Would Current Ad-Hoc Routing Protocols be Adequate for the...

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2018.2812727, IEEE Internet ofThings Journal

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Would Current Ad-Hoc Routing Protocols beAdequate for the Internet of Vehicles?

A Comparative StudyArmir Bujari, Member, IEEE, Ombretta Gaggi, Claudio Enrico Palazzi, Member, IEEE, and Daniele Ronzani

Abstract—In recent years we have seen a great proliferation ofsmart vehicles, ranging from cars to little drones (both terrestrialand aerial), all endowed with sensors and communication capabil-ities. It is hence easy to foresee a future with even more smart andconnected vehicles moving around, occupying space and creatingan Internet of Vehicles (IoV). In this IoV, a multitude of nodes(both static and mobile) will generate a continuous multihopflow of local information to support local smart environmentapplications. Therefore, one interesting environment for the IoVwould be in the form of 3D Mobile Ad-Hoc Networks (MANETs).Unfortunately, MANET routing protocols have generally beendesigned and analyzed keeping in mind a 2D scenario; there isno guarantee on how they would support a 3D topology of theIoV. To this end, we have considered routing protocols deemedas the state-of-the-art for classic MANETs and tested them over3D topologies to evaluate their assets and technical challenges.

Index Terms—IoV, routing, topology-based, position-based,performance

I. INTRODUCTION

Mobile Ad-hoc Networks (MANETs) have been a chal-lenging research topic for a while now, thanks to their ver-satility, which has been demonstrated to be useful in numer-ous scenarios (e.g., emergency deployments and communitynetworking). Yet, they have now gained new interest dueto recent technological revolutions palpable in our everydaylife. Consider, for instance, smartphones, cars and drones(both terrestrial and aerial): they have all become smart,with computational, sensing and communication capabilities.These devices can hence now be interconnected, creating realMANETs supporting new and innovative service provisioningschemes [1], [2], [3].

In fact, the popularity of mobile phones have created thepotential for actual MANETs, whereas thanks to the IEEE802.11p a lot of research has now been devoted to VehicularAd-hoc Networks (VANETs) [4], [5]. Furthermore, groupsof micro aerial vehicles and Unmanned Airborn Vehicles(UAVs) have been considered as possible Flying Ad-hocNetworks (FANETs) [6]. In this context, Drone Ad-hoc Net-works (DANETs) are considered as the next logical evolution,comprising ad-hoc networks composed of both micro aerialand terrestrial vehicles.

In this paper, we intend to focus on ad-hoc networkscomposed of any sort of vehicle (e.g., cars, drones,...) that

The authors are with the Department of Mathematics, Universityof Padua, Via Trieste 63, Padua, Italy (email: [email protected];[email protected], [email protected]; [email protected])

could be present in our towns. These networks could be usedto gather, elaborate and disseminate a lot of information havinga local scope: traffic condition, local message exchange, pollu-tion sensing, coordinated movements, warnings, etc. In otherwords, they would form an Internet of Vehicles (IoV), withmultihop communications flowing amongst its nodes. Evenin case some information has to go through the Internet, wecan still consider the possibility of having some node actingas Internet gateway and other nodes in our ad-hoc networkresorting to multihop connectivity to communicate with theInternet gateway [7], [8]. Note that we consider the case inwhich drones may become more and more popular and ableto perform smart autonomous purposes; i.e., we assume thatdrones will have an increment of popularity and functionssimilar to what happened with cellular phones (beside beinginteresting, this currently seems to be a plausible trend [9]).

It is hence easy to see how multihop, ad-hoc routingrepresents a fundamental task in the envisioned IoV. Onemay say that finally we will have the chance to apply allthose decades of research in MANET routing. Unfortunately,the depicted IoV scenario has peculiar features that makeit different from a general MANET. First of all, since theassumed presence of numerous flying objects, we have toconsider a 3D topology. Then, we have to assume that mobilityconditions may change greatly from case to case; we may havescenarios where all nodes are moving, as well as scenarioswhere just a small percentage of nodes are moving whereasthe majority is static or nomadically moving by now and then(and hence can be assumed as static during the duration ofa data flow). Furthermore, the scenario where most nodes arestatic also includes those cases where the IoV is interconnectedwith static sensors with communication capabilities (e.g., somesensors on top of roadside lamps or buildings’ roofs), thusmaking the considered scenarios even more representative.

