Random and Realistic Mobility Models Impact onthe Performance of Bypass-AODV Routing Protocol

Ahed Alshanyour and Uthman Baroudi

Abstract—Due to lack of mass deployment of wireless ad hocnetworks, simulation is the main tool to evaluate and comparedifferent routing protocol proposals. Previous studies have shownthat a certain routing protocol behaves differently under differentpresumed mobility patterns. Bypass-AODV is a new optimizationof the AODV routing protocol for mobile ad-hoc networks.It is proposed as a local recovery mechanism to enhance theperformance of AODV routing protocol. It shows outstandingperformance under Random Waypoint mobility model comparedwith AODV. However, Random Waypoint is a simple model thatmay be applicable to some scenarios but it is not sufficient to cap-ture some important mobility characteristics of scenarios whereMANETs deployed. In this work, we would like to investigatethe performance of Bypass-AODV under wide range of mobilitymodels including other random mobility models, group mobilitymodels and vehicular mobility models. Ns-2 simulation resultsshow that Bypass-AODV is insensitive to the selected randommobility model and it has a clear performance improvementcompared to AODV. It shows a comparable performance undergroup mobility model compared to AODV. But, for vehicularmobility models, Bypass-AODV is suffering from performancedegradation at high speed conditions.

Index Terms—Ad hoc networks, routing protocols, AODV,bypass-AODV, mobility models.

I. INTRODUCTION

RESEARCH has gained a significant advancement in thedevelopment of routing protocols for wireless ad hoc

networks [3], [4]. The movement pattern of mobile nodesplays an important role in performance analysis of mobile andwireless networks. Additionally, mobility has a major effecton the route stability and availability. For example, to keep oncommunication, signaling traffic is needed for route construc-tion and subsequent route maintenance. The extra signalingtraffic over the air interface consumes radio resources andincreases the interferences that affect the performance of othermobile nodes. Therefore, the modeling of movement is thusan essential building block in analytical and simulation-basedstudies of these systems. Moreover, some researchers [6], [7]have observed that the performance of routing algorithms maybe affected by the choice of mobility models. For example,random models are not a good choice to simulate the realworld mobility scenarios because usually mobile users eithermove towards certain attraction points such as classrooms ortrain stations, or move in certain directions such as vehicles.However, some attempts have been made to implement specific

Alshanyour and Baroudi are with the College of Computer Science and En-gineering, King Fahd University of Petroleum and Minerals. Email: shanyour,ubaroudi@kfupm.edu.sa

The authors acknowledge King Fahd University of Petroleum and Mineralsfor its support, project FT2005-16.

mobility scenarios that are more realistic [5], [8]. However,implementing a general realistic mobility models is a hardsolution because mobility requirement in MANET changeddue to the application environments. Indeed, the choice of themobility model has a significant effect on the obtained results.If the model is unrealistic, invalid conclusions may be drawn.

Bypass-AODV [9] is one of the recent developed routingprotocols. It is an optimization of the AODV routing protocol[3] for mobile ad-hoc networks. It uses a specific strategyof cross-layer MAC-notification to identify mobility-relatedpacket loss, and then setup up a bypass between the nodeat which the route failure occurred and this node’s previoussuccessor via an alternative node. By restricting the bypass toa very small topological radius, route adaptations occur onlylocally and communication costs are small. Random Waypoint(RWP) mobility model used to evaluate the performance ofBypass-AODV. Performance evaluation shows a clear perfor-mance gain over the conventional AODV [9].

RWP model is a simple and a general model that is easy toanalyze and implement and it does not reflect the mobile nodesmovement pattern in real life applications. Therefore, to ana-lyze the performance of any new routing protocol thoroughlyand systemically, there is a need to use mobility models thatemulate the real life applications. Otherwise, the observationsmade and the conclusions drawn from the simulation studiesmay be misleading. This study has two main objectives

– To study the impact of other well-known random mobilitymodels, Random Walk (RW)[5] mobility model and RandomDirection Mobility (RDM) model [6], on the performance ofBypass-AODV.

