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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volu...
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Impact of mobility and maps size on the performances of vanets in urban area

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Impact of mobility and maps size on the performances of vanets in urban area

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME556IMPACT OF MOBILITY AND MAPS SIZE ON THE PERFORMANCESOF VANETS IN URBAN AREAA. Rhattoy1and A. Zatni21Department of Computer, Modeling Systems and Telecommunications ResearchGroup/MoulayIsmailUniversity, Higher School of Technology,B.P. 3103, 50000, Toulal, Meknes, Morocco2Department of Computer, MSTI Laboratory/ Ibnou Zohr University, Higher School ofTechnology, B. P. 33/S, 80000, Agadir, MoroccoABSTRACTVehicular Ad hoc Networks (VANETs) represent a rapidly emerging research field,being a particularly challenging class of Mobile Ad Hoc Networks [1], used forcommunication and cooperative driving between cars on the road. There are strongeconomical interests in this field since vehicle-to-vehicle communication allows to improvetraffic safety, to improve route planning, or to control traffic congestion.The 802.11p is adraft amendment to the IEEE 802.11 standard for vehicular communications. It has beenadopted by Wireless Access in Vehicular Environments, which defines an architecture tosupport Intelligent Transportation Systems (ITS).For this purpose, we first examine and then display the simulation findings of theimpact of different radio propagation models on the performance of vehicular ad hocnetworks. We have compared the performances of two routing protocols (AODV and OLSR)for three propagation model (two-Ray ground, Rice and Nakagami). We study thoseprotocols under varying metrics such as mobility of vehicle and size of the scenario areas.Our objective is to provide a qualitative assessment of the protocols applicability in differentvehicular scenarios. These two routing protocols are simulated and compared with NetworkSimulator-2 under Manhattan Grid Mobility Model.Keywords: Propagation model, Routing protocols, OLSR, AODV, VANET.INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING& TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 2, March – April (2013), pp. 556-568© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)www.jifactor.comIJCET© I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME5571. INTRODUCTIONThe development of VANETs is backed by strong economical interests since vehicle-to-vehicle (V2V) communication allows to share the wireless channel for mobileapplications, to improve route planning, to control traffic congestion, or to improve trafficsafety. Besides, the vehicular communication radio depends on several parameters such as theemission power, the environment where the waves spread and the utilized frequency also playa crucial role. The radio propagation waves are controlled by strict rules, mainly when thereare obstacles between the transmitter and the receiver [2], [3]. Among the changes a wavemay undergo, we can cite: reflection, diffraction, diffusion and absorption. This study isorganized as follows. We give three radio propagation models types. Then we discuss ofrouting protocols concepts in vehicular ad hoc networks. In addition, we declare themethodologies of simulation. Finally, we investigate the impact of radio propagation modelson the performances of routing protocols in VANETs and we present our conclusions.Fig. 1. Model of urban displacement2. RADIO PROPAGATION MODELSIn a propagation model, we use a set of mathematical models which are supposed toprovide an increasing precision. Propagation radio models are three types: path loss,shadowing and fading [4]. The first type can be expressed as the power loss during the signalpropagation in the free space. The second type is characterized by fixed obstacles on the pathof the radio signal propagation. The third category is the fading which is composed ofmultiple propagation distances, the fast movements of transmitters and receivers units andfinally the reflectors. In this work, we study three propagation models: Two-Ray Ground,Rice and Nakagami.2.1 Two-ray ground modelA single line-of-sight path between two mobile nodes is seldom the only means ofpropagation. The two-ray ground reflection model considers both the direct path and a groundreflection path [5]. This model gives more accurate prediction at a long distance than the freespace model. The received power is represented by Eq. 1:
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME5582 2t t r t rr 4PG G h hP (d)d L=(1)Where, ht and hr are the heights of the transmitter and receiver respectively.Nonetheless, for short distances, the two-ray model does not give accurate results because ofin oscillation caused by the constructive and destructive combination of the two rays. Thepropagation model in the free space is instead, still used where d is small. Hence, in thismodel, we calculate dc as a cross-over distance. When d<dc, we use the free space equation,but when d > dc, the equation (1) is used. Consequently, dc can be calculated as Eq. 2:t rc4 h hdπ=λ(2)2.2 Rice modelThis fading model depicts the rapid fluctuations of the received signal due tomultipath fading. This fading phenomenon is generated by the interference of at least twotypes of transmitted signals to the receiver with slight time intervals [6]. The outcome mayvary according to fluctuations and to different phases in terms of multiple factors such as:delay between waves, the intensity and the signal band width. Hence, the system performancemay be attenuated by the fading. However, there are several techniques that help stoppingthis fading. The signal fading were monitored according to a statistical law wherein the mostfrequently used distribution is Raleigh’s [7]. The transmitted signal is, thus, conditioned bythe following phenomena: reflection, scattering and diffusion. Thanks to these threephenomena, the transmitted power may reach the hidden areas despite the lack of directvisibility (NLOS) between the transmitter and receiver. Consequently, the amount of thereceived signal has a density of Rayleigh Eq. 3:( )22x xexp( ), pour 0 xf x P P0 , pour x 0− ≤ ≤ ∞=  <(3).Where, P is the average received power. In case where there is a direct path (LOS)between the transmitter and receiver, the signal no longer obeys to Rayleighs law but toRice’s. The probability density of Rice is represented by Eq. 4:( ) 20K 1 x2x(K 1)exp K IP PK(K 1)f(x) 2x , pour 0 xP0 , pour x 0  ++− −      += ≤ ≤∞    <(4)Where:K, the ratio of the power received in the direct line and in the pathP, the average power receivedI0 (x), the zero-order Bessel function de fined by Eq. 5:
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME55920 01I (x) exp( x cos )d2π= − θ θπ ∫ (5)The density of Rice is reduced to the density of Rayleigh in the case of an absence ofa direct path which means that K = 0 and thus I0 (x) =1.2.3 Nakagami modelThis distribution encompasses several other distributions as particular cases. Todescribe Rayleigh distribution, we assumed that the transmitted signals are similar and theirphases are approximate. Nakagami model is more realistic in that it allows similarly to thesignals to be approximate. Since we have used the same labels as in Rayleigh and Rice cases,we have ∑= ijierr θ. The probability density of Nakagami related to r is represented by Eq. 6:( )( )m 2m 1 2r m2m r mrP r exp , r 0m− = − ≥ Γ Ω Ω (6)Where, Γ(m) is gamma function, Ω = (r2) and m = {E (r2)}2/var (r2) with theconstraint m≥1/2. Nakagami model is a general distribution of fading which is reduced toRayleigh’s distribution for m = 1 and to unilateral Gaussian model for m = 1/2. Besides, itrepresents pretty much rice model and it is closer to certain conditions in the lognormaldistribution.3. AD HOC ROUTING PROTOCOLSVehicular Ad-hoc Networks (VANETs) are characterized by a very high nodemobility and limited degrees of freedom in the mobility patterns. Hence, ad hoc routingprotocols must adapt continuously to these unreliable conditions, whence the growing effortin the development of communication protocols which are specific to vehicular networks.Oneof the critical aspects when evaluating routing protocols for VANETs is the employment ofmobility models that reflect as closely as possible the real behavior of vehicular traffic. In thispaper, we compare the performance of two prominent routing protocols AODV and OLSR inurban traffic environment.Ad hoc routing protocols are based on fundamental principles ofrouting such as: Inundation (flooding), the distance Vector, the routing to the source and thestate of the site. According to the way routes are created and maintained during the datadelivery [8]. Here is a summary of the routing protocols assessed in this study.3.1 Ad-hoc On-Demand Distance Vector protocol (AODV)AODV has a way for route request close to that of DSR. However, AODV does notperform a routing to the source. Every single node on the path refers to a point towards itsneighbour from which it receives a reply. When a transit node needs broadcasts a routerequest to a neighbour, it also stores the node identifier in the routing table from which thefirst reply is received. To check the links state, AODV uses control messages (Hello) betweendirect neighbours. Besides, AODV utilizes a sequence number to avoid a round trip and toensure using the most recent routes [9].