As MANET routing protocols have generally been designedand analyzed keeping in mind a 2D scenario, there is noguarantee on how they would support the 3D topology ofthe envisioned IoV. To this end, we have considered routingprotocols deemed as the state-of-the-art for classic MANETsand tested them over 3D topologies to evaluate current assetsand technical challenges. In particular, we have considered twomain classes of protocols: topology-based and position-based.For each of these classes, we have taken main representativesand analyzed them in the 3D scenario in order to highlightmain pros and cons in using them. Given the absence ofa reference mobility model for DANETs, we simulate this



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network by employing a synthetic mobility model whileadopting realistic, well-established mobility models for othercomponents of the network. While there have been efforts indevising mission or application-specific mobility models fordrones, our study is not tailored to a specific use-case andgoes beyond the specific application scenario. The goal is toasses the feasibility of current routing proposals for a generalIoV.

The rest of this paper is organized as follow. In Section 2we discuss general background information related to theIoV scenario, whereas Section 3 overviews the state-of-the-art of routing protocols in the context. Section 4 presentsthe first experimental results, discussing the tradeoffs thatemerge. Following the same objective, in Section 5, we assesa realistic IoV deployment consisting of heterogeneous IoVdevices engaged in communication with each other. Finally,conclusions are drawn in Section 6.

II. BACKGROUND

A. Application scenario

Ad-hoc networking seems a promising paradigm, able tosupport new and innovative applications in scenarios involv-ing vehicles, drones and personal devices such as smart-phones [10], [11], [12]. As anticipated, these networks couldbe used to gather, elaborate and disseminate informationin an IoV through multihop connectivity. Communicationamong vehicles has been categorized in recent years intovarious declinations, e.g., Vehicle-to-Vehicle (V2V), Vehicle-to-Road (V2R), Vehicle-to-Human (V2H), Vehicle-to-Sensorand Vehicle-to-Drone (V2D); it is hence clear how an IoVcould be composed by much more than just cars. On thecontrary, we can expect an IoV to be a 3D topology ad-hocnetwork involving terrestrial and flying vehicles as well assensors, computers, smartphones, Internet gateways and anyother device with communication capabilities present in theenvironment.

There are many applications that could exploit ad-hoc com-munication in the context of IoV, ranging from environmentalmonitoring to safety and entertainment [13]. For instance,one might envisage VANETs being employed to disseminateinformation regarding vehicular movements, general trafficconditions, or even advertisement toward the Internet (e.g.,to web services such as Google Maps) and viceversa [7].

A timely and very challenging application regards thedistributed control over wireless links in order to enable au-tonomous driving. Autonomous vehicles would in fact need toget as much information as possible from the IoV to determinetheir best course of action to ensure efficiency, reliability,and safety [14], [15]. This means obtaining information, eventhrough multihop, from surrounding vehicles, from camerasplaced on top of lamps and buildings, from sensors around,from hovering UAVs, etc. Automated driving or flying, andthe need for IoV-based, distributed control could be used evenin case of rescue operation based on autonomous terrestrialand aerial vehicles [16].

Indeed, flying drones are becoming frequently seen vehicleswith communication and sensing capabilities. We can expect

them to evolve in terms of functionalities and reach similarpopularity peaks as smartphones. It is even possible that,in future, each person will have a personal drone helpingher/him with creating material to populate a social account(e.g. automatically created logs composed by pictures, videos,etc. [17]. Drones may include any unmanned aircraft orself-driving vehicles, ranging in size from a palm-sized toseveral meters; they may also carry small amounts of cargo.Another possible application is the traffic monitoring, safetyand law enforcement over the streets, in which drones cancommunicate amongst themselves, or to a specific car, or to agroup of vehicles.