– To evaluate the performance of the proposed protocolwith real life applications by using one of the group mobilitymodels, Reference Point Group Mobility (RPGM) model [11],and some vehicular mobility models, Freeway (FRW) andManhattan (MAN) mobility models [5].

Simulation results show that Bypass-AODV routing pro-tocol is insensitive to the random mobility pattern used insimulation. For group mobility models, Bypass-AODV andAODV have similar performance. However, Bypass-AODVis not a suitable choice for VANET applications due to theunnecessary increase in the route length.

The remainder of this paper is as follows. In Section 2, webriefly present the AODV routing protocol then we presentour enhanced local recovery routing scheme, Bypass-AODV,and outline its advantages. Section 3 describes some com-monly used mobility models and their applications. Section4 presents the simulation environment. Section 5 considersthe performance of Bypass-AODV under selected mobility

978-1-4244-2829-8/08/$25.00 ©2008 IEEE

models. Finally, Section 6 concludes the paper.

II. AODV AND BYPASS-AODV

A. AODV Routing Protocol

Ad-hoc On-demand Distance Vector (AODV) [3] is a proac-tive routing protocol used by mobile nodes in an ad hocnetwork. Flooding is the main mechanism for route discovery.To find a route to a particular node, the source node broadcastsa route request (RREQ) message to its neighbor nodes andthen the process continues. As the RREQ message reaches therequested destination or a node with a fresh-enough route1, aroute reply (RREP) message is sent back to the source node.To ensure loop-free and route freshness properties, destinationsequence numbers attached to the RREQ messages. Nodesmonitor their next hop links status continuously. Link breakeither maintained locally or invalidated the correspondingroutes that use that link. Route invalidation extends to includeall other nodes that using the lost link.

B. Bypass-AODV

Bypass-AODV uses cross-layer MAC-notification to iden-tify mobility-related packet loss and then triggers the routinglayer to start a local repair process, which allows upstreamnode of the broken link to do the repair by setting up a bypassbetween it and the downstream node via an alternative node.A MAC-notification message is used to distinguish betweenmobility-related packet loss and other sources-related (signalinterference, high error rate, fading environment and packetcollision) packet loss. Bypassing mechanism works with aspecified time-to-live value in order to limit the area of routesearch. Based on spatial locality where a node cannot movetoo far too soon. Thus with high probability, the new distancebetween the broken link end nodes will not exceed 2 hops.Therefore, route bypassing will minimize routing overheads.In contrary to AODV local repair mechanism, Bypass-AODVhas the ability to repair the broken route regardless of break’slocation and consequently it will minimize packet losses.Packet losses occur when route bypassing does not work,for example, the distance between the upstream and thedownstream nodes of a broken link is more than 2 hops. Insuch case, no construction for a bypass with the downstreamnode and a route error message after some timeout valuepropagates towards the source node. Route bypassing resultsin an unnecessary increase in hop count metric. This increasedepends on the frequency of route’s bypassing. To handle thisissue, bypass-route is a temporary route lasts for a period thatis enough to guarantee that packets left their source node willreach their final destination. 5

III. MOBILITY MODELS

In general, mobility models can be categorized into twocategories: entity and group mobility models. Entity mobilitymodels represent the behavior of an individual node or groupof nodes independently from other nodes. Group mobility

1A valid route entry to the requested destination with an associated sequencenumber that is greater or equal the one contained in the RREQ.

models, on the contrary, takes into account the interactionamong individual mobile nodes. Group mobility models aremore suitable for some ad hoc networks scenarios, such asgroups of soldiers in military actions. In this Section, Inaddition to RWP model, we will briefly discuss two otherrandom mobility models: RW and RDM models. Next, wediscuss RPGM, FRW and MAN models.

A. Random Walk Mobility Model (RW)

This model originally proposed to emulate the unpredictablemovement of particles in physics. Each node randomly anduniformly selects its new direction θ(t) from (0, 2π] and speedv(t) from (0, Vmax]. During the time interval t, the node moveswith the velocity vector (v(t)cosθ(t), v(t) sin θ(t)). As thenode reaches the boundary of simulation region, it bouncedback to the simulation region with an angle of θ(t) or π−θ(t).

