research article ijs: an intelligent junction selection...

Research ArticleIJS An Intelligent Junction Selection Based Routing Protocol forVANET to Support ITS Services

Sourav Kumar Bhoi and Pabitra Mohan Khilar

Department of Computer Science and Engineering National Institute of Technology Rourkela 769008 India

Correspondence should be addressed to Sourav Kumar Bhoi souravbhoigmailcom

Received 26 June 2014 Accepted 14 September 2014 Published 29 October 2014

Academic Editor Francisco W S Lima

Copyright copy 2014 S K Bhoi and P M Khilar This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Selecting junctions intelligently for data transmission provides better intelligent transportation system (ITS) services The mainproblem in vehicular communication is high disturbances of link connectivity due to mobility and less density of vehicles Iflink conditions are predicted earlier then there is a less chance of performance degradation In this paper an intelligent junctionselection based routing protocol (IJS) is proposed to transmit the data in a quickest path in which the vehicles aremostly connectedand have less link connectivity problem In this protocol a helping vehicle is set at every junction to control the communication bypredicting link failures or network gaps in a route Helping vehicle at the junction produces a score for every neighboring junctionto forward the data to the destination by considering the current traffic information and selects that junction which has minimumscore IJS protocol is implemented and compared with GyTAR A-STAR and GSR routing protocols Simulation results show thatIJS performs better in terms of average end-to-end delay network gap encounter and number of hops

1 Introduction

Vehicular ad hoc network (VANET) is an advance wirelesscommunication network to provide safety to the passengersand drivers [1ndash5] Due to the high population of vehiclesin the city areas the level of the accident increases Thisreduces the safety level for passengers and drivers in the cityenvironment So there should be a communication betweenthe vehicles so that a vehicle which encounters an accidentcan send a warning message to the fellow vehicles to pre-vent the vehicles from an accident VANET provides manytypes of applications to the users by using ITS services[6 7] Vehicles provide services [8] like P2P communicationmedia downloading safety systems vehicular cloud com-puting e-marketing and so forth VANET uses many typesof communication like vehicle-to-vehicle communication(V2V) vehicle-to-infrastructureinfrastructure-to-vehiclecommunication (V2II2V) and infrastructure-to-infrastruc-ture (I2I) communication The architecture of VANET con-sists of road side units (RSUs) which acts as the infrastructureto communicate with the vehicles and central authority (CA)Many projects are developed and implemented in USA and

Japan and the European nations like WAVE IVI VSCASV-2 C2C-CC Fleetnet NoW iTETRIS and so forth toprovide ITS services to the users [1 3] The main objective ofthese projects is to provide safety applications to the passen-gers and drivers

VANET mainly suffers from link connectivity problemdue to high mobility and less density of vehicles in an area[9] Drivers are of many types in the city areas like aggressivedrivers slow drivers and moderate drivers [5 10] Due tothe speed of these drivers there is high mobility which leadsto network gaps Network gap is a zone where a vehicle isunable to communicate with its forward positioning vehiclesThis is called link connectivity problem where no link isavailable between the vehicles to communicate As topologyfrequently changes topology based routing protocols aredifficult to implement on VANET environment So positionbased routing protocols are used to send the data to thevehicles by knowing their location

The main objective of this paper is to select the mostconnected route which has less link connectivity problems ornetwork gaps This enhances the performance of the system

Hindawi Publishing CorporationInternational Scholarly Research NoticesVolume 2014 Article ID 653131 14 pageshttpdxdoiorg1011552014653131

2 International Scholarly Research Notices

by reducing the delay The main contribution in this paper isas follows

(1) An IJS routing protocol is proposed to transmit thedata quickly to the destination 119863 in the mostly con-nected path which has less number of network gapsAn HV is set at every junction to predict the linkfailures (network gaps) between the vehicles in a route(between two neighboring junctions)

(2) HV calculates the score Score for every neighboringjunction by predicting the delay between the twojunctions and predicting the link connectivity statusbetween the vehicles in a logical route Routereference(logical route from one junction to another) HVselects the junction which has a minimum Scoreand forwards the data in that direction This processcontinues until last junction 119869last (junction nearer to119863) is reached and then data is sent in the route of 119863through the vehicles

The paper is organized as follows Section 2 presents therelated works which discuss the standard routing protocolsproposed for forwarding the data to destination Section 3presents the preliminary part in which three basic models arediscussed which are the key elements for IJS routing protocolSection 4 presents the IJS routing protocol where systemmodel and functionality of the model are described Section5 presents the analysis of the proposed IJS routing protocolSection 6 presents the simulation part where the protocols areimplemented and compared to identify the performance Atlast we concluded in Section 7

2 Related Works

Many works are proposed to provide efficient routing pro-tocols for VANET to send the data quickly from the sourceto the destination [3 11 12] We are motivated by an efficientjunction routing protocol known asGyTARproposed by Jerbiet al [13] to send the data efficiently through the junctions Ascore is calculated for every neighboring junction by knowingthe current traffic conditions This protocol performs betterbecause the vehicles near the junctions are aware of the trafficconditions Cell data packet (CDP) is usedmainly to send thedensity information to the junctions But there may be manynetwork gaps generated between the junctions by which theCDP information is not updated at the current time Thismay generate a false score calculation The congestion in thenetwork is more due to high CDP packet exchange

A-STAR [14] is a junction routing protocol in which thedata packet is transmitted in a predefined path which consistsof buses and vehicles These junctions have higher weightagethan the normal junctions So the vehicles always prefer tosend the data through the densified regions But by usingthese routes there may be delay in reaching the destinationbecause these types of routes exist in some areas There maybe less density in these areas which leads to higher delay

GSR routing protocol [14] is a position based routingprotocol which sends the data in the shortest path route to119863 This suffers from link connectivity problem due to lack

of information about the traffic conditions ahead This alsoincreases the delay

GPSR [15] is a position based routing protocol whichforwards the data by selecting the vehicle which is nearer tothe destination To overcome the local optimum problem itworks in perimeter mode Due to the lack of informationabout the current traffic conditions it sometimes goes toperimeter mode This degrades the performance of thenetwork

GPCR [16] is a routing protocol which chooses a coor-dinator node to forward the data to the destination Afterforwarding the data through the street vehicles choose acoordinator node at the junction When the density of thevehicles in a region is low GPCR suffers from the networkgap problem It does not use current traffic information tochoose the best path to the destination

Chen et al [17] proposed a diagonal-intersection-basedrouting protocol (DIR) in which the source forwards the datathrough the diagonal junctions The path to the diagonaljunction is selected by finding a delay and the path with lessdelay is selected as the path to reach the diagonal junctionThis process continues until destination is reached Thisautoadjustability enhances the performance of the system

Bernsen and Manivannan [18] proposed a reliable inter-vehicular routing (RIVER) protocol by considering the cur-rent traffic information It assigns a reliability rating to thestreet edges and forwards the data according to the ratings Itshares the reliability information with other vehicles to makeother vehicles aware of the current traffic conditions Thisincreases the performance of the system by selecting the bestpossible routes to the destination

Eiza and Ni [19] proposed a graph based reliable routingprotocol in which evolving graph theory is used to forwardthe data to the destination in a highway scenario Thisprotocol is mainly proposed to provide QoS in routing Itprovides better average end-to-end delay packet deliveryratio and lower link failure rate