IoV communication could be exploited even for local mes-sage exchange amongst passengers of cars in a certain area andpeople nearby. They might share text, voice, images, videos,online gaming, music, news and advertisement, even resortingto data generated elsewhere (e.g., a drone in the sky abovethem). Regarding local news and advertisement dissemination,data floating solutions could be adopted by having an IoVsupporting them [18].

B. Routing in 3D MANETs

The highly dynamic and heterogeneous nature of 3DMANETs clearly raises questions on the suitability of currentrouting protocols. Route discovery and maintenance in ad-hocnetworks is related to the topology changes; thus, the systemperformance depends on how reactive is the routing protocolto link changes.

The simplest approach to data delivery would be flooding. Inessence, every node transmits each data packet to every otherneighboring node. Nevertheless, this type of data propagationdoes not scale with the network size or density, because ofredundant transmissions.

Classic topology-based ad-hoc routing protocols, such asAODV or DSDV, can be used for this type of networks, al-though they are not appropriate for highly dynamic scenarios.Another class of protocols are the position-based (geo-routing)approaches, which exploit node locations to determine thenext hop. Typically, nodes resorting to geo-routing exploit alocation service, such as the Global Positioning System (GPS).Position-based approaches were introduced to eliminate someinherent limitations of topology-based protocols, such as theroute maintenance. Nodes in this context exploit local informa-tion mirrored in a neighboring table, containing geographicalpositions of nodes. In general, neighbor discovery relies ona beaconing service whereby nodes periodically broadcastpositional information. Clearly, the beaconing period is animportant factor that shapes the performance of a position-based protocol [19].

A well known technique exploiting location information toroute data packets is the Greedy approach [20]. In this ap-proach, the neighbor node closest to the destination is the oneeligible to further advance their data packet into the network.So far, many geographic routing protocols have been proposedand they can be categorized in three classes: progress-based,randomized-based and face-based. In progress-based routingprotocols, the current node holding the packet forwards it to



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Fig. 1. Illustration of several next-hop nodes chosen by nc using the progress-based forwarding strategies. With Greedy, nc chooses n5 as the next-hopnode.

the node making the most progress towards a destination. Anillustration of the progress-based strategy is shown in Figure 1.The randomized-based strategy is similar to progress-basedmethod but in this case the next node is chosen randomlyor according to a probability distribution, from the set ofcandidate nodes. The face-based strategy uses an algorithm,called Face [21], that advances the packet between the facesby considering the right-hand rule, always guaranteeing thepacket delivery to the destination in the context of planar(2D) networks. Position information is used to extract a planarsubgraph containing the faces whose vertices are the nodes.

More recently, there have been a number of practicaldeployments of three-dimensional networks (sensor networks,drone networks, vehicle networks). Many actual real applica-tions require a three-dimensional node arrangement. To thebest of our knowledge, a lot of research has been devoted todevise efficient geographic routing protocols in 2D networksand many 3D geographic routing protocols proposed aremainly studied in a theoretical interest. Indeed, they havebeen designed and analyzed in ideal topologies, abstractingfrom the intricacies of the wireless medium (like unit ballgraphs [22]). Geographic routing in 3D space is intrinsicallyharder than routing in 2D topologies. For example, a greedyforwarding approach tends to reach more local minima in ageneral 3D topology, than in a 2D counterpart. Moreover,many of the geographic protocols are not extensible to the thirddimension and often the extension of 2D routing protocolsinto 3D ones is not trivial, since some assumptions made inthe 2D context break down (e.g., the ability to extract planarsubgraphs). Durocher et al. [22] show the impossibility ofrouting protocols to guarantee delivery in 3D ad-hoc networks,when nodes are constrained to rely only on information relatedto their k-hop neighborhood (with k strictly lower than thenetwork diameter). This is in contrast to the results from 2Denvironments, where a protocol relying on local informatione.g., face routing, does guarantee delivery. This leads theproblem of finding other solutions able to guarantee thedelivery of packets, with the least use of resources.

III. DESCRIPTION OF ROUTING PROTOCOLS

Our goal is to asses the feasibility of current state-of-the-art routing protocols for the IoV environment. To this end,

we chose some representative approaches for each consideredclasses. In the following, we provide a concise overview foreach of them.