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME5603.2 Optimized Link State Routing Protocol (OLSR)OLSR [10], [11] is proactive routing protocol or table driven protocol. Initially nodeshave routing tables and they update their routing tables time to time. It is based on the link-state algorithm. Each node maintains the topology information of network and sending thisinformation from time to time to neighbors. The uniqueness of OLSR is that it minimizes thesize of control messages and rebroadcasting by using the MRP (Multipoint Relaying). Thebasic concept of MPR is to reduce the loops of retransmissions of the packets. Only MPRnodes broadcast route packets. The nodes within the network maintain a list of MPR nodes.MPR nodes are selected within the environs of the source node. The selection of MPR isdone by the neighbor nodes in the network, with the help of HELLO messages.4. METHODOLOGYIn this study, on one hand we study the impact of different propagation models inorder to analyze the environment effect on the VANETs performance. On the other hand, wecompare two routing protocols performances (AODV and OLSR) according to threepropagation models. The assessment is twofold: First, we diversified the nodes’ speed.Second, we altered the size of the scenario areas. The propagation models under study are:the two-Ray ground, Rice’s and Nakagami’s models. The simulation span is of 200 sec. Thedata packet size is 512 octets.Since the Random Waypoint Model is considered unrealistic[12] and [13], a mobility model clearly affects the simulation results. This mobility model donot consider vehicles’ specific patterns, they cannot be applied to simulation of vehicularnetworks in urban Area. Accordingly, we have chosen Manhattan Grid Mobility Model [14],this Model is similar to City Section Mobility Model, and he uses a grid road topology, asshown Figure. 1. This model is implemented in the BonnMotion framework [15]. This modeladds traffic density like in a real town, where traffic is not uniformly distributed; so, there arezones with a higher vehicle density. These zones are usually in the downtown, and vehiclesmust move more slowly. The evaluation is done in two scenarios, in the first scenario wehave varied the nodes speed and in a second we have varied the size of the scenario areas.4.1 Scenario 1So as to analyze the routing protocols’ behaviour, we selected traffic sources with aconstant output (CBR) related to UDP protocol. The packet emission rate is settled at 8packets per second with a maximal speed variation of nodes. Ten speed values wereconsidered: 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 m/sec. The assessed protocols are: AODV andOLSR. These two are available in 2.34 of ns-2. At the moment, we limit the number ofsources in 10 and we analyze the impact of the nodes’ speed.4.2 Scenario 2In this section we show the simulation results when we varying the size of the area,maintaining unaltered the number of nodes and the rest of parameters. We selected scenarioareas of 1400*700m, 1600*800m, 1800*900m, 2000*1000m and 2200*1100m. The numberof nodes is set to 40 vehicles. Let’s limit the nodes’ maximal speed at 10 m/s while the otherparameters are similar to those in the first case.
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME5614.3 Performance indicatorsBecause of the length chosen in this study, we have selected just three performanceindicators in order to study the routing protocols performances. They are outlined as follows:Packet delivery fraction, end average to end delay and the throughput.a. Packet Delivery Fraction (PDF)This is the ratio of total number of CBR packets successfully received by thedestination nodes to the number of CBR packets sent by the source nodes throughout thesimulation:nrecv100 nsent1CBRPkt _ Delivery 100CBR= ×∑∑This estimation gives us an idea of how successful the protocol is in deliveringpackets to the application layer. A high value of PDF indicates that most of the packets arebeing delivered to the higher layers and it is a good indicator of the protocol performance.b. Average End-To-End Delay (AE2E Delay)This is defined as the average delay in transmission of a packet between two nodesand is calculated as follows:( )nsent _ Time recv _ Time1nrecv1CBR CBRAvg_ End _ to_End_delayCBR−=∑∑c. ThroughputThe throughput data reflects the effective network capacity. It is computed bydividing the message size with the time it took to arrive at its destination. It is measuredconsidering the hops performed by each packet.5. RESULTS AND DISCUSSIONIn this part, we display the study findings about the impact of the nodes’ maximalspeed and the size of the scenario areas, on the routing protocols; according to the threeaforementioned performance indicators: packets Delivery fraction, Throughput and averageend to end delay.5.1 Scenario 1The results corresponding to the PDF, AE2E Delay and throughput are shown infigure 2-4 respectively.5.1.1 Packet delivery fractionIn figure 2, we notice the packet delivery fraction decrease according to the speedincrease. Consequently, the links are weaker with speed; the main reason for the packet lossis mobility, congestion and the wireless channel characteristics.
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME562Fig. 2: (a) AODV- PDF versus SpeedFig. 2: (b) OLSR- PDF versus SpeedMeanwhile, we notice that the two-ray ground deliver more packets than Rice andNakagami, the bad performance of these two last models is due to the low intensity of thesignal caused by the obstacles. This results in the packet loss on weak links, displays wronglythe links disconnection and leads to the interruption and thus the dire need to set up a newitinerary.The Rice and Nakagami Models are most appropriate to simulate urban scenarios.OLSR present the bad delivery rate of data packets, OLSR uses wrong routes to send data.5.1.2 Average end-to-end delaySimilarly to PDF, we notice that the two-ray ground endure less delay than the twoother models. The nodes’ mobility has an influence on every metric; in other words, itinfluences mainly the end-to-end delay.