B. Random Waypoint Mobility Model (RWP)

In RWP, each node randomly selects a position, and movestowards that location with a constant speed chosen uniformlyand randomly from (0, Vmax], where Vmax represents themaximum allowable speed for the mobile node. Once themobile node reaches that position, it becomes stationary for apredefined pause time, Tpause. After that pause time, it selectsanother random location within the simulation region andmoves towards it. The whole process continuously repeateduntil the end of the simulation time. Vmax and Tpause are twokey parameters that define the mobility behavior of mobilenodes. If Vmax is small and Tpause is large, the networktopology is expected to be stable. On the other hand, largeVmax and small Tpause will produce a highly dynamic networktopology.

RWP is widely accepted mainly due to its simplicity ofimplementation and analysis. However, RWP fails to capturethe characteristics of temporal dependency (the velocities attwo different time slots are dependent), spatial dependency(the movement pattern of mobile nodes may be influenced byand correlated with nodes in its neighborhood) and geographicconstraints (nodes movement are restricted by obstacle, alongstreets and along freeways) [5].

C. Random Direction Mobility Model (RDM)

The spatial node distribution of RWP is transformed fromuniform node distribution to non-uniform distribution as thesimulation time elapsed and finally it reached a steady state.In steady state, the mobile nodes are concentrated at the centerregion and almost zero around the boundaries [10], [12]. RDMmodel [13] was proposed to overcome such phenomenon. InRDM, the node randomly and uniformly chooses a directionand move along it until it reaches a boundary. After reachingthe boundary and stopping for some Tpause, it then randomlyand uniformly chooses another direction to travel.

D. Reference Point Group Mobility Model (RPGM)

RPGM model emulates group movement patterns. InRPGM, mobile nodes inside the simulated region form certain

groups. Each group has a group leader that determines thegroup members’ motion behavior. It acts as a reference pointfor that group. Group members’ mobile nodes randomly moveabout their own pre-defined reference points with a speedvector Vmember(t) and direction vector θmember(t) that isderived by randomly deviating from that of the group leadervelocity and direction, (Vleader(t), θleader(t)) , respectively. ASpeed Deviation Ratio (SDR) and an Angle Deviation Ration(ADR) used to control the deviation of the velocity vector ofgroup members from that of the leader.∣∣∣~Vmember

∣∣∣ =∣∣∣~Vleader

∣∣∣ + rand(.) ∗ SDR ∗maxs (1)∣∣∣~Θmember

∣∣∣ =∣∣∣~Θleader

∣∣∣ + rand(.) ∗ADR ∗maxa (2)

Where , maxs and maxa are used to limit the maximum speedand the maximum angle the group member can take. Since themovements of the group’s members controlled by the group’sleader movement, this mobility model expected to have highspatial dependency for small values of SDR and ADR.

E. Freeway Mobility Model (FRW)

FRW is proposed to emulate the motion behavior of mobilenodes on a freeway (exchange the traffic status or track avehicle on a freeway). In this model, each freeway has severallanes in both directions. Thus, the mobile node movementis restricted to its lane on the freeway (strict geographicrestrictions on the node movement) and its velocity at differentinstants of time is temporally dependent. Moreover, mobilenodes movement on the same lane is spatially dependentbecause in addition to keep on a Safe Distance (SD) fromthe preceding vehicle, the following vehicle’s speed is alsoconstrained by the speed of its preceding vehicle.∣∣∣~Vi(t+ 1)

∣∣∣ =∣∣∣~Vi(t)

∣∣∣ + rand(.) ∗ |~ai(t)| (3)

∀i, j, t, Di,j(t) ≤ SD ⇒∣∣∣~Vi(t)

∣∣∣ ≤ ∣∣∣~Vj(t)∣∣∣ (4)

F. Manhattan Mobility Model (MAN)

MAN is proposed to emulate the movement of mobile nodeson streets defined by maps. In this model, there are horizontaland vertical streets and each street has two lanes for eachdirection. A mobile node can probabilistically move straight,turn right, or turn left at the intersections with probabilitiesof 0.5, 0.25 or 0.25 respectively. In this model, mobile nodemovement has the same restrictions as in FRW and the samevelocity equations are applicable. MAN is expected to havespatial dependency, strong temporal dependency and strictgeographic restrictions on the node movements.