A. Topology-based protocols

Destination-Sequenced Distance-Vector (DSDV [23]) isbased on the Bellman Ford algorithm. DSDV is a proactiveprotocol that is enhanced by the use of sequence numbers inthe routing tables to avoid the loop problem. In this way, themost recently updated paths have a higher sequence number.Each node updates its sequence number every time that itsends an update and maintains a routing table with an entry foreach other network node. Each entry holds a sequence number,which is updated with each change, used to avoid cycles anddiscriminate between old routes and new ones. Updates aretransmitted by nodes periodically or as soon as major changestake place. When a node receives two different paths to thesame destination, it chooses the one with the greater sequencenumber, or the one with less hops in case of equal sequencenumber. To reduce the overhead of network traffic, this routingprotocol uses two types of update packets:

• Full dump: all complete routing information are sent.• Incremental dump: only updates are sent.

Ad-Hoc On Demand Distance Vector (AODV [24]) is areactive protocol whereby routes are established on-demand,as they are needed. In AODV, the network is silent until aconnection is needed. When a node needs to find the pathtoward a certain destination, a Route Request packet (RREQ)is sent in broadcast over the network. Other nodes that receivethis RREQ packet forward it and record the node from whichthey have received it by creating or updating the temporaryroute to reach the source node in their routing table. Whena node possessing the information about the route to thedestination receives a RREQ, it answers by sending a RREPpacket through a temporary route to the requesting node. Therequesting node then begins to use the route that has the leastnumber of hops through other nodes. When a link fails, arouting error is passed back to a transmitting node sending aRoute Error packet (RERR), and the process repeats. Nodesuse a sequence number so that they do not repeat route requeststhat they have already forwarded. The advantage of AODV isthat it does not create extra traffic in maintaining the routingtables if cases they are not used. On the other hand, it requiresmore time to establish a route when compared to DSDV.

Dynamic Source Routing (DSR [25]), like in AODV, thesource node initiates a route discovery process generating aRREQ packet which is flooded into the network. The RREQpacket contains a list of hops which are incrementally addedinto the route request packet header as it is propagated throughthe network. Once the RREQ reaches the destination or anode that has a path toward the destination, a RREP is sentback along the reverse path collected in the RREQ. The maindifference between DSR and AODV is in the way the routeinformation is kept: in DSR it is stored at the source and inthe header of the transmitted control packet, while in AODVit is stored at the intermediate nodes.



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B. Position-based protocols

Position-based routing protocols (geo-routing) exploit nodescoordinates to route packets toward a destination. Severalgeographic routing protocols have been proposed; they can becategorized among three main classes: progress, randomizedand face-based. Clearly, we also have hybrid approaches thatcombine the strengths of the various strategies. In this paper,we hence consider and analyze three representative hybridprotocols considered to be as the state-of-the-art amongstposition-based routing protocols for 3D ad-hoc networks.

Greedy-Face-Greedy (GFG [21]), also referred to as GreedyPerimeter Stateless Routing algorithm (GPSR) [26] for 2Dnetworks, uses a combination of greedy and face methods.With GFG, a flag is stored in each data packet. This flagcan be set into greedy and face-mode, indicating whether thepacket is forwarded with a greedy approach (using the Greedyforwarding algorithm) or a face approach, respectively.

The greedy approach uses a deterministic method to deliverthe packet. Typically, the Greedy algorithm [20] is used: anode that receives a packet searches among its neighbors thenode that is closest to the destination. If this node exists, thepacket is transmitted to it, otherwise the packet is dropped andthe current node is called a local minimum.

The face approach adopts the Face algorithm, which makesuses graph planarization to forward the packet. The Facealgorithm is a prominent solution proposed to address the localminima problem which a greedy approach is subject to. In a2D network, Face obtains a guaranteed packet delivery, butin 3D scenarios its benefits are inhibited since the conceptof 3D graph planarization is not so straightforward. However,some works [27], [28], [29] have proposed techniques wherebynodes are projected over a 2D plane so that the Face algorithmcould still be used, although it is not clear with whichlimitations. As the face algorithm representative, we havechosen the method described in [29] which is considered asthe state-of-the-art for this class of algorithms, achieving thebest performance in terms of packet delivery.