Saleet et al [20] proposed an intersection-based geo-graphical routing protocol (IGRP) to send the data effectivelythrough the junction to reach the internet gateway Thejunctions are selected on the basis of delay QoS bandwidthusage connectivity and error rate

Bilal et al [21] proposed an enhanced-GyTAR routingprotocol to send the data through the intelligent junctions byfinding a score But this protocol only considers the densityand speed of the vehicles It does not know about the con-nectivity of the vehicles CDP packet is used for connectivityinformation but due to network gaps the information is notupdated which leads to false score calculation

Taleb et al [22] proposed a stable routing protocol wherethe vehicles are grouped according to their velocity vectorsThis method enhances the connectivity between the vehiclesThis leads to less link breakage problemsThis model reducesthe traffic by reducing the broadcast storm in the networkThe main idea of this method is to send the well-definedpackets

In summary if the traffic conditions are predicted thenthere is a less chance of performance degradation Manyprotocols predict the route by knowing the density of the

International Scholarly Research Notices 3

vehicles If in a road (connecting two junctions) there ishigh density then this does not signify that the vehicles areconnected IJS routing protocol predicts the link connectivitystatus LCS between the two vehicles in a route in high densityand low density This LCS is used to calculate the score of aneighboring junction It predicts the delay to send the datafrom one junction to the other and this delay is used tocalculate the score The junction with the minimum Scoreis selected as the optimal junction through which the data isforwarded to119863 To calculate the score IJS uses the three basicmodels discussed in the next section The notations used inthe paper are shown in Notations section

3 Preliminaries

31 ExpectedDataTransmissionDelayModel (EDTDM) Thismodel shows the expected time to send a message of length119871 in V2V communication and V2II2V communication [23]The expected time 119905expected(119881

11198812) to send a message of length

119871 bits from one vehicle 1198811 to the other vehicle 1198812 at a datatransmission rate of 119903(119881

11198812) is

119905expected(11988111198812) =

119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other (1)

119871119903(11988111198812) denotes the transmission delay and 119889(119881

11198812)119875speed

denotes the propagation delay where 119889(11988111198812) denotes the

distance between the two vehicles (radic(1199091 minus 1199092)2+ (1199101 minus 1199102)

2)

where (1199091 1199101) and (1199092 1199102) denote the position coordinatesof the vehicles 119875speed denotes the propagation speed and119905other denotes the other delays representing the queuing delayand processing delay The expected time 119905expected(119881RSU) tosend a message of length 119871 bits from a vehicle 119881 to RSU(V2I) at a data transmission rate of 119903uplink(119881RSU) (uplinkcommunication) is

119905expected(119881RSU) =119871

119903uplink(119881RSU)+

119889(119881RSU)

119875speed+ 119905other (2)

For downlink transmission from RSU to a vehicle 119881 at rate119903downlink(RSU119881) 119905expected(RSU119881) is calculated to be

119905expected(RSU119881) =119871

119903downlink(RSU119881)+

119889(RSU119881)

119875speed+ 119905other (3)

The expected time 119905expected(RSU1RSU2) to send a message of

length 119871 bits from one RSU to another (I2I) at a datatransmission rate of 119903(RSU

1RSU2) is

119905expected(RSU1RSU2) =

119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)

119875speed+ 119905other (4)

V2I I2VV2I and I2I communication enhances the networkperformance by preventing the network from network gapproblem If a vehicle 1198811 is not in a communication range ofanother vehicle 1198812 then network gap is created and 1198812 cantransmit the data to RSU (to prevent network gap problem)Then RSU transfers the data to another vehicle or RSU As

RSU has a higher communication range than a vehicle itsends the data to a vehicle which is nearer to 119863 (119863 is theneighboring junction)This reduces the delay to send the datafrom one junction to the other

Let the vehicles which are selected as the next hopvehicles between the two junctions to transmit the data be119881 = (1198811 1198812 119881119899) where 119899 is the number of next hopvehicles Then the expected time delay to send the data fromone junction 119869current to another junction 119869neighbor using V2Vcommunication is

119905expected(119869current 119869neighbor)

=119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other +

119871

119903(11988121198813)

+

119889(11988121198813)

119875speed+ 119905other + sdot sdot sdot +

119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)

119875speed+ 119905other

(5)

As data transmission through a route has different data rates(V2V V2I I2V and I2I) the total expected time delay to sendthe data packet from one junction 119869current to another junction119869neighbor using V2V V2I I2V and I2I communication is


= (119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other + sdot sdot sdot

+119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)

119875speed+ 119905other)

+ (119871

119903uplink(1198811RSU1)

+

119889(1198811RSU1)


+119871

119903uplink(119881119898RSU119898)

+

119889(119881119898RSU119898)


+ (119871

119903downlink(RSU11198811)

+

119889(RSU11198811)


+119871

119903downlink(RSU119894119881119894)

+

119889(RSU119894119881119894)


+ (119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)


+119871

119903(RSU119896minus1RSU119896)

+

119889(RSU119896minus1RSU119896)


=

119899

sum

119899=1

119905expected(119881119899minus1119881119899) +

119898

sum

119898=1

119905expected(119881119898RSU119898)

+

119894

sum

119894=1

119905expected(RSU119894119881119894) +

119896

sum

119896=1

119905expected(RSU119896minus1RSU119896)

(6)


where 119898 is the total number of data forwarding operationsfrom vehicle 119881 to RSU 119894 is the total number of data for-warding operations from RSU to vehicle 119881 and 119896 is the totalnumber of RSUs In this model only transmission delay andpropagation delay are considered as the main performanceparameters We have used this model for expecting the datatransmission delay to calculate the score

32 Link Connectivity Model (LCM) The link connectivitymodel (LCM) describes whether a link is available betweenthe vehicles or not Let at time 119905 the link start and continueup to 1199051 time So the link availability time between the twovehicles 119905available is denoted by

119905available = 119905 + 1199051 (7)

The velocity of the vehicles is used to calculate the linkconnectivity time between the two vehicles The IJS protocolonly considers the vehicles which are moving in the samedirection This means that the data is transmitted to thevehicle which is in the forward position Let the positionsof the vehicles 1198811 and 1198812 at time 119905 be (1199091 1199101) and (1199092 1199102)respectively Let 1198811 and 1198812 move at a speed of V1 and V2respectively and 119877 denotes the communication range of avehicle which is the same for all the vehicles There are twocases to calculate the connectivity time between the vehiclesCase 1 1198811 is ahead of 1198812 and 1198811 moves faster than 1198812

119905available = 119905 +

119877 minus radic(1199091 minus 1199092)2+ (1199101 minus 1199102)

2

1003816100381610038161003816V1 minus V21003816100381610038161003816

(8)

Case 2 1198811 is ahead of 1198812 and 1198812 moves faster than 1198811

119905available = 119905 +

radic(1199091 minus 1199092)2+ (1199101 minus 1199102)

2

1003816100381610038161003816V2 minus V11003816100381610038161003816

(9)

From (8) and (9) if 119905available gt 0 link 119897 exists else thelink is disconnected According to IJS LCS is set accordingto the conditions in (10) This model is used to predict theLCS values of the links in a route and used to calculate thescore

LCS = 1 if 119905available gt 0

0 if 119905available le 0(10)

The connectivity probability Pr of a road is calculatedfrom the vehicles between the junctions [13] This connec-tivity signifies how much the vehicles are connected to eachother to form a path from one junction to the other accordingto their radio rangeThe distance 119889(119881