IV. SIMULATION ENVIRONMENT

We implement a simulation model in ns-2 [1] to evaluatethe performance of TCP used in conjunction with the Bypass-AODV. IEEE 802.11 MAC standard [2] and TCP New-Reno are used at the MAC and TCP layers, respectively.The radio model is the ground radio propagation model.In each simulation-iteration, we generate a scenario with a

TABLE IEVALUATION PARAMETERS

Parameter Value

Transmission range(Rx) 180mInterference range 400m

Transmission bit rate 2MbpsSignal to noise ratio (SNR) 10dB

Simulation region 1000m x 1000mNumber of nodes 60

Number of TCP connections 1Session interval 150 secSimulation time 160 sec

Maximum speed (Vmax) 1, 5, 10,20,30, and 40 m/sec

Packet size 1060 bytePause time(Tpause) 0 sec

SDR and ADR 0.1

source-destination pair is randomly and uniformly chosen. Thesimulation results reported in the next section represent theaverage results over 6000 different scenarios. Each readingis averaged over 30 independent runs. The velocity for eachnode randomly and uniformly selected from (0, Vmax]. TableI shows the values of all parameters used in the simulation.

The following metrics are computed to evaluate the impactof mobility model on the performance of the Bypass-AODVas well as the original AODV:

– The routing overhead ratio is the ratio of the amount inbytes of control packets transmitted to the amount in bytes ofdata packets received.

– The absolute goodput of the TCP is the number ofsequenced bits that a TCP receiver received per time.

– The ”goodput improvement ratio” is the TCP goodputobserved with a Bypass-AODV strategy as compared to thestandard AODV routing strategy.

V. SIMULATION RESULTS AND DISCUSSION

In this section, we examine the impact of different randommobility models as well as group and vehicular mobilitymodels on the performance of Bypass-AODV and AODV.

A. Impact of random mobility models on Bypass-AODV

In addition to RWP, RW and RDM models are used to eval-uate the performance of Bypass-AODV compared to AODV.The maximum velocity for each node is 20m/s. Our objectiveis to study the performance of Bypass-AODV on both longand short TCP connections(in terms of hop-count). Thus, wemake the TCP connection’s end nodes static while all the othernodes are allowed to move in accordance with the mobilitymodel. As a result, the physical distance2 between the sourceand the destination of a TCP connection relatively unchangedduring a simulation run.

From fig. 1, bypass-AODV and AODV have similar TCPgoodput when the two end nodes are close to each other.When the physical distance between the two end nodes is

2The minimum distance between TCP connection end nodes in terms ofthe number of hops, assuming nodes use their maximum transmission range(180m).

Fig. 1. TCP goodput for Bypass-AODV routing protocol

1 hop, the two end nodes are in direct communication andthere is no possibility of link failure. Thus, Bypass-AODVhas the same goodput regardless of the random mobility modelused in simulation. As the physical distance becomes 2 hops,the two end nodes are communicating via an intermediatenode. In such scenario, all communicating nodes are withinthe sensing range of each other and thus one transmissiononly allowed at any given time. Therefore, any link failureis mobility-related. At this physical distance, the probabilitythat the two end nodes exist at the center of the simulationarea is high. Therefore, Bypass-AODV shows better goodputwith RWP because the center region has higher node densitythan the boundaries. In RW and RDM, nodes most likely areuniformly distributed over the simulation area. Therefore, theaverage route lifetime is small compared to RWP due to thecontinuous node mobility. For number of hops more than 3,the connection end nodes start approaching the boundaries.As a consequence, Bypass-AODV show clear enhancement inperformance with RW and RDM models due to the uniformdistribution of nodes. In RWP, the probability of networkpartitioning is high due to the non-uniform distribution ofmobile nodes. Fig. 3 compares between the performance ofBypass-AODV and AODV. It shows a clear improvement inthe TCP goodput ratio especially for long TCP connections.When the physical distance than 3 hops, there is a possibilityof simultaneous contention on the transmission medium (collision). Collision causes unsuccessful packet transmission.802.11 MAC translates unsuccessful packet transmission intolink failure. Therefore, there is a need for an efficient MACmechanism that distinguishes mobility-related failures fromother sources-related failures such as contention. Existence ofsuch mechanism will reduce the frequency of route mechanisminvocation and minimize routing overheads and packet drops.