In details, GFG starts from the source node with the Greedyalgorithm. When along the route the packet gets stuck into alocal minimum, the packet is marked to switch from greedy toface-mode and GFG performs the Face forwarding algorithmin the projected planar graph defined in [29]. Moreover, whena packet enters in face-mode at node x , GFG records in thepacket the location of x as the node when greedy forwardingmode failed. This information is then used at next hops todetermine whether and when the packet can be forwardedin a greedy fashion. Upon receiving a face-mode packet,the current node compares the location of x as stored inthe packet with the forwarding nodes location; GFG returnsin greedy-mode if the distance from the forwarding nodeto the destination node is less than the distance x to thedestination node. In this case, the packet is set into greedy-mode and the algorithm continues the greedy progress towardsthe destination. Otherwise, GFG continues with the face-modeforwarding. Figure 2 shows an example of the GFG protocol.

Greedy-Random-Greedy (GRG [30]) is yet another hybridapproach belonging to the progress/randomized-based class.

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Fig. 2. GFG algorithm over a 2D graph; solid arrows represent greedy-modeforwarding, dashed arrows represent face-mode forwarding.

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Fig. 3. In AB3D, plane PL1 passes through ns, nd and n1, plane PL2 isorthogonal to PL1. Both planes contain the line (nsnd).

GRG uses Greedy as the primary scheme and a randomizedalgorithm as a recovery strategy. A randomized approach triesto solve the local minimum problem stated previously byrandomly choosing the next node toward destination from asubset of the current neighbors. Typically, a 2D randomizedalgorithm [31] chooses a neighbor node above the line passingthrough the current node and the destination node, and aneighbor node below the same line. The two nodes are selectedusing a greedy approach. Then, from these two neighbors, thenext node is chosen randomly or according to a distribution ofprobability. A 3D extension of this approach, named AB3D,is proposed in [28], [32] and uses planes passing through thesource and the destination to divide the neighbor selectingregions. In our comparison we have chosen this method asthe randomized approach. Figure 3 shows a typical regionsubdivision of the AB3D protocol.

GRG starts with a greedy approach until it finds a local min-imum. At this point, GRG stores the distance from this localminimum and the destination immediately before switching tothe random phase as a recovery strategy. In this phase, thenode randomly selects one of its neighbors using the stepsdefined in [28]. If the distance between the next node and thedestination is less than the distance between the previous local



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minimum and the destination, then the algorithm resumes thegreedy forwarding, otherwise it continues with the randomphase.

Depth First Search (DFS [33]) is a distributed approxima-tion of the classical depth-first search algorithm from wherethe name derives. The proposal follows a progress-basedforwarding strategy like Greedy and the forwarding strategytakes place as follows: each node has a list in its local memorythat contains the id of received packets. If a packet arrives ina node, its id is stored in this list. If a packet id is not foundin the local memory of a node, this node is marked as white;otherwise, if the packet id is present, the node is marked asgray (which means that it has received packet at least once).The process of visiting nodes coincides with sending packetsbetween nodes. If a node receives a packet for the first time, itmemorizes also the node that forwarded that packet. So, eachnode stores a list of tuples id, from, where id is the packetid and from is the node from which the packet arrived.

The source node s starts DFS coloring itself as gray andstoring the id packet in its list (the from field is empty). EachDFS packet has one bit that indicates whether the message isforwarded or returned. When a node c receives a packet for thefirst time, it adds a tuple (id, from) into its memory and ordersits neighbors according to their distance to the destination d(hence following a greedy method). The only node not to betaken into account in the ordered list is node from that sentthe packet to c. The packet is then forwarded to the first choiceu among the neighbors (the first node chosen is the node thatis closest to the destination). If there is no choice, the packetis returned to from.