11198812) between the vehicles

is expressed in terms of exponential distribution and it isshown as follows

119891 (119889(11988111198812)) = 120582119890

minus120582119889(11988111198812) (11)

where 120582 denotes the density of the vehicles per kilometerTheprobability that a vehicle is in the range 119877 of another vehicleis

Pr = int

119877

0

119891 (119889(11988111198812)) = 1 minus 119890

minus120582119877 (12)

So (13) shows the probability of a link 119897 between the twovehicles when they are connectedThe probability Pr1015840 that thevehicles are not connected is shown as follows

Pr1015840 = 119890minus120582119877

(13)

If the vehicles on a road are connected consecutively to forma path from one junction to the other then the probability ofconnectivity of the road is

Prroad = [1 minus 119890minus120582119877

]119899 (14)

where 119899 is the number of vehicles If a road segment consistsof 119899 vehicles which form a path by connection consecutivelythen there are 119899minus1 number of links If 119901 number of links failsthen the probability of connectivity of the road is


]119899minus1minus119901

sdot [119890minus120582119877

]119901 (15)

33 Vehicle Population Model (VPM) This section showsthe population 119880 of the vehicles between any two junctionsand their positions From Figure 1 let the population of thevehicles in an area 119860 at an interval 119905119880 be given by 119880 =

(1198811 1198812 1198813 1198814 1198815 1198816) In this model a fixed vehicle HV is setat every junction which records the traffic information usingbeaconing service where every vehicle beacons to share itslocation speed direction and identity Let the speed of thevehicles vary from V1 to V2 and the vehicles can attain anyspeed between them (government is limiting the speed tocontrol the accidents) At any time 119905119880 the population of thevehicles in area 119860 is calculated by predicting the positions ofthe vehicles between the junctions HV knows the positionsof the vehicles which are in the range So HV predicts thepositions of the vehicles (out of range vehicles) by calculatingthe distance covered by a vehicle after the last beacon Lastbeacon signifies the last information broadcasted by a vehicleto its neighboring vehicles after which the vehicle becomesout of range From this information HV knows the speed ofthe vehicle at the time 119905last-beaconThe position of the vehicle ispredicted by

119889covered = (119905119880 minus 119905last-beacon) Vlast-beacon (16)

where 119889covered is the distance covered by a vehicle afterlast beacon and Vlast-beacon signifies the speed of the vehiclepresented in the last beacon By finding the 119889covered by allthe vehicles between the two junctions population 119880 canbe calculated But for how many vehicles HV calculates the119889covered and finds 119880 This can be calculated by finding a time119905cross which signifies the time taken to cross another junctionby a vehicle If in between the two junctions the vehiclesmoveat a speed limit of V1 to V2 then in the worst case 119905cross iscalculated to be

119905cross =119875length

V1 (17)

where 119875length is the path length from one junction to anotherand V1 is the lowest bound speed So 119889covered is calculated for


Area (A) RSU

Jcurrent Jneighbor

Population

V1 V2 V3 V4 V5V6

HVHV

Range of HV

Figure 1 Population between the two junctions

the vehicles which crosses the junction between 119905119880 minus 119905crossand 119905119880 IJS considered only those vehicles whose positionsare between the junctionsThis model is used to calculate thepositions of the vehicles moving in a direction and used tofind a Routereference from one HV to the other This modelhelps to calculate the scores of the neighboring junctions

4 Proposed IJS Routing Protocol

41 SystemModel In this model we have assumed the city asa graph119866which consists of junctions as vertices and edges asroads which connect the junctions Vehicle and RSU beaconat a particular interval of time by which every vehicle knowsabout its neighboring vehicles location information Vehiclesuse GPS services and maps to know their own position androads ahead A helping vehicle (HV) is set at every junction119869 to store the information of the vehicles passing throughthe junction Vehicles forward the data to the vehicle whichis in the forward position and nearer to the destinationVehicles transmit the data to the vehicles moving in the samedirection The position of 119863 is known to the source vehicle119881119878 and it is updated at every junction by the use of locationservices [24 25]

42 Functionality of IJS Protocol In the city areas the densitymay be high but what is the chance that the vehicles areconnected to each other (there may be network gaps)So IJS is proposed to enhance the network performanceby predicting link connectivity problems at an early stageFor this a partial centralized architecture is designed inwhich an HV is set at every junction HV knows aboutthe traffic conditions ahead (between itself and neighboringjunctions) AsHV is a fixed vehicle it beacons and receives theinformation from its neighboring vehicles HV is a trustedvehicle which is mainly fixed to receive the data from thevehicles and send the data in a selected path When a vehiclewith a data reaches the junction it hands over the data toHV Then HV forwards the data to the junction which hasminimum Score This process continues until 119863 is reachedThese data are updated regularly with the beaconing service

The functionality of IJS protocol starts with sending thedata from the source vehicle119881119878 to the destination vehicle119881119863119881119878 initializes the communication and calculates the SP to 119881119863

using Dijkstra algorithm and forwards the data in the direc-tion of this path But 119881119878 has only one neighboring junctionwhich consists of an HV Then 119881119878 forwards the data to HVthrough many optimal vehicles 119881optimal (119881optimal is the vehiclein the range which is nearer to the neighboring junction)

After receiving the data packet HV at the junctionassumes the junction as 119869current and generates a Score forevery neighboring junction (119869neighbor

1

119869neighbor2

119869neighbor119895

)

(from Figure 3) HV uses the traffic information to select anew route to forward the data As HV is aware of the vehicleidentification location speed and direction HV calculatesthe score for every neighboring junction HV uses EDTDMLCM and VPM models to generate a score Firstly HV usesthe VPMmodel to find the positions of the vehicles betweenthe two junctionsThen HV finds a logical route of referenceRoutereference to forward the data from oneHV (HV at 119869current)to another HV (HV at 119869neighbor) by using the positions andrange of the vehicles

According to Figure 3 HV can find a 119881optimal to send thedata in its range If119881optimal is searched then it is assumed thata link 119897 can be established for data transmission to 119881optimalIn Figure 3 119881optimal is the vehicle 1198813 and a link 1198974 can beestablished for data transmission for 119905available time (from (8)(9) and (10)) So if data is transmitted it takes 119905expected(HV119881

3)

time (from (1))The route of reference Routereference = HV1198974

997888rarr

1198813 So IJS considers the link connectivity status LCS = 1

for the two vehicles HV and 1198813 After 1198813 receives the datalogically HV continues the above steps and it finds 1198814 as119881optimal for vehicle 1198813 This signifies that a link 1198975 for 119905availabletime can be established for data transmission and if data istransmitted it takes 119905expected(119881

31198814) time Now the new route of

reference is Routereference = HV1198974

997888rarr 1198813

1198975

997888rarr 1198814 Then IJS setsthe LCS value to 1 for vehicles1198813 and1198814 According to Figure3 this process continues by applying Algorithm 1 and finallythe data is logically forwarded to 119869neighbor (HV) on the route


(1) 119881119878 initializes the communication(2) for 119881119878 to HV do ⊳ 119881119878 sends the data to HV in 119869neighbor(3) if RSU is present then(4) Send data(5) if RSU has a neighbor RSU then(6) Send data(7) else RSU searches 119881optimal(8) Send data(9) end if(10) else 119881119878 searches 119881optimal(11) Send data(12) end if(13) end for

