CORP: Cooperative Rateless code Protocol forVehicular Content DisseminationPasquale Cataldi∗, Andrea Tomatis†, Gianluca Grilli‡ and Mario Gerla∗

∗Computer Science DepartmentUniversity of California, Los Angeles - CA (US)

Email: pasquale.cataldi@gmail.com and gerla@cs.ucla.edu†Hitachi Europe

Sophia Antipolis - FranceEmail: andrea.tomatis@hitachi-eu.com

‡Computer Science DepartmentUniversity of Rome “Tor Vergata”, Rome - Italy

Email: gianluca.grilli@uniroma2.it

Abstract—Data dissemination in vehicular networks has beena challenge due to unpredictable network dynamics and chan-nel unreliability. In fact, conventional approaches that rely onTCP or UDP do not perform well and cannot consistentlyguarantee reliable communications. A potential solution to theseproblems involves using erasure-correcting codes to make UDPtransmissions reliable. However, it is also important to notethat, due to vehicular mobility and road obstacles, connectionsamong nodes can be as short as a few seconds. Therefore, ina communication between two nodes, the delay between nodediscovery and data exchange must be minimized. In this paperwe present CORP, a Cooperative Rateless Protocol that exploitsthe reliability of the rateless coding approach while performingfast and efficient dissemination through cooperating nodes withinthe network. The use of rateless codes and unicast connectionsplaces minimal constraints on the delay between node discoveryand data transmission, thus simplifying and rendering feasiblethe content reliable download even in a highly dynamic scenario.Results from simulations reveal that performance improves asmore and more nodes cooperate. In sum, CORP yields importantimprovements in terms of speed of dissemination when comparedto traditional approaches such as TCP.

I. INTRODUCTION

The application that we consider in this paper for CORPis e-commerce and e-advertising. When a car approaches apre-defined area, information about local commercial offers isprovided. Such information may range from common goodsto sophisticated service offerings and may also include eventsand similar entities. Via vehicular communications, vehiclepassengers can request more information and, if electroniccommerce is supported, consume offers immediately. As anexample, a car driver may inquire about the price of aparticular good at various local businesses and selects the bestprice/value offering. Alternatively, a local business can adver-tise to car drivers in a certain area a new offer (e.g., restaurantmenu, etc.). The protocol that we present implements thedissemination of the information between local businesses andvehicles.

Current data dissemination protocols for vehicular networksstill lack the reliability and the efficiency required by most

applications, ranging from safe navigation to content delivery.One of the main problems is the unreliable connectivity.Existing dissemination methods (e.g., BitTorrent) use TCP fordata transmission among nodes. Unfortunately, due to the highvehicular mobility, TCP does not perform well and cannot con-sistently guarantee efficient communications. Frequent packetloss affects window size and transmission efficiency.

The use of UDP for data transfer may not be suitable fordissemination in vehicular networks. In particular, the lackof acknowledgements from the receiver may trigger repeatedtransmissions regardless of the status of the transfer. Thisproblem can be solved by allowing the receiver to send apacket to the sender to stop the data transmission when ithas received the information. As is well known, UDP cannotguarantee the reliability of the data transfer. In fact, in theevent that even a single packet is lost, the information willnot be received correctly. The classic solution involves usingerasure-correcting codes. By using these codes, a subset ofencoded packets will allow to recover the original content.Among all the erasure-correction techniques presented inliterature, rateless codes are the most interesting ones. In fact,they provide low encoding and decoding complexity and areespecially suitable in networks where the channel conditionsare unpredictable and unknown. These are often referred toas “channel oblivious” transmissions, because the number ofencoded packets that can be generated is potentially infinite.

The use of erasure-correcting codes for the disseminationof data not only improves the reliability of the transmissionbut also helps to address the problem known as couponproblem [1]. This problem is typical of P2P networks wherethe amount of time needed to obtain the last missing piece ofthe data can be very long. By generating encoded informationand disseminating it across the network, it is possible toincrease the information availability. Thanks to their uniquecharacteristics, rateless codes have been widely used for datadissemination over the Internet. Yet, another problem thatP2P systems face is how to efficiently exchange data amongpeers. At the beginning of the communication, peers have to

The 8th IFIP Annual Mediterranean Ad Hoc Networking Workshop 2009

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exchange information about the packets they want to receivein order to efficiently use the available bandwidth by avoidingduplicates. In this paper we will call this phase the contentreconciliation phase, that is heavier, the larger the informationis. Speed is a key factor in the design of a communicationsystem in vehicular networks and is therefore critical thatnodes exchange information about their contents as fast aspossible. This means that the efficient techniques have tobe developed in order to minimize the content reconciliationphase.

In this paper we aim to address the following issues:• Reliable and fast data dissemination in vehicular net-

works.• Maximization of the content availability on the network.• Minimization of the content reconciliation phase cost.