If receiving a packet forwarded from any node b, a graynode c will reject it immediately, returning it to b (returnedmessage). A gray node b, upon receiving a returned messagefrom node c, will forward the message to the next choice ein its sorted list of neighbors, if such a neighbor exists. If bhas no more neighbors in its list, the packet will be returnedto the node from, which originally sent the message to b(memorized in the list of packets). An index L is used to knowwhich is the next node to forward the packet in the orderedlist. L is the index, in the list, of the last neighbor u selectedfor packet forwarding. When a new node has to be chosen, Lis increased.

IV. PERFORMANCE EVALUATION

The adopted simulation environment is the well-known andwidely used Network Simulator 2 (NS-2 [34]). While thesimulator is equipped with a native implementation of thetopology-based protocols, we had to implement from scratchthe position-based ones. It is noteworthy to mention, that thetargeted simulation environment does not natively support 3Denvironments. To this end, we had to apply a publicly availablepatch [35]. In the following we provide some details regardingthe simulation parameters and evaluation strategy.

A. Simulation Environment

Our main goal is to show how the considered protocolsbehave in an environment different from the one they were

designed for. Our simulator environment consists of a set ofnodes randomly generated in a cube of side length 1000 unitswith a transmission range of 250 units. We considered fourdifferent cardinalities for the set of nodes in the network: 50,100, 150 and 200 nodes, in order to evaluate the protocolperformance variation. Mobility models proposed in the pasthave been focused, for instance, on VANETs but not on dronenetworks [4]; we have hence considered the recommendedmobility model but we had to adapt to our considered 3Denvironment. In essence, nodes alternate movement and sta-tionary periods. For each cardinality of the set of nodes, fourmobility constraints are chosen, in terms of pause time: 5,20, 40 and 100 s, during which the node remains stationary,before resuming to move toward a new destination in the 3Dspace. The simulation duration is set to 100 s, so that thecase with 100 s of pause time corresponds to a network withstatic nodes. The traffic scenario consists on 10 flows (10different sources and 10 different destinations) of CBR traffic.These and the other mobility parameters are summarized inTable I. For position-based protocols, the proactive beaconingprocess period (i.e, the time between two consecutive beacontransmissions) is set equal to 0.5 s.

The metrics of interest used to asses the protocols are thefollowing:

1) Packet Delivery: it is the average ratio of the data packetdelivered to the destination to those generated by thesource.

2) Path Dilation: it is also called stretch factor and cor-responds to the average ratio of the number of hopstraversed by the packet to reach the destination, to theminimum path length from source to destination.

B. Simulation results and discussion

In this section we show the performance results of routingprotocols in a set of networks of 50, 100, 150, 200 nodes,with pause times 5, 20, 40 and 100 s. Basically, with a 5 s ofpause time the nodes moves frequently, whereas with 20 and40 s of pause time the network has less mobility and with 100s of pause time all the nodes are still during the whole 100 sof simulation.

Pause time of 5 s: topology-based and position-based pro-tocols perform in a heterogeneous way. For instance, inFigure 4 we can notice the different performances for eachof the considered metrics. The delivery rate in low densityscenarios(50 nodes) is quite low for position-based protocols,especially when employing the GFG scheme, while AODVand DSR perform at an acceptable level. When increasingthe number of nodes, the packet delivery rate of the position-based protocols increases as well. The best performance whenconsidering a network density of 200 nodes, is reached bythe DFS scheme (about 95%). In terms of path dilation,position-based protocols achieve the worst performance. Thisis intuitively expected as these schemes rely solely on localknowledge. Instead, all the topology-based protocols performwell, with packets traversing a path of length close to theoptimal length. We can also notice that when increasing thenumber of nodes, the path dilation decreases. This effect



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TABLE ISIMULATION PARAMETERS.

Parameter ValueMAC type IEEE 802.11gSimulation area 1000 m x 1000 m x 1000 mTransmission range 250 mNode max speed 10 m/sTraffic type CBRNumber of data flows 10Data packet size 64 bytesPacket rate 2 pckt/sQueue type Drop TailNumber of nodes 50, 100, 150, 200Pause times (sec) 5, 20, 40, 100 (static)

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Fig. 4. Performance comparison among the protocols in a 3D MANETvarying the number of nodes. The nodes are mobile with pause time 5s.

is explained due to the fact that having a denser networkincreases the chances of finding a straight path toward thedestination. On the contrary, if a node has a low number ofneighbor nodes, it may unfortunately happen that a few out-of-date information from neighbors could lead to a loop amongthe nodes, and hence to higher values of path dilation, untilthe neighbor table settles.