Algorithm 1 Data forwarding from 119881119878 to HV

HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979

997888rarr HVwith links 1198974 11989751198976 1198977 1198978 and 1198979 After the Routereference is generated HV findsthe SP(119869current 119881119863) from the current junction 119869current to 119881119863 and itfinds the SP(119869neighbor 119881119863) from neighbor junction 119869neighbor to 119881119863After getting all the information IJS calculates the score ofthe neighboring junction as follows

Score119895 =SP(119869neighbor 119881119863)SP(119869current 119881119863)

+ sum119905expected(119869current 119869neighbor)

+sum LCS

Total number of links

(18)

where 119895 is the number of neighboring junctionssum 119905expected(119869current 119869neighbor) is the total time delay to transferthe data from HV of 119869current to HV of 119869neighbor and sum LCS isthe sum of all the LCS values predicted IJS mainly considersthe propagation delay and transmission delay in 119905expectedto calculate the score The score is generated for everyneighboring junction except the junction through which thedata has already transferredThe junction with the minimumscore is selected as the next optimal junction The optimaljunction 119869optimal is selected as

119869optimal = min (Score1 Score2 Score119895) (19)

According to Figure 3 HV selects the straight path with theminimum score because the left and right paths have hugenetwork gaps with less density of vehicles which increases thevalue of the score

Then the data is transmitted in the direction of 119869optimalby using the Routereference information which is forwardedin the packet This process continues until the last junction119869last (junction which is nearer to 119881119863) is reached From Figure3 after data packet is delivered to HV it forwards the datadirectly to 119881119863 by selecting optimal vehicles Figure 2 showsthe flowchart of IJS routing protocol This method showsbetter performance by predicting the optimal junctions onthe route

IJS shows better performance by reducing the time delayto forward the data to the destination In the city areas

Start

Source initializesthe communication

if (J = Jlast)

Yes

No Send data tothe destination

vehicle

End

HV generates scorefor every

neighboring junction

Send data to J(HV)

Joptimal = min(Score1 Score2 Score3)

Figure 2 Flowchart of IJS routing protocol

due to high mobility of vehicles there may be network gapsituations in any direction This leads to less densified regiongeneration which disrupts the link between the vehiclesTo overcome this situation IJS uses V2I I2VV2I and I2Icommunication If any of these communications fails thenvehicleinfrastructure carries the data until a new vehicle isencountered in its range (Algorithm 2)

5 Analysis of IJS protocol

IJS protocol is analyzed by considering the junctions asvertices and edges as the roads connecting the two junctionsThen the city is represented as a graph 119866 We consider pathlength delay and network gap to evaluate the performance ofthe protocol The performance is analyzed as follows


HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11

Figure 3 Data forwarding from 119881119878 to 119881119863 using IJS protocol

(1) for 119869first to 119869last do ⊳ 119869first is the first junction to receive data from 119881119878

(2) if 119869 = 119869last then(3) HV receives the data(4) 119869neighbor becomes 119869current(5) HV at 119869current predicts the positions of the vehicles using VPM(6) HV finds a Routereference ⊳ HV

1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV

(7) Calculate the links between the vehicles(8) if link breaks then(9) Network Gap exists(10) LCS = 0(11) else(12) no Network Gap exists(13) LCS = 1(14) end if(15) HV records the LCS values and 119905expected values using LCM and EDTDM(16) HV calculates SP(119869current 119881119863) and SP(119869neighbor 119881119863)(17) Score is calculated as Score119895 = SP(119869neighbor 119881119863)SP(119869current 119881119863) + sum 119905expected(119869current 119869neighbor) + sum LCSTotal number of links(18) 119869optimal = min(Score1 Score2 Score119895)(19) 119869neighbor with minimum score becomes 119869optimal(20) Forward the data in the direction of 119869optimal(21) else Forward the data to 119881119863 using optimal vehicles(22) end if(23) end for

Algorithm 2 Score generation

Lemma 1 The 119878119875 selected from 119878 to 119863 is the sum of all theintermediate distances

Proof Let the total distance calculated from 119878 (source vehi-cle) to 119863 (destination vehicle) be 119889total and suppose that 119878

sends data to 119863 through the intermediate optimal junctions1198691 1198692 119869119895 where 119895 = 1 2 119895 and then path 119875 isrepresented as

119875 = 119878 997888rarr 1198691 997888rarr 1198692 997888rarr sdot sdot sdot 997888rarr 119869119895 997888rarr 119863 (20)


From (20)

119889total = 1198781198691 + 11986911198692 + sdot sdot sdot + 119869119895119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)

where 119869119895 is the last junction nearer to destination119863

Lemma 2 Path length increases with the increase in thenumber of junctions in the path

Proof Let the shortest path 1198751 have 1198951198751

number of junctionsand let the shortest path 1198752 have 119895119875

2

number of junctionswhere 119895119875

1

gt 1198951198752

From (20) and (21) 1198751 = 119878 rarr 1198691 rarr 1198692 rarr

sdot sdot sdot rarr 1198691198951198751

rarr 119863 and 1198752 = 119878 rarr 1198691 rarr 1198692 rarr sdot sdot sdot rarr 1198691198951198752

rarr

119863 and 119889total for 1198751 and 1198752 is presented as

119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2

be the time delays in paths 1198751 and 1198752respectively to send the data from 119878 to 119863 and it is presentedas

119905total1

= 119905expected(1198781198691) + 119905expected(119869

11198692) + sdot sdot sdot + 119905expected(119869

1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752

from (23) 119905total1

gt 119905total2

and hence if the numberof hops (junctions) increases distance increases

Lemma 3 Delay increases with the increase in the number ofjunctions in the path

Proof FromLemma 2 as the number of junctions in the pathincreases delay increases Equation (23) shows the delays inthe paths 1198751 and 1198752 respectively It is concluded that if thejunctions are not selected intelligently then the number ofjunctions increases and 119905total

1

gt 119905total2

Lemma 4 Delay increases with the generation of networkgaps

Proof Let the data transmission path from one junction 1198691 tothe other be 1198751 = 1198691 rarr 1198811 rarr 1198812 rarr sdot sdot sdot rarr 1198692 wherevehicles (1198811 1198812 119881119899) denote the optimal vehicles Path 1198751

has a higher density of vehicles and they aremostly connectedto each other Similarly the data transmission path for 1198752 is1198752 = 1198693 rarr 1198811 rarr 1198812 rarr sdot sdot sdot rarr 1198694 Let the distance from1198691 to 1198692 be the same as 1198693 to 1198694 According to the protocol ifnetwork gaps are predicted earlier then the delay decreasesLet 1198751 have 119862119875

1

number of carry and forward mechanismsand let1198752 have119862119875

2

number of carry and forwardmechanismswhere 119862119875

2

gt 1198621198751

According to the protocol if vehicle encounters a network

gap it carries the data until a new vehicle is encountered inthe path If the number of carry and forward mechanismsincreases delay increases So the delay for paths 1198751 and 1198752

is represented as

119905expected(11986911198692) = 119905expected(119869

11198811) + 119905expected(119881

11198812)

+

carry and forward⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞119905119862 + 119905expected(119881

21198813) sdot sdot sdot

+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)

carry and forward+⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞119905119862 + 119905expected(119881

21198813) +



+ 119905expected(1198811198991198694)

(24)

where 119905119862 denotes the carry and forward mechanism Aspath 1198751 has less number of carry and forward mechanisms119905expected(119869