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME563Fig. 3: (a) AODV-AE2E Delay versus SpeedFig. 3: (b) OLSR-AE2E Delay versus SpeedThe AODV protocol has an end-to-end delay considerably higher than OLSR. Hence,the transmitted data packets will be deleted once they reach their broken links. In addition,the data packets undergo extra delays during the communication interfaces’ waiting becauseof the frequent retransmissions. This latency causes the packets death (their deletion).5.1.3 ThroughputAs we expected, the throughput decreases slightly when the speed increases because ithas to find the path for more routing traffic delivery. Therefore, the channel will be less usedfor the data transfer to as to reduce the useful throughput. We notice that the Two-Ray Grandmodel is more efficient than Rice and Nakagami models; the bad performance of these twolast models is due to the low intensity of the signal caused by the obstacles.
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME564Fig. 4: (a) AODV-Throughput versus speedFig. 4: (b) OLSR-Throughput versus speed5.2 Scenario 2: Varying the scenario sizeThe results corresponding to the PDF, AE2E Delay and Throughput are shown inFigure 5-7 respectively.5.2.1 Packet delivery fractionWhen there are increases in the size of the scenario, the density nodes decreases. Thetotal number of packets received decreases. By increasing the size of the simulated scenarioincreases the block size, this prevents direct communication through the blocks and thenlimits the spread and increases the radio losses of data packets which resulted to a decrease ofuseful throughput and increase the number of nodes blind.By increasing the size of thesimulated scenario increases the block size, this prevents direct communication through theblocks and then limits the spread and increases the radio losses of data packets which resultedto a decrease of useful throughput and increase the number of nodes blind. The block sizes inthe topology play an important role in determining the performance of VANETs. With largeblock sizes, vehicles spend more time in traversing between intersections; thus, nodes aremobile more often. This increased mobility leads to a weakened connectivity in the network,and a corresponding drop in the delivery ratio.
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME565Fig. 5: (a) AODV- PDF versus size of the areaFig. 5: (b) OLSR- PDF versus size of the area5.2.2 Average end-to-end delayFigure 6, depicts the Average end-to-end delay. As can be seen, when the areaincreases, the system needs more time to inform the vehicles. As can be observed in figure,the percentage of blind nodes highly depends on this factor. When the area is very small, thepercentage of blind nodes is also very small.Fig. 6: (a) AODV-AE2E Delay versus size of the area
  11. 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME566Fig. 6: (b) OLSR-AE2E Delay versus size of the areaWhen the size of the area increases, the number of blind nodes also increases.Neverthe-less, the number of packets received per node decreases. We note that, if the size ofthe urban area decreases (the density of nodes increases), and the number of link nodesincreases, which reduces the end to end delay, as well, the percentage of mobile blinddecreases. AODV protocol has a delay significantly higher than OLSR.5.2.3 ThroughputFigure 7, illustrate the variation of throughput as a function of the scenario size. Asexpected, the Two-Ray Grand model offers the best values of Throughput than Rice andNakagami models. The percentage of vehicles blind depends strongly on the size of the area.OLSR has a throughput slightly higher than AODV.Fig. 7: (b) AODV-Throughput versus size of the area
  12. 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME567Fig. 7: (b) OLSR-Throughput versus size of the area6. CONCLUSIONS AND PERSPECTIVESIn this article, we have study the impact of different radio propagation models on theperformance of vehicular ad hoc networks. According to the simulation findings, we maystate that the choice of the propagation models has a great impact on the routing protocol’sperformance. The latter decreases rapidly when the fading models, mainly Ricean andNakagami have been taken into consideration. The main reasons of their deterioration are theoutcome of the big variation in the received intensity signal.In this paper, we have evaluated the performance of AODV and OLSR for vehicularad hoc networks in urban environments. We have tested OLSR and AODV against mobilityof vehicle and size of the scenario areas. Globally, for most of the metrics we have used inthis paper, OLSR has better performance that AODV. Indeed, OLSR has smaller routingoverhead and end-to-end delay. For the PDR, where OLSR may be outperformed by AODV.We have also illustrated in this paper, that the average velocity was not a valid parameter toevaluate routing protocols in VANET. Accordingly, one should rather evaluate ad hocprotocols against new metrics, such as acceleration/ deceleration, or the length of streetsegments instead of simple average mobility. We can also say that, the propagation delay islower when node density increases. Besides, the percentage of blind nodes highly depends onthis factor. When the area increases, the system needs more time to inform the rest of thevehicles and the percentage of blind nodes highly depends on this factor, too. When the areais very small, the percentage of blind nodes is also very small. When the area increases, thenumber of blind nodes also increases. Nevertheless, the total number of packets received pernode decreases.In the forthcoming studies, we plan to include geographical forwarding protocols infuture performance evaluation as they are more suited to dense networks; we will look at therouting protocols’ behaviors in the multi-channel environment and/or multi-networks in orderto determine the key parameters that have an impact on the protocols’ choice.