Pause time of 20 s: the results evidenced in Figure 5 showthat there are not many differences when compared to theprior configuration. We see a little growth in the deliveryrate for AODV and DSR and the position-based protocols.

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Also, position-based approaches present a reduction of values,whereas packets in the topology-based schemes on averagetraverse almost the same number of hops as in the caseof 5 s pause time. In general, we can see that AODV andDSR achieve very good performance indexes both in termsof delivery rate and path dilation. In particular, DFS is ableto achieve the best data delivery rate when the number ofnodes is higher or equal to 100. Unfortunately, this comesat the cost of a path dilation that, although lower than otherposition-based schemes, results even three or four times wider



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than with AODV and DSR.Pause time of 40 s: with a pause time of 40 s, we are con-

sidering a network composed by nodes with seldom mobility.DSDV performs well in terms of delivery rate when comparedto prior configurations. Even the position-based approachesshow better delivery rates than the case of 20 s pause time;furthermore, increasing the node density we achieve higherdelivery rates due to the increment of alternative routes thata node can choose. The path dilation values are reduced forposition-based protocols, which take a better decision for thenext node, since the nodes are stationary for longer periods. Inthe case of topology-based protocols the path dilation remainsbetween 1 and 2. In general, even in this case, AODV andDSR seem to be the best protocols to constantly ensure highdelivery rate and low path dilation.

Pause time of 100 s (static network): in a static network,the delivery rate is as expected very high for all the routingprotocols. If a protocol is not able to ensure the delivery ofall packets it is due to the unreliable nature of the wirelesslink. Furthermore, the GFG algorithm still experiences someproblems in delivering the packets, since the planarization ofa 3D graph is not optimal and the algorithm gets stuck in aloop. The lowest performance of position-based protocols areshown in the case of 100 nodes, while with 200 nodes theyare all able to reach more than 95% of delivery rate.

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Fig. 7. Performance comparison among the protocols in a 3D MANETvarying the number of nodes. The nodes are static.

Fig. 8. A multi-tier IoV network comprised of moving cars, drones and trafficintersections equipped with sensing and communication capabilities. In thisenvisaged scenario communication takes place amongst entities in a multi-hop fashion. Cars could exploit the IoVs sensing capabilities, gathering andmerging information from different complementary sources in order to extendthe drivers’ perception beyond direct line of sight.

V. PERFORMANCE OF ROUTING PROTOCOLS IN AREALISTIC URBAN ENVIRONMENT

Figure 8 depicts a potential deployment consisting of aheterogeneous, general purpose IoV network. The area rep-resents a portion of a city with static sensors positioned inlamps and/or at traffic intersections, moving cars and dronesflying above them (all these nodes are also endowed with



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TABLE IISIMULATION PARAMETERS FOR THE ENVISAGED IOV SCENARIO.

Parameter ValueSimulation area 500 m x 500 m x 200 mTransmission range 125 m

vehicle movement characteristicsPositioning randomMobility model Manhattan grid (3 x 3) blocksNumber of nodes 40Speed of nodes [17, 20] m/sPause time 0 s

drone movement characteristicsPositioning random, altitude from 50 to 200 mMobility model Random WaypointNumber of nodes 20Speed of nodes 10 m/sPause time 5 s

static nodesPositioning one at each intersectionNumber of nodes 4

communication capabilities). In specifics, the scenario consistsof a total of 64 nodes arranged in different ways movingaccording to a specific mobility model. In particular, there are40 mobile vehicles, arranged on a 4 x 4 grid of length 500 m,whose streets have a width of 10 m. Each vehicle is initiallypositioned at a random crossroad. Moreover, each vehiclerandomly selects an axis (either x or y) and a crossroads onthat axis, proceeding towards that point. The speed is randomlychosen from 14 m/s to 20 m/s (50 km/h - 72 Km/h). Next, thereare 20 nodes representing flying drones, randomly deployedabove the vehicles’ grid at a random altitude ranging between50 and 200 m. These flying drones follow a Random Waypointmobility model, with a fixed speed of 10 m/s and a pause timeof 5 s, during which the drone is assumed to perform sometask (e.g., taking a picture, sensing environmental conditions,collecting/distributing some data, etc.). As stated earlier, weemploy a synthetic mobility model given the absence of areference mobility model for DANETs. Along with the mobilenodes, at each crossroad there is one static node, representinge.g., access points (on buildings, stations or simple poles). Asummary of the simulation parameters is reported in Table II.