31198694) gt 119905expected(119869

11198692) So the existence of network gap

increases the delay

Lemma 5 119878119888119900119903119890 calculation uses the best parameters to selectan optimal junction

Proof To calculate the Score in IJS protocol we use theshortest path SP(119869current 119881119863) from the current junction 119869current to119881119863 and the shortest path SP(119869neighbor 119881119863) from neighbor junction119869neighbor to 119881119863 Paths are calculated to find the minimumdistance from the junctions to the destination vehicle 119881119863

to send the data in a quicker path SP(119869neighbor 119881119863)SP(119869current 119881119863)provides the closeness of destination to the neighbor junc-tion Then IJS uses 119905expected(119869current 119869neighbor) which provides theminimum time to send a data packet from one junction tothe other This value should be the minimum Connectivityof the vehicles is assumed by the link connectivity statusLCS which shows whether the vehicles are connected or not(sum LCSTotal number of links) finds the average of the linksbetween the vehicles


By combining the three parameters we find the formulafor the Score in (25) and the junctionwith theminimumvalueis selected as the optimal junctionThe Score for a junction iscalculated as follows



+sum LCS


(25)

Theorem 6 The junction with the minimum 119878119888119900119903119890 is the bestjunction through which the data is forwarded

Proof Let a junction 1198691 have three neighboring junctions1198692 1198693 and 1198694 We assume that the density of the vehiclesbetween 1198691 and the three neighboring junctions is 1205821 1205822 and1205823 respectively According to Lemma 5 Score of a junctiondepends on the connectivity of the vehicles If in between thejunctions the vehicles are connected consecutively then it isthe best path to transmit the data For junctions 1198692 1198693 and 1198694the probability of connectivity is shown as follows

Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890

minus120582119877]119872minus1minus119901

Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)

where 119872 119873 and 119876 are the number of consecutive vehicleswhich can be connected in between the junctions 119901 119902 and 119906

are the number of network gaps or link failures between thejunctions We assumed that 119901 gt 119902 gt 119906 and 1205823 gt 1205822 gt 1205821so we conclude that Pr(11986911198694) has a higher value than Pr(11986911198692)and Pr(11986911198693)(Pr(11986911198694) gt Pr(11986911198693) gt Pr(11986911198692)) From Lemma4 it is proved that if there is less number of network gaps andthe road is highly connected then delay reduces

From Lemma 5 dividing the shortest paths providesthe closeness information of the destination to the sourceThis minimum value helps in finding the minimum Scorejunction This combination of the parameters justifies thefinding of the best junction through which the data is quicklytransmitted

Theorem 7 If network gaps are predicted earlier then thedelay to send the data from 119878 to 119863 reduces

Proof After receiving the data let junction 1198691 use path 1198751 tosend the data to 119863 using the optimal junctions Let path 1198751

be represented as 1198751 = 1198691 rarr 1198692 rarr 1198693 rarr sdot sdot sdot rarr 119869119895 rarr 119863where (1198692 1198693 119869119895) are the optimal junctionsThen the totaldistance is represented as

119889total = 11986911198692 + 11986921198693 + sdot sdot sdot + 119869119895119863 (27)

and according to Lemma 4 the expected delay to sendthe data from one junction to the other is represented by

119905expected(119869current 119869neighbor) So the total delay 119905total for the path isrepresented as

119905total



119895119863)

= [119905expected(11986911198811) + 119905expected(119881

11198812)

+


21198813) +



+119905expected(1198811198991198692)]

+ [119905expected(11986921198815) + 119905expected(119881

51198816)

+


61198817) +



+119905expected(1198811198991198693)] + sdot sdot sdot


911988110)

+

carry and forward⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞119905119862 + 119905expected(119881

1011988111) +



+119905expected(119881119899119863)]

(28)

According to Lemma 4 if the path has less number ofnetwork gaps or link failures then delay reduces From (28)if the network gaps between the junctions increase the delayincreases So IJS selects the best junctions in the path bypredicting the network gaps between the junctions earlier andselects the neighboring junctionwhere the vehicles aremostlyconnected

6 Simulation and Results

IJS routing protocol is simulated and compared with GyTARA-STAR and GSR routing protocols To check the perfor-mance of IJS we have considered three performance metricsthrough which the performance of the protocols is evaluatedThe performance metrics are discussed as follows

(1) average end-to-end delay this is the average delay tosend a data packet from the source to the destination

(2) network gap encounter this represents the total num-ber of network gaps encountered in a route when adata packet is sent from the source to the destination

(3) number of hops this represents the total number ofhops or vehicles used for data transmission betweenthe source and the destination

In this simulation the parameters are set according to Table1 We have considered 36 junctions and the distance between


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50

Maximum number of vehicles

Del

ay (s

)

IJSGyTAR

A-STARGSR

(a) 2500m with no RSU

20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR

(b) 2500m with 1 RSU

20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR

(c) 3000m with no RSU

20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR

(d) 3000m with 1 RSU

Figure 4 Simulation results for average end-to-end delay in IJS GyTAR A-STAR and GSR

the junctions is set to be 2500m and 3000m To identifythe performance of the protocols the distance betweenthe junctions is varied The maximum number of vehiclesbetween the junctions is varied to evaluate the performance ofthe network with different densitiesThe speed of the vehiclesis set to be 70ndash90KmH Vehicle range is set to be 250mand as RSU has a higher range it is set to be 500m RSUis set at the center of the road The packet size is set to be512 bytes The protocols are implemented using MATLABversion R2012a (7140739) The protocols are implementedin the perfect conditions and the performance is evaluatedaccording to EDTDM LCM and VPM models discussedabove

In this simulation environment we have assumed theideal conditions in which link is established with no dis-turbance when an optimal vehicle is encountered in therange We considered perfect conditions where no droppingof packets and contention occurs in the network

In these scenarios we have set the distance between thejunctions to be 2500m and 3000m by varying the number ofRSUswith noRSU and 1 RSUAccording to Figures 4(a) 4(b)4(c) and 4(d) IJS shows a less delay than GyTAR A-STARand GSR routing protocols In this we identify that when thedensity of vehicles increases the delay reduces due to highchance of link establishment When the maximum densitybetween the vehicles is set to 60 80 and 100 the delays of


20 30 40 50 60 70 80 90 100Maximum number of vehicles

IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter


Figure 5 Simulation results for network gap encounter in IJS GyTAR A-STAR and GSR

the protocols varied by a less difference Figure 4(b) shows aless delay than Figure 4(a) because RSU reduces the numberof hops As the distance between the junctions increases thedelay increases due to less density of vehicles in the areaFrom Figures 4(a) and 4(c) we conclude that Figure 4(c) hasa higher delay than Figure 4(a)

In Figures 5(a) 5(b) 5(c) and 5(d) IJS encounters lessnumber of network gaps When the vehicle populationincreases the network gap reduces because of the high chanceof encountering a vehicle in the rangeThis concludes that IJSdetects the network gaps at an early stage and protects thenetwork from link disruption problems The probability ofconnectivity Pr increases with the decreases in the number

Table 1 Simulation environment

Parameter Parameter value

Number of junctions 36Distance between the junctions 3000mNumber of vehicles between the junctions 20ndash100Vehicle speed 70ndash90KmHVehicle range 250mNumber of RSUs 1RSU range 500mPacket size 512 bytes


