and ν be the speed of the vehicles. From [8], we have The relation between the vehicle density ρ and speed ν is given by the following equation [17]: where νf is the free-flow speed when the vehicle is moving on the road without any other vehicles, and ρ max is the vehicle density during traffic jam. The maximum number of vehicles that can be accommodated within the coverage range of the jth AP is given by 2.2 Channel Model Wireless signal propagations suffer from path loss, shadowing, and fading. Since the distance between the vehicle and the AP varies in thedrive-thruscenario, wefocus on the dominant effect of channel attenuation due to path loss. The data rate at time slot t is given by where W is the channel bandwidth, P is the transmit power of the vehicle, dt is the distance between the vehicle and the closest AP at time slot t, and γ is the path loss exponent. We assume that the additive white Gaussian noise has a zero mean and a power spectral density N0/2. 2.3 Distributed Medium Access Control (MAC) We consider a slotted MAC protocol, where time is divided into equal time slots of length Δt. We assume that there is perfect synchronization between the APs and the vehicles with the use of global positioning system (GPS) [9].Thetotal number of time slots that the vehicle stays within the coverage range of the jth AP is When the vehicle first enters the coverage range of the jth AP, it declares the type of its application to the AP. In return, the jth AP informs the vehicle the channel contention in the coverage range, data rate in all the time slots in the jth coverage range, the price qj, and the estimated number of vehicle departures from the coverage range. 3. Problem Formulation Here, we formulate the optimal transmission problem of a single vehicle as a finite-horizon sequential Decision problem[10]. After that, we describe how to obtain the optimal transmissionpoliciesinbothsingle-APandmultiple- AP using finite-horizon sequential Dynamic Programming. When the traffic pattern can be estimated accurately, we consider a joint AP optimization. 3.1 Single AP Optimization with Random Vehicular Traffic. Since we are considering one AP in this subsection, we drop the subscript j for simplicity. Although the exact traffic pattern (i.e., the exact number of vehicles in the coverage range of the AP in each time slot) is not known, the vehicles arrive according to a Poisson process with parameter λ.Now
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1434 propose the general dynamic optimal randomaccess(DORA) algorithm in Algorithm1 to obtain the optimal policy 3.2 Joint AP Optimization with Deterministic Vehicular Traffic. Now, consider the optimization problem in a single AP. So, we extend the result to the case of multiple APs, where we assume that the traffic pattern (i.e., the exact number of vehicles in the coverage ranges of the APs in each time slot) can be estimated accurately. The traffic pattern can be estimated in various ways, such as by installing a traffic monitor at a place before the first AP to observe the actual traffic pattern when the vehicles pass by (e.g., using computer vision [11] and pattern recognition [12]). If the traffic flow reaches the steady state the estimation of the number of vehicles at time can be accurate. The JDORA algorithm for joint AP optimization is given in Algorithm 3. In Algorithm 3, the vehicle first needs to obtain the values of t , ∀ t ∈ T, from the traffic monitor. In the Planning phase, for each s ∈S and t ∈ T, the optimal decision rule δ∗t (s,t ) is the action that minimizes the expected total cost , where the expected total cost ψt (s, t ,a) for all possible actions is calculated based on vt+1 obtained in theprevious iteration t+1.Afterthe process is repeated for all t ∈ T and s ∈S, we obtain the optimal policy π ∗ . In the transmission phase, the transmission decision in each time slot is made according to the optimal policy π ∗ ,and it follows the MAC protocol. Now, we first compare Algorithms 1 and 3 with three heuristic schemes using the traffic model forboththesingle- AP and multiple AP scenarios. The three heuristic schemes that we consider are as follows. The first heuristic scheme is a greedy algorithm, in whicheachvehiclesendstransmission requests at all the time slots if its file upload is not complete. That is, the greedy algorithm aims to maximize the total uploaded file size. The second heuristic scheme is the exponential bakeoff algorithm that is similar to the one used in the IEEE 8 02.11. We have slightly modified it for the system that we consider as follows. Each vehicle has a counter, which randomly and uniformly chooses an initial integer value cnt from the interval [0, cw), where cw is the contention window size. The value of cntisdecreasedbyone after each time slot. When cnt = 0, the vehicle will send a request. If the vehicle has sent a request in a time slot, the size of cw ∈ [cwmin, cwmax] will change according to the response from the AP: If an ACK is received from the AP, cw is set to cwmin. Otherwise, cw is doubled until it reaches cwmax. For the DORA, JDORA, greedy, and exponential schemes, we assume that the APs allow the vehicles to share the channel with an equal probability. Therefore, pt succ=1/nt. The third heuristic scheme is the MAC protocol in the multi- carrier burst contention (MCBC) scheme [13]. Similar to the greedy scheme, a vehicle will send a request if it has data to send in each time slot. However, the vehicles need to undergo K rounds of contention in each time slot. First, in round r, a vehicle survives the contention with probability pr. Each of these vehicles will choose a random integer in {1, . . ., F}. Vehicles that have chosen the largest number can proceed to round r +1. The transmissionissuccessful ifthere is only one vehicle left in round K. Otherwise, packet collision will occur. For the evaluations of all the schemes, we use the convex self-incurred penalty function. 