In addition to our primary goals, we also seek to design aprotocol that does not demand high computing resources, thusextending our solution to portable devices.

The solution of these problems is CORP (Cooperative Rate-less Protocol), a new rateless protocol for data disseminationdesigned for vehicular networks. In this paper, we present apreliminary analysis of the performance of CORP. We focuson three metrics: (a) delivery ratio after 15 minutes as afunction of the nodes that collaborate in the dissemination;(b) delivery ratio as function of time; (c) average numberand size of the sets of encoded packets that are exchangedamong nodes. For the analysis we adopted a random-waypoint mobility scenario (RWP) [2]. Although this model isonly an approximation of vehicles’ behavior, it is still usefulto understand the asymptotical performance of the protocol.

Later in this paper we will compare CORP to other twoapproaches. The first approach, Basic Rateless Protocol (BRP)considers a rateless dissemination from a single access pointto all the interested vehicles in its range, so that the nodes canrecover the information if they have collected enough encodedpackets. Since there is no cooperation among vehicles, thecommunication will be of the type infrastructure-to-vehicles(I2V). In the second one, mobile nodes can disseminate thesource information as after having decoded it. Therefore, wewill call this approach DDRP (i.e., decode and disseminaterateless protocol). Note that this second approach is similar tothe one presented in [3].

The present paper is organized in the following structure.Section II presents the idea that underlies CORP. Section IIIexplains the protocol in detail. Simulation analysis is per-formed in Section IV. Section V presents the most interestingsolutions in literature for data dissemination using codingtechniques. Finally, Sect. VI provides future directions.

II. PROPOSED APPROACH PRINCIPLES

The following Section presents the main ideas of the pro-posed protocol. The considered scenario is based on accesspoints (APs) and mobile nodes (MNs). APs collect informationfrom external sources and deliver it (or fractions of it) tointerested MNs that will disseminate it to other vehicles. Inother words, interested vehicles cooperate with each other to

disseminate information. APs can be fixed or mobile nodes, ora mix of those. However, for simplicity, this paper considersonly fixed APs.

The dissemination approach can be divided in two differentphases. The first phase is the dissemination between AP andMN. This phase starts when a MN communicates its interestin a certain information that an AP is advertising. The secondphase pertains to the dissemination among MNs, in whichnodes cooperate to disseminate the received data.

The dissemination of the information is performed byexploiting the characteristics of rateless codes. These codeshave been presented by Luby [4] and by Maymounkov [5]and implement the digital fountain idea. From a block of Ksource information symbols, the rateless encoder can producea potentially infinite amount of encoded symbols (ESs). Thegeneration of the ESs is a based on random ex-oring of sourcesymbols that depends on a random sequence generator that isassumed to be known to both encoder and decoder. The ESsgenerated from a random sequence (that is created from aparticular generation seed) form a set of ESs Λ. Given therandom nature of the encoding process, the probability ofgenerating two identical ESs is negligible for sufficiently largeinformation blocks. This means that two different sets (i.e.,created from two different seeds) contain different ESs withhigh probability, i.e., the sets are disjoined. As an example,we can consider a file of 512KB composed of 8000 sourcesymbols of 64B. The block is encoded with different genera-tion seeds, Sa, Sb, Sc, etc., thus generating two disjoined setsof ESs Λa, Λb, Λc, etc.

Given that enough symbols have been received, the rate-less decoder is able to recover the source block with highprobability starting from any subset of distinct received ESs.In particular, the received ESs can belong to different sets ofESs without any loss of performance. The number of symbolsN that have to be received is slightly greater than K. Thismeans that the decoding process introduces an inefficiencyε = N/K − 1. Usually, this inefficiency is very small and itcan be demonstrated that it tends to be zero when the numberof source symbols tends to infinite.

The proposed approach is based on the dissemination ofsets of ESs, rather than on single packets. This means that fora given block, nodes try to exchange all the ESs belongingto a set that is not in common (e.g., Λa), before exchangingESs from another set (e.g., Λb). This method is advantageousin that ESs are transferred in groups, thus reducing in thisway the content reconciliation complexity. In fact, since setsare univocally identified by the generation seeds, the contentreconciliation algorithm is reduced to the research of the seedsthat are not in common between two communicating nodes.For example, let us suppose that a node has received the ESsbelonging to the sets Λa and Λb. Instead of understandingwhich packets can be received, the node can just require ESsbelonging to sets different than the ones associated to Sa andSb.

The dissemination process has to follow four basic rulesin order to be efficient and fast. The first rule is that a

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node that owns the whole information disseminates it bygenerating new disjoined sets of ESs and delivering themwith unicast connections. This allows APs to increase theinformation availability by generating unique sets of ESs(i.e., generated from a new generation seed) per each newconnection. By providing always disjoined sets of ESs, thecontent reconciliation phase is simplified. In our approach,the use of unicast connections is more efficient than usingbroadcasting. In fact, despite its simplicity, broadcasting wouldnot increase the information availability, and an expensivecontent reconciliation phase would be necessary.