0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


Figure 6 Simulation results for number of hops in IJS GyTAR A-STAR and GSR

of network gaps The average number of network gaps inFigure 5(a) for IJS GyTAR A-STAR and GSR is 14 2 34and 42 respectively and for Figure 5(b) the average numberof network gaps is 06 1 22 and 3 respectively As RSU isset between the junctions network gap reducesThis signifiesthat RSU helps in reducing the network gaps Figure 5(a)shows a less number of average network gaps than Figure 5(c)which has an average of 2 28 54 and 6 for IJS GyTAR A-STAR and GSR routing protocols respectively

In Figures 6(a) 6(b) 6(c) and 6(d) IJS has less numberof hops than GyTAR A-STAR and GSR routing protocolsAs vehicles density increases the number of hops reducesdue to high chance of availability of vehicles This helpsin selecting the optimal vehicles in the range Figure 6(a)

shows more number of hops than Figure 6(b) because thepresence of RSU reduces the number of hop counts Figure6(a) shows less number of hops than Figure 6(c) because asthe distance between the junctions increases the chance ofgetting vehicles in the range reduces

7 Conclusion

IJS is an intelligent routing protocol for VANET in whichHV predicts the most connected route to the destinationwith less delay This protocol will be a better routing protocolfor vehicular communication providing ITS services (egaccident warning services) to the passengers and driversIJS detects the network gap at an early stage before sending


the data packet By considering the population between thejunctions and finding a logical Routereference the score forevery neighboring junction is calculated and the minimumscore junction is selected as the next junction through whichthe data is forwarded This strategy helps the vehicles tosend their data in a quicker path This reduces the end-to-end delay network gap encounter and number of hopsSimulation results show that IJS performance is better thanGyTAR A-STAR and GSR routing protocols This protocolpromises a better solution to provide services to the driversand passengers

Notations

119881 Vehicle119869 Junction119878 Source119863 DestinationSP Shortest pathHV Helping vehicle119877 Range120582 DensityScore Score119905expected Expected delay119905119862 Carrying timeLCS Link connectivity statusRoutereference Logical route reference119875 Path119869current Current junction119869neighbor Neighbor junction119869optimal Optimal junction119881optimal Optimal vehicle119889 DistanceV VelocityPr Probability of connectivity119871 Message length119903 Channel rate

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] S K Bhoi and PM KhilarVehicular Communication A SurveyIET Networks Stevenage UK 2013

[2] F Li and Y Wang ldquoRouting in vehicular ad hoc networks asurveyrdquo IEEE Vehicular Technology Magazine vol 2 no 2 pp12ndash22 2007

[3] S Zeadally R Hunt Y-S Chen A Irwin and A HassanldquoVehicular ad hoc networks (VANETS) status results andchallengesrdquo Telecommunication Systems vol 50 no 4 pp 217ndash241 2012

[4] M L Sichitiu and M Kihl ldquoInter-vehicle communication sys-tems a surveyrdquo IEEE Communications Surveys amp Tutorials vol10 no 2 pp 88ndash105 2008

[5] E Schoch F Kargl M Weber and T Leinmuller ldquoCommuni-cation patterns in VANETsrdquo IEEE Communications Magazinevol 46 no 11 pp 119ndash125 2008

[6] KAHafeez L Zhao BMa and JWMark ldquoPerformance anal-ysis and enhancement of the DSRC for VANETrsquos safety appli-cationsrdquo IEEE Transactions on Vehicular Technology vol 62 no7 pp 3069ndash3083 2013

[7] J B Kenney ldquoDedicated short-range communications (DSRC)standards in the United Statesrdquo Proceedings of the IEEE vol 99no 7 pp 1162ndash1182 2011

[8] Y Toor P Muhlethaler A Laouiti and A de La FortelleldquoVehicle ad hoc networks applications and related technicalissuesrdquo IEEE Communications Surveys and Tutorials vol 10 no3 pp 74ndash88 2008

[9] G Karagiannis O Altintas E Ekici et al ldquoVehicular network-ing a survey and tutorial on requirements architectures chal-lenges standards and solutionsrdquo IEEE Communications Surveysand Tutorials vol 13 no 4 pp 584ndash616 2011

[10] J Harri F Filali and C Bonnet ldquoMobility models for vehicularad hoc networks a survey and taxonomyrdquo IEEE Communica-tions Surveys and Tutorials vol 11 no 4 pp 19ndash41 2009

[11] Y-W Lin Y-S Chen and S-L Lee ldquoRouting protocols invehicular Ad Hoc networks a survey and future perspectivesrdquoJournal of Information Science and Engineering vol 26 no 3 pp913ndash932 2010

[12] C Lochert H Hartenstein J Tian H Fussler D Hermann andM Mauve ldquoA routing strategy for vehicular ad hoc networks incity environmentsrdquo inProceedings of the IEEE Intelligent VehiclesSymposium pp 156ndash161 2003

[13] M Jerbi S-M Senouci T Rasheed and Y Ghamri-DoudaneldquoTowards efficient geographic routing in urban vehicular net-worksrdquo IEEE Transactions on Vehicular Technology vol 58 no9 pp 5048ndash5059 2009

[14] B-C Seet G Liu B-S Lee C-H Foh K-J Wong and K-KLee ldquoA-STAR a mobile ad hoc routing strategy for metropolisvehicular communicationsrdquo in Networking Technologies Ser-vices and Protocols Performance of Computer and Communica-tion Networks Mobile and Wireless Communications pp 989ndash999 Springer Berlin Germany 2004

[15] B Karp and H T Kung ldquoGPSR greedy Perimeter StatelessRouting for wireless networksrdquo in Proceedings of the 6th AnnualInternational Conference on Mobile Computing and Networking(MOBICOM rsquo00) pp 243ndash254 August 2000

[16] C Lochert M Mauve H Fubler and H Hartenstein ldquoGeo-graphic routing in city scenariosrdquo ACM SIGMOBILE MobileComputing and Communications Review vol 9 no 1 pp 69ndash72 2005

[17] Y-S Chen Y-W Lin and C-Y Pan ldquoDIR diagonal-inter-section-based routing protocol for vehicular ad hoc networksrdquoTelecommunication Systems vol 46 no 4 pp 299ndash316 2011

[18] J Bernsen and D Manivannan ldquoRIVER a reliable inter-vehicular routing protocol for vehicular ad hoc networksrdquoComputer Networks vol 56 no 17 pp 3795ndash3807 2012

[19] M H Eiza and Q Ni ldquoAn evolving graph-based reliable rout-ing scheme for VANETsrdquo IEEE Transactions on Vehicular Tech-nology vol 62 no 4 pp 1493ndash1504 2013

[20] H Saleet R Langar K Naik R Boutaba A Nayak and NGoel ldquoIntersection-based geographical routing protocol forVANETs a proposal and analysisrdquo IEEE Transactions on Vehi-cular Technology vol 60 no 9 pp 4560ndash4574 2011

[21] S Bilal S Madani and I Khan ldquoEnhanced junction selectionmechanism for routing protocol in VANETsrdquo InternationalArab Journal of Information Technology vol 8 no 4 pp 422ndash429 2011


[22] T Taleb E Sakhaee A Jamalipour K Hashimoto N Kato andY Nemoto ldquoA stable routing protocol to support ITS services inVANET networksrdquo IEEE Transactions on Vehicular Technologyvol 56 no 6 pp 3337ndash3347 2007