4. Figures and the Tables In this section, the performance of Server Based DORA in VANET and its Applications is evaluated using Network Simulator. The performance of V2R and V2V is compared with the various parameters. This analysis can be obtained by varying the parameters such as throughput, drop performance, packet delay andpacketdelivery ratiowiththe simulation time Fig 2: Packet delivery ratio The results are demonstrated using more nodes. Here Fig2 represents the Packet Delivery Ratio (PDR). Fig 2 saysabout both existing system and the proposedsystem.Theredcolor explain about the PDR of existing system and Green color explain about the (PDR) proposed system. Here the proposed system says the constant level of delay performance. Next, we are going to see about throughput performance of V2R and V2V in DORA concepts.. Fig 3 Throughput performance While comparing the throughput, V2V has been increasing throughput. As a result, a larger file size is uploaded to reduce the penalty. Depending on the QoS requirements of different applications, different valuesshouldbechosenthat
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1435 tradeoff the total uploaded file size and total payment to the AP by a different degree. Fig 4 Drop performance Next, we going to see the drop performance of proposedand existing systems. Since the DORA algorithm takes into account the varying channel contentionlevel anddata ratein determining the transmission policy,it is cost effectiveand achievesthe highestuploadratio. In Fig. 8, we canseethatthe JDORA scheme with perfect estimation achieves the highest upload ratio. In particular, it achieves an upload ratio 130% and 207% better than the exponential backoffschemeatlow and high traffic density Fig.5 Delay performance Finally, we going to see about delay performance of existing and proposed systems. Here the delay has beendecreased in V2V communication. Fig 5. Upload ratio versus traffic density ρ for file size S = 500 Mbits with five APs. The JDORA scheme with perfect estimation to achieves the highest upload ratio as compared with three other heuristic schemes. Moreover, a lower upload ratio is achieved when the precision of the estimation reduces 5. Conclusion The paper has implemented by using same vehicle with server based manner in V2V and V2R communication. Vehicles to provides communication between requested nodes and Do not give a chance to data drop and Easy to manipulate data-base.Then, uplink transmission from a vehicle to the APs in a dynamic drive-thru scenario, where both the channel contention level and data rate vary over time. Depending on the applications’ QoS requirements, the vehicle can achieve different levels of trade off between the total uploaded file size and the total payment to the APs by tuning the self-incurred penalty and Path Trafficdensitywill be enhanced. References [1] H. Hartenstein and K. P. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE Commun.Mag.,vol. 46, no. 6, pp. 164–171, June 2008. [2] Y. Toor, P. M¨uhlethaler, A. Laouiti, and A. de La Fortelle, “Vehicle ad hoc networks: Applications and related technical issues,” IEEE CommunicationsSurveys & Tutorials, vol. 10, no. 3, pp. 74–88, third quarter 2008. [3] D. Niyato, E. Hossain, and P. Wang, “Optimal channel access management with QoS support for cognitive vehicular networks,” IEEETrans.MobileComputing,vol. 10, no. 5, pp. 573–591, Apr. 2011. [4] D. Hadaller, S. Keshav, and T. Brecht, “MV-MAX: Improving wireless infrastructure access for multi- vehicular communication,” in Proc. ACM SIGCOMM Workshop, Pisa, Italy, Sept. 2006. [5] J. Ott and D. Kutscher, “Drive-thru Internet: IEEE 802.11b for automobile users,” in Proc. IEEE INFOCOM, Hong Kong, China, Mar. 2004. [6] J. Choi and H. Lee, “Supporting handover in an IEEE 802.11p-based wireless access system,” in Proc. ACM International Workshop on VANETs, Chicago, IL, Sept. 2010. Characters VALUE Number of APs J 1, 5 AP’s transmission radius R 100 m Free-flow speed νf 110 km/h Vehicle jam density ρmax 100 veh/km Duration of a time slot Δt 0.02 sec Duration for data transmission in a time slot Δt data 0.018 sec Channel bandwidth W 20 MHz Transmit signal-to-noise ratio P/N0 W 60 dB Path loss exponent γ 3 Payment per time slot q 1 Contention window cw ∈ [cwmin,cwmax] [1, 8] MCBC parameter K (used in[11]) 3 MCBC parameter [p1 p2 ,p3 ] (used in [11]) 12, 0.77, 0 MCBC parameter F (used in[11] 15
- 5. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1436 [7] R. J. Cowan, “Useful headway models,” Transportation Research, vol.9, no. 6, pp. 371–375, Dec. 1975. [8] Y. L. Morgan, “Notes on DSRC & WAVE standards suite: Its architecture, design, and characteristics,” IEEE Commun. Surveys & Tutorials, vol. 12, no. 4, pp. 504– 518, fourth quarter 2010. [9]. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY: John Wiley and Sons, 2005. BIOGRAPHIES G. GOWSHIKA is persuing B.E. in the discipline of Electronics and Communication Engineering in Annai Mathammal Sheela Engineering College at Namakkal R.THENAMUTHAN is currently working as an Assistant Professor in the Department of Electronics and CommunicationEngineering at Annai Mathammal Sheela Engineering College at Namakkal. He received his B.E. - Electronics and CommunicationEngineering S.AMALORPAVAMARY is persuing B.E, in the discipline of Electronics and Communication Engineering in Annai Mathammal Sheela Engineering College at Namakkal M. Dhineshkumar is currently working as an Assistant Professor in the Departmentof Electronics and Communication Engineering at Annai Mathammal Sheela Engineering College at Namakkal He received his B.E. - Electronics and Communication Engineering in Annai Mathammal Sheela Engineering College under Anna University, Chennai and M.E. – Communication Systems in Pavendar Bharathidasan College of Engineering and Technology at Trichy in Annai Mathammal Sheela EngineeringCollegeunderAnna University, Chennai and M.E. – Communication Systems in M.Kumarasamy College of Engineering and MBA in Annamalai University.