As we can see in Figure 9, DSDV has the lowest per-formance in terms of delivery rate, with an average of 10%of the packets reaching the destination. This is due to nodemobility causing path disruptions with the protocol not beingable to counteract the effects. On the other side, the rest of theprotocols achieve acceptable performance indexes, but noneis capable to guarantee absolutely reliable packet delivery.When analyzing the path dilation in Figure 10, AODV andDSR achieve a good performance, along with DSDV. Instead,in position-based approaches data packets traverse long pathsbefore finally reaching the destination. This could easilylead to the undesirable effect of packets queuing up in thenodes’ buffers, and flows ending up interfering with each

AODVDSR

DSDVG-F

-GG-R

-G DFS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Deliv

ery

Rate

%

Fig. 9. Performance comparison (delivery rate) among the consideredprotocols in the scenario depicted in Figure 8.

AODVDSR

DSDVG-F

-GG-R

-G DFS

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

Path

Dila

tion

Fig. 10. Performance comparison (path dilation) among the consideredprotocols in the scenario depicted in Figure 8.

other. Considering all, there is no clear winner amongst thestudied protocols and no one seems to provide outstandingperformances. We believe this represents a crucial technicalchallenge that needs the researchers’ attention in order toenable IoV.

VI. CONCLUSIONS

In this work, we have explored some basic behaviors oftopology-based and position-based routing protocols on a va-riety of network graphs that represent a possible IoV scenario.The considered topologies are different by number of nodesand by pause times. Using a well-known simulator, we showedthe performance results in terms of delivery rate and pathdilation achieved by state-of-the-art routing protocols for ad-hoc networks.

Our results shed lights on which are the technical challengesopen in routing messages over an IoV. More in detail, we havenoticed that topology-based protocols such as AODV and DSRachieve acceptable performance in terms of both delivery rate



9

and path dilation, whereas position-based protocols achievehigher data rates in high density scenarios but at the cost of alarge path dilation.

In general, these tests assessed the efficacy of state-of-the-art topology-based protocols in supporting general datatransmissions over an IoV, with no one capable of providingany delivery guarantee. We believe that this would be crucialto support applications in IoV scenarios (e.g., to support safetyand distributed control for automated vehicles, or just for en-tertainment applications) and we hence encourage researchersin devising new routing solutions specifically designed for thispurpose.

ACKNOWLEDGEMENTS

This work has been partially funded by the Universita degliStudi di Padova, through the projects PRAT CPDA137314 andPRAT CPDA151221.

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Armir Bujari is an Assistant Professor with the Department of Mathematics,University of Padua. His research interests include protocol design andanalysis for wired/wireless networks.

Ombretta Gaggi is an Assistant Professor at the University of Padua. Herresearch interests include multimedia document and application design,mobile context-aware information systems, web technologies and socialplatforms.

Claudio Enrico Palazzi is an Associate Professor with the Department ofMathematics, University of Padua, Italy. His research is primarily focusedon protocol design and analysis for wired/wireless networks, with emphasison network-centric multimedia entertainment and vehicular networks.

Daniele Ronzani is currently a Ph.D student at the doctoral schoolof Brain Mind and Computer Science at University of Padua, Italy. Hisresearch interests are wireless network protocol design and analysis, withparticular interest on mobile ad-hoc networks.

https://www.sciencedaily.com/releases/2016/11/161117104402.htm

https://www.sciencedaily.com/releases/2016/11/161117104402.htm

http://www.isi.edu/nsnam/ns/

http://www.isi.edu/nsnam/ns/

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