  13. 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME568REFERENCES[1] Olariu S., Weigle M. C. ( 2009): Vehicular Networks: From Theory to Practice. CRCPress, A Chapman & Hall Book[2] Kaya, A. and L. Greenstein, 2009: Characterizing indoor wireless channels via raytracing combined with stochastic modelling. IEEE Transactions on WirelessCommunications, Volume 8 Issue 8, pp.: 4165 – 4175. DOI: 10.1109/TWC.2009.080785[3] A. Rhattoy, M. Lahmer, « Simulation de La Couche Physique Dans Les RéseauxMobiles », RNIOA’08, 05-07 Juin 2008, Errachidia, Maroc[4] Arne Schmitz, Martin Wenig, "The effect of the radio wave propagation model inmobile ad hoc networks", MSWiM 06 Proceedings, ACM New York, NY, USA 2006, doi:10.1145/1164717.1164730, Pages 61-67.[5] Pranav K and Kapang L, 2011: Comparative Study of Radio Propagation andMobility Models in Vehicular Adhoc Network. IJCA Journal, Number 8, Article 6, pp.; 37-42[6] Amjad, K.; Stocker, A.J.; 2010: Impact of slow and fast channel fading and mobilityon the performance of AODV in ad-hoc networks. Antennas and Propagation Conference(LAPC), 8-9 Nov. pp.: 509–512. DOI: 10.1109/LAPC.2010.5666192[7] Carvalho, M, 2004. Modeling single-hop wireless networks under Rician fadingchannels. Proceedings of the IEEE Wireless Communications and Networking Conference,(WCNC’ 04), pp: 219-224. http://www.citeulike.org/user/marcelocarvalho/article/1049438[8] Feeney, L.M., 1999. A taxonomy for routing protocols in mobile ad hocnetworks.SICS Report.http://eprints.sics.se/2250/[9] Pallavi Khatri, and Monika Rajput, 2010. Performance Study of Ad-Hoc ReactiveRouting Protocols. Journal of Computer Science, Volume: 6, pp.: 1159-1163[10] Christopher Dearlove and Thomas Clausen, “The Optimized Link State RoutingProtocol version 2”, IETF Draft RFC draft-ietf-manet-olsrv2-10, September 2009[11] Andreas Tonnesen, “Implementing and extending the Optimized Link State RoutingProtocol”, Master’s thesis, University of Oslo, Department of Informatics, 2004[12] A. Rhattoy and A. Zatni : “The Impact of Radio Propagation Models on Ad HocNetworks Performances,” Journal of Computer Science, Volume 8, Issue 5, 752-760, 2012[13] A. Rhattoy, A. Zatni, “Physical propagation and Traffic Load Impact on thePerformance of Routing Protocols and Energy Consumption in Manet”, IEEE Xplore,(ICMCS12), pp.: 767 – 772, DOI : 10.1109/ICMCS.2012.6320247[14] Geetha, J and G. Gopinath, 2008: Performance Comparison of Two On-demandRouting Protocols for Ad-hoc Networks based on Random Way Point Mobility Model.American Journal of Applied Sciences, Vol: 5, pp.: 659-664[15] BonnMotion, a mobility scenario generation and analysis tool,http://web.informatik.unibonn.de/IV/ Mitarbeiter/dewaal/BonnMotion/[16] S. A. Nagtilak and Prof. U.A. Mande, “The Detection of Routing Misbehavior inMobile Ad Hoc Networks using the 2ack Scheme with OLSR Protocol”, International journalof Computer Engineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp. 213 - 234,ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.[17] R.Boopathi and R.Vishnupriya, “Performance Evaluation of AODV and OLSR inVANET under Realistic Mobility Pattern”, International Journal of Electronics andCommunication Engineering &Technology (IJECET), Volume 4, Issue 2, 2013, pp. 58 - 71,ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.

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