[23] A Mostafa A M Vegni R Singoria T Oliveira T D C Littleand D P Agrawal ldquoA V2X-based approach for reduction ofdelay propagation in vehicular Ad-Hoc networksrdquo in Proceed-ings of the 11th International Conference on ITS Telecommunica-tions (ITST rsquo11) pp 756ndash761 St Petersburg Russia August 2011

[24] M D Dikaiakos A Florides T Nadeem and L Iftode ldquoLoca-tion-aware services over vehicular ad-hoc networks using car-to-car communicationrdquo IEEE Journal on Selected Areas in Com-munications vol 25 no 8 pp 1590ndash1602 2007

[25] EHeidari AGladisch BMoshiri andDTavangarian ldquoSurveyon location information services for Vehicular CommunicationNetworksrdquoWireless Networks pp 1ndash21 2013

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Advances in

Computer EngineeringAdvances in



by reducing the delay The main contribution in this paper isas follows

(1) An IJS routing protocol is proposed to transmit thedata quickly to the destination 119863 in the mostly con-nected path which has less number of network gapsAn HV is set at every junction to predict the linkfailures (network gaps) between the vehicles in a route(between two neighboring junctions)

(2) HV calculates the score Score for every neighboringjunction by predicting the delay between the twojunctions and predicting the link connectivity statusbetween the vehicles in a logical route Routereference(logical route from one junction to another) HVselects the junction which has a minimum Scoreand forwards the data in that direction This processcontinues until last junction 119869last (junction nearer to119863) is reached and then data is sent in the route of 119863through the vehicles

The paper is organized as follows Section 2 presents therelated works which discuss the standard routing protocolsproposed for forwarding the data to destination Section 3presents the preliminary part in which three basic models arediscussed which are the key elements for IJS routing protocolSection 4 presents the IJS routing protocol where systemmodel and functionality of the model are described Section5 presents the analysis of the proposed IJS routing protocolSection 6 presents the simulation part where the protocols areimplemented and compared to identify the performance Atlast we concluded in Section 7

2 Related Works

Many works are proposed to provide efficient routing pro-tocols for VANET to send the data quickly from the sourceto the destination [3 11 12] We are motivated by an efficientjunction routing protocol known asGyTARproposed by Jerbiet al [13] to send the data efficiently through the junctions Ascore is calculated for every neighboring junction by knowingthe current traffic conditions This protocol performs betterbecause the vehicles near the junctions are aware of the trafficconditions Cell data packet (CDP) is usedmainly to send thedensity information to the junctions But there may be manynetwork gaps generated between the junctions by which theCDP information is not updated at the current time Thismay generate a false score calculation The congestion in thenetwork is more due to high CDP packet exchange

A-STAR [14] is a junction routing protocol in which thedata packet is transmitted in a predefined path which consistsof buses and vehicles These junctions have higher weightagethan the normal junctions So the vehicles always prefer tosend the data through the densified regions But by usingthese routes there may be delay in reaching the destinationbecause these types of routes exist in some areas There maybe less density in these areas which leads to higher delay

GSR routing protocol [14] is a position based routingprotocol which sends the data in the shortest path route to119863 This suffers from link connectivity problem due to lack

of information about the traffic conditions ahead This alsoincreases the delay

GPSR [15] is a position based routing protocol whichforwards the data by selecting the vehicle which is nearer tothe destination To overcome the local optimum problem itworks in perimeter mode Due to the lack of informationabout the current traffic conditions it sometimes goes toperimeter mode This degrades the performance of thenetwork

GPCR [16] is a routing protocol which chooses a coor-dinator node to forward the data to the destination Afterforwarding the data through the street vehicles choose acoordinator node at the junction When the density of thevehicles in a region is low GPCR suffers from the networkgap problem It does not use current traffic information tochoose the best path to the destination

Chen et al [17] proposed a diagonal-intersection-basedrouting protocol (DIR) in which the source forwards the datathrough the diagonal junctions The path to the diagonaljunction is selected by finding a delay and the path with lessdelay is selected as the path to reach the diagonal junctionThis process continues until destination is reached Thisautoadjustability enhances the performance of the system

Bernsen and Manivannan [18] proposed a reliable inter-vehicular routing (RIVER) protocol by considering the cur-rent traffic information It assigns a reliability rating to thestreet edges and forwards the data according to the ratings Itshares the reliability information with other vehicles to makeother vehicles aware of the current traffic conditions Thisincreases the performance of the system by selecting the bestpossible routes to the destination

Eiza and Ni [19] proposed a graph based reliable routingprotocol in which evolving graph theory is used to forwardthe data to the destination in a highway scenario Thisprotocol is mainly proposed to provide QoS in routing Itprovides better average end-to-end delay packet deliveryratio and lower link failure rate

Saleet et al [20] proposed an intersection-based geo-graphical routing protocol (IGRP) to send the data effectivelythrough the junction to reach the internet gateway Thejunctions are selected on the basis of delay QoS bandwidthusage connectivity and error rate

Bilal et al [21] proposed an enhanced-GyTAR routingprotocol to send the data through the intelligent junctions byfinding a score But this protocol only considers the densityand speed of the vehicles It does not know about the con-nectivity of the vehicles CDP packet is used for connectivityinformation but due to network gaps the information is notupdated which leads to false score calculation

Taleb et al [22] proposed a stable routing protocol wherethe vehicles are grouped according to their velocity vectorsThis method enhances the connectivity between the vehiclesThis leads to less link breakage problemsThis model reducesthe traffic by reducing the broadcast storm in the networkThe main idea of this method is to send the well-definedpackets

In summary if the traffic conditions are predicted thenthere is a less chance of performance degradation Manyprotocols predict the route by knowing the density of the



3 Preliminaries




11198812) is

119905expected(11988111198812) =

119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other (1)


11198812)119875speed



2)


119905expected(119881RSU) =119871

119903uplink(119881RSU)+

119889(119881RSU)

119875speed+ 119905other (2)


119905expected(RSU119881) =119871


119889(RSU119881)

119875speed+ 119905other (3)



1RSU2) is


119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)

119875speed+ 119905other (4)





=119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other +

119871

119903(11988121198813)

+

119889(11988121198813)


119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)


(5)



= (119871

119903(11988111198812)

+

119889(11988111198812)


+119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)


+ (119871

119903uplink(1198811RSU1)

+

119889(1198811RSU1)


+119871

119903uplink(119881119898RSU119898)

+

119889(119881119898RSU119898)


+ (119871


+

119889(RSU11198811)


+119871

119903downlink(RSU119894119881119894)

+

119889(RSU119894119881119894)


+ (119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)


+119871

119903(RSU119896minus1RSU119896)

+

119889(RSU119896minus1RSU119896)


=

119899

sum

119899=1

119905expected(119881119899minus1119881119899) +

119898

sum

119898=1

119905expected(119881119898RSU119898)

+

119894

sum

119894=1

119905expected(RSU119894119881119894) +

119896

sum

119896=1


(6)




119905available = 119905 + 1199051 (7)


119905available = 119905 +


2

1003816100381610038161003816V1 minus V21003816100381610038161003816

(8)


119905available = 119905 +


2

1003816100381610038161003816V2 minus V11003816100381610038161003816

(9)







119891 (119889(11988111198812)) = 120582119890

minus120582119889(11988111198812) (11)