B. Impact of group mobility models on Bypass-AODV

In this experiment setting, we try to show how the per-formance of routing protocols may depend on the movementpattern used in simulation. For RPGM model, we use 4 groupsof 15 nodes each moving independently of each other and inan overlapping fashion.

Fig. 3 shows that Bypass-AODV routing protocol has slightenhancement in goodput at high speeds and similar perfor-mance at low speeds, fig. 4 shows the goodput improvementratio. The similarity in performance can be attributed to that

Fig. 2. Goodput improvement ratio (Bypass-AODV / AODV)

Fig. 3. Goodput (Bypass-AODV and AODV)

Fig. 4. Goodput improvement ratio (Bypass-AODV/AODV)

fact that both routing protocols have short connection duringthe most of time. Table II shows that about 98% of the receivedTCP packets have short hop count (≤ 3). In a previouswork [9], we have shown that Bypass-AODV and AODVhave similar performance for short distance TCP connections.Bypass AODV effectively minimizes packet drops by bufferingthe data packets for subsequent transmission after doing routebypassing. However, a bypassed route is a temporary route thatlasts for a period of time that is enough to forward the bufferedpackets, and then a new route discovery mechanism will start.Therefore, it is expected that routing overhead in Bypass-AODV will be increased compared to AODV as shown fromfig. 5. On the other hand, increasing the speed will increasethe possibility of overlapping between groups and then shortenthe physical distance between the connection end nodes ifthey exist at different groups. Furthermore, fig. 3 shows howspatial dependency enhances the performance of both routingprotocols. It shows that RPGM movement pattern doubled thegoodput of both routing protocols compared to RWP. Thisconsiderable enhancement in goodput is due to the spatialdependency nature of the RPGM model, which increases thelifetime of the routes.

TABLE IITHE CONNECTION HOP COUNT DISTRIBUTION(HC)

Mobility model Short Medium Longhc ≤ 3 3 < hc ≤ 6 hc > 6

RPGM 98% 2% 0%FRW 84% 10% 6%MAN 72% 22% 6%

Fig. 5. Routing overhead ratio

C. Impact of vehicular mobility models on Bypass-AODV

Vehicular mobility models, FRW and MAN, are adoptedto evaluate the performance of Bypass-AODV and then tocompare it with AODV. Initially, the nodes placed on thefreeway lanes or local streets randomly in both directions.Their movement is controlled as per the specification of themodels.

In each experiment setting, the direction of movement ofthe communicating end nodes forms two groups of scenarios.The first group has scenarios with same direction and thesecond group has scenarios with opposite or perpendiculardirection. In FRW, the first group has about 50% of scenariosand the second group has the remaining. Due to the existenceof horizontal and vertical streets in MAN model, the firstgroup has about 25% of scenarios while the second grouphas about 75%. First group’s movement pattern is similar tothat in RPGM, which enhances the performance of the rout-ing protocol. Moving in opposite or perpendicular directionleads to frequent and fast route failures especially at highspeeds. Therefore, bypassing is not a suitable mechanism forsuch type of failures. Frequent bypassing for the same routeleads to unnecessary increase in the route length, which inturn increases the packet delivery delay and produces furtherfailures. Thus, it is better to start a new route-request-discoveryprocess instead of repairing the broken route. From table II,in MAN model only 72% of received packets have shorthop count while this percent is about 84% in case of FRW.These percentages clarify why Bypass-AODV shows betterperformance under FRW model than MAN model. Fig. 6shows that Bypass-AODV is not a suitable choice for VANETapplications especially at high speeds.