Pr = int

119877

0

119891 (119889(11988111198812)) = 1 minus 119890

minus120582119877 (12)


Pr1015840 = 119890minus120582119877

(13)



]119899 (14)




sdot [119890minus120582119877

]119901 (15)






V1 (17)



Area (A) RSU

Jcurrent Jneighbor

Population

V1 V2 V3 V4 V5V6

HVHV

Range of HV









1

119869neighbor2


)



3)


997888rarr





997888rarr 1198813

1198975





HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979




+sum LCS


(18)






Start


if (J = Jlast)

Yes


vehicle

End



Send data to J(HV)







HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





3 Preliminaries




11198812) is

119905expected(11988111198812) =

119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other (1)


11198812)119875speed



2)


119905expected(119881RSU) =119871

119903uplink(119881RSU)+

119889(119881RSU)

119875speed+ 119905other (2)


119905expected(RSU119881) =119871


119889(RSU119881)

119875speed+ 119905other (3)



1RSU2) is


119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)

119875speed+ 119905other (4)





=119871

119903(11988111198812)

+

119889(11988111198812)

119875speed+ 119905other +

119871

119903(11988121198813)

+

119889(11988121198813)


119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)


(5)



= (119871

119903(11988111198812)

+

119889(11988111198812)


+119871

119903(119881119899minus1119881119899)

+

119889(119881119899minus1119881119899)


+ (119871

119903uplink(1198811RSU1)

+

119889(1198811RSU1)


+119871

119903uplink(119881119898RSU119898)

+

119889(119881119898RSU119898)


+ (119871


+

119889(RSU11198811)


+119871

119903downlink(RSU119894119881119894)

+

119889(RSU119894119881119894)


+ (119871

119903(RSU1RSU2)

+

119889(RSU1RSU2)


+119871

119903(RSU119896minus1RSU119896)

+

119889(RSU119896minus1RSU119896)


=

119899

sum

119899=1

119905expected(119881119899minus1119881119899) +

119898

sum

119898=1

119905expected(119881119898RSU119898)

+

119894

sum

119894=1

119905expected(RSU119894119881119894) +

119896

sum

119896=1


(6)




119905available = 119905 + 1199051 (7)


119905available = 119905 +


2

1003816100381610038161003816V1 minus V21003816100381610038161003816

(8)


119905available = 119905 +


2

1003816100381610038161003816V2 minus V11003816100381610038161003816

(9)







119891 (119889(11988111198812)) = 120582119890

minus120582119889(11988111198812) (11)


Pr = int

119877

0

119891 (119889(11988111198812)) = 1 minus 119890

minus120582119877 (12)


Pr1015840 = 119890minus120582119877

(13)



]119899 (14)




sdot [119890minus120582119877

]119901 (15)






V1 (17)



Area (A) RSU

Jcurrent Jneighbor

Population

V1 V2 V3 V4 V5V6

HVHV

Range of HV









1

119869neighbor2


)



3)


997888rarr





997888rarr 1198813

1198975





HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979




+sum LCS


(18)






Start


if (J = Jlast)

Yes


vehicle

End



Send data to J(HV)







HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






119905available = 119905 + 1199051 (7)


119905available = 119905 +


2

1003816100381610038161003816V1 minus V21003816100381610038161003816

(8)


119905available = 119905 +


2

1003816100381610038161003816V2 minus V11003816100381610038161003816

(9)







119891 (119889(11988111198812)) = 120582119890

minus120582119889(11988111198812) (11)


Pr = int

119877

0

119891 (119889(11988111198812)) = 1 minus 119890

minus120582119877 (12)


Pr1015840 = 119890minus120582119877

(13)



]119899 (14)




sdot [119890minus120582119877

]119901 (15)






V1 (17)



Area (A) RSU

Jcurrent Jneighbor

Population

V1 V2 V3 V4 V5V6

HVHV

Range of HV









1

119869neighbor2


)



3)


997888rarr





997888rarr 1198813

1198975





HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979




+sum LCS


(18)






Start


if (J = Jlast)

Yes


vehicle

End



Send data to J(HV)







HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Area (A) RSU

Jcurrent Jneighbor

Population

V1 V2 V3 V4 V5V6

HVHV

Range of HV









1

119869neighbor2


)



3)


997888rarr





997888rarr 1198813

1198975





HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979




+sum LCS


(18)






Start


if (J = Jlast)

Yes


vehicle

End



Send data to J(HV)







HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






HV1198974

997888rarr 1198813

1198975

997888rarr 1198814

1198976

997888rarr RSU1198977

997888rarr 1198815

1198978

997888rarr 1198816

1198979




+sum LCS


(18)






Start


if (J = Jlast)

Yes


vehicle

End



Send data to J(HV)







HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




HV HV

HV HVHV

HV HV HV

HV

RSU

RSU

RSU

RSU

RSU

RSU

RSU

RSU

J1 J2

J3

J4

V1 V2 V3 V4 V5

V6

V7

VS

VD

l1

l2

l3

l4l5 l6 l7

l8 l9 l10

l11




1198974997888rarr 1198813

1198975997888rarr 1198814

1198976997888rarr RSU

1198977997888rarr 1198815

1198978997888rarr 1198816

1198979997888rarr HV








From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




From (20)


= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(21)





2


1

gt 1198951198752




rarr


119889total1

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198751

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

119889total2

= 1198781198691 + 11986911198692 + 11986921198693 + sdot sdot sdot + 1198691198951198752

119863

= 1198891 + 1198892 + sdot sdot sdot + 119889119895 =

119895

sum

119895=1

119889119895

(22)

Let 119905total1

and 119905total2


119905total1



1198951198751

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

119905total2



1198951198752

119863)

= 1199051 + 1199052 + sdot sdot sdot + 119905119895 =

119895

sum

119895=1

119905119895

(23)

As 1198951198751

gt 1198951198752


gt 119905total2




1

gt 119905total2




1


2


2

gt 1198621198751



is represented as


11198811) + 119905expected(119881

11198812)

+



+ 119905expected(1198811198991198692)


31198811) + 119905expected(119881

11198812)


21198813) +



+ 119905expected(1198811198991198694)

(24)


31198694) gt 119905expected(119869


increases the delay








+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







+sum LCS


(25)



Pr (11986911198692) = [119890minus120582119877

]119901sdot [1 minus 119890


Pr (11986911198693) = [119890minus120582119877

]119902sdot [1 minus 119890


Pr (11986911198694) = [119890minus120582119877

]119906sdot [1 minus 119890


(26)










119905total



119895119863)


11198812)

+


21198813) +



+119905expected(1198811198991198692)]


51198816)

+


61198817) +





911988110)

+


1011988111) +



+119905expected(119881119899119863)]

(28)









20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

50


Del

ay (s

)

IJSGyTAR

A-STARGSR


20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45


Del

ay (s

)

IJSGyTAR

A-STARGSR








IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter

18

16



IJSGyTAR

A-STARGSR

14

12

10

8

6

4

2

0

Net

wor

k ga

p en

coun

ter









0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




0

10

20

30

40

50

60

70Pa

th le

ngth

(num

ber o

f hop

s)

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR


20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80


90

20 40 60 80 100

Number of vehicles

IJSGyTAR

A-STARGSR

0

10

20

30

40

50

60

70

Path

leng

th (n

umbe

r of h

ops)

80






7 Conclusion




Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





Notations




References


































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in















Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



research article ijs: an intelligent junction selection...

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