VI. CONCLUSION

Accurate evaluation of mobility impact on the routing pro-tocols requires testing different mobility patterns. Otherwise,the observations made and the conclusions drawn from thesimulation studies may be misleading.

Fig. 6. Goodput, Bypass-AODV and AODV routing protocols

Simulation results show that Bypass-AODV is insensitiveto random mobility model used and has a clear performanceimprovement compared to AODV. It has a comparable perfor-mance under group mobility model compared to AODV. Cur-rently, Bypass-AODV is not suitable for VANET applications.As a future work, Bypass-AODV needs more improvementin order to handle VANET applications. We believe that thatseveral parameters such as vehicle speed and direction arenecessary for appropriate route selection.

REFERENCES

[1] K. Fall and K. Varadham, NS Manual. Available:http://www.isi.edu/nsnam/ns/ns-documentation.html/

[2] Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications, IEEE standard 802.11, 1997.

[3] C. E. Perkins and E. M. Royer. ”The ad hoc on-demand distance vectorprotocol,” in Ad Hoc Networking, C. E. Perkins, ed. Addison-Wesley,2001, pp. 173-219.

[4] D. B. Johnson, D. A. Maltz, and J. Broch, ”DSR: the dynamic sourcerouting protocol for multi-hop wireless ad hoc networks,” in Ad HocNetworking, C. E. Perkins, ed. Addison-Wesley, 2001, pp. 139-172.

[5] F. Bai, N. Sadagopan and A. Helmy, ”Important, a framework to system-atically analyze the impact of mobility on performance of routing protocolfor ad hoc networks,” in Proc. Of IEEE Information CommunicationsConference (INFOCOM 2003), San Francisco, Apr. 2003.

[6] T. Camp, J. Boleng, and V. Davies, ”A Survey of mobility modelsfor ad hoc network research,” WCMC: Special Issue on Mobile Ad HocNetworking, Vol. 2, No. 5, pp. 483-502, 2002.

[7] T.K. Madsen, F. Fitzek, and R. Prasad, ”Impact of different mobilitymodels on connectivity probability of a wireless ad hoc network,” inProceedings of International Workshop on Wireless Ad Hoc Networks.June 2004.

[8] A. Jardosh , E. M. Belding-Royer , K. C. Almeroth and S. Suri, ”Towardsrealistic mobility models for mobile ad hoc networks,” Proceedingsof the 9th annual international conference on Mobile computing andnetworking, September 14-19, 2003, San Diego, CA, USA.

[9] A. Alshanyour and U. Baroudi, ”Bypass-AODV: improving performanceof ad hoc on-demand distance vector (AODV) routing protocol in wirelessad hoc networks,” International Conference on Ambient Media andSystems (Ambi-sys 2008), Quebec-Canada, February 2008.

[10] C. Bettstetter and C. Wagner, ”The spatial node distribution of therandom waypoint mobility model,” emphIn Proceedings of the 1st Ger-man Workshop on Mobile Ad hoc Networks (WMAN), pp. 41-58, Ulm,Germany, March 2002.

[11] X. Hong, M. Gerla, G. Pei, and C.-C. Chiang, ”A group mobilitymodel for ad hoc wireless networks,” in Proceedings of the ACM/IEEEMSWIM’99, Seattle, WA, August 1999.

[12] D. M. Blough, G. Resta and P. Santi, ”A statistical analysis of the long-run node spatial distribution in mobile ad hoc networks,” in Proceedingof ACM International Workshop on Modeling, Analysis and Simulationof Wireless and Mobile Systems(MSWiM), Atlanta, GA, Sep. 2002.

[13] E. M. Royer, P. M. Melliar-Smith, and L. E. Moser, ”An analysis of theoptimum node density for ad hoc mobile networks,” in Proceedings ofthe IEEE International Conference on Communications (ICC), Helsinki,Finland, June 2001.