The second rule we introduce is that a node cannot dis-seminate ESs belonging to the set it is receiving. This ruleavoids the drawbacks of broadcast transmissions, restrainingthe uncontrolled spread of ESs that would yield to a longcontent reconciliation phase. For example, let assume that nodeA is receiving Λa from the AP and that the rule is not used.At a certain moment of the transfer, node B arrives close tonode A and receives some of the ESs belonging to the Λa

before leaving. In this way, node B will have only a subset ofΛa. If in the future node B want to receive the missing ESsbelonging to that set, then it will have to perform the contentreconciliation phase on the ESs.

A particular situation in the dissemination scenario is whena node can receive ESs belonging to a certain set from twoor more neighbors. The scenario requires a rule to define howthe downloading activity is performed. The third rule is thata node cannot download information of a certain set of ESsby multiple nodes at the same time. According to this rule, anode can receive multiple sets at the same time only if theseare disjoined. In this way, there is no need for coordinationamong the sending nodes. This is useful because it avoids thecase in which nodes that are not within each other’s radiocoverage serve the same requesting node with the same set.

Although we stated that only one node can service a certainset, we still have to establish which of them will do it. Infact, because of poor channel conditions, nodes might not havereceived the same number of ESs belonging to that set. Thefourth rule that we introduce is called certification strategy.According to this strategy, a node can disseminate a set onlyif it owns all the ESs of that set that are present on thenetwork. Thus, a received set of ESs is said to be certifiedif the receiving node has indeed received all the ESs that arepresent on the network. For simplicity, in this paper we willalso say that that a node is certified for a set if it owns all theESs generated from a certain generation seed that are presentin the network.

By using the certification strategy, we further simplify thecontent reconciliation strategy. In fact, the content reconcil-iation is performed only on certified sets. If the conditionsof the channel are optimal, i.e., no packets are lost, then allsets will be certified. This would yield an optimal dissemi-nation of the sets. However, when the channel conditions arepoor, the certification strategy limits the transmission of setsonly to nodes with enough information to deliver. Therefore,the strategy might yield a sub-optimal dissemination of the

information. However, the content reconciliation time will beminimal, so that most of the connection time (that, in this case,would be very short) can be dedicated to data transmission.Moreover, even if a node cannot disseminate a set that is notcertified, the ESs of that set are still useful for the decodingof the information block.

III. CORP

In this following we briefly introduce the prototype packetsat the application layer that CORP uses for communication.Then, we present the two phases of the communication pro-tocol in detail.

Note that CORP differs from DDRP in the disseminationof the information. In DDRP, the only blocks of the files thatcan be disseminated are the decoded ones. This means that,in order to maximize the collaboration among nodes, eachnode tries to download all the needed information regardingto one block. In fact, this is the only method that a node canuse in order to increase the information availability on thatblock. However, by using CORP, nodes can download certifiedsets even not belonging to the same block, because they canalready contribute to the dissemination process. In addition,when the nodes decode a block, they can to further increasethe information availability similarly to DDRP.

A. Packet Definition

The CORP application packet is made up of an header anda payload. The header contains a field, Type, that indicates thepayload type. The payload contains the data corresponding tothe type indicated in the header and, optionally, a CRC. Inparticular, CORP uses eight different types of packets.

• Beacon. This packet is broadcasted by AP nodes topublish the list of the available information files. Eachfile is identified by a unique tag.

• Request. This packet is broadcasted to request a specificfile.

• Offer. This packet is sent either by AP or MN nodesas a response to a request for a file. In this packet, theseeds S of the certified sets Λ and the parameters neededfor the decoding process are specified. For example, itis indicated that the block is composed of 8000 sourcesymbols of 64B and that the adopted code is a LT codewith given parameters δ and c. The packet has also a fieldthat indicates if the offering node owns the whole file.

• ACK. This packet is sent by the requesting node tocommunicate the set of ESs it wants to download byindicating the generating seed set. In addition, the re-questing node can also specify the Starting SequenceNumber (SSN), which is the first ES of the set that itwants to receive.

• Data. This packet contains the encoded data and aSequence Number (SN). The encoded data is a groupof one or more ESs belonging to the same. SN indicatesthe number of the packet in the generated set of ESs.

• Stop. This packet is sent by the requesting node toterminate the download of a certain set.

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• EoS. This packet is sent by the offering node to com-municate the end of the transmission of the set of ESs itis providing to a requesting node. The packet includesalso a digest computed on the certified set, useful tothe receiving node to check if the set can be tagged ascertified.

B. Communication Protocol

The communication protocol is made up of two phases: thecommunication between AP and MN (Phase 1) and betweentwo MNs (Phase 2). The information file is composed by thedata information and a digest computed on the data file. Thewhole information file is then disseminated according to theCORP protocol. For simplicity, in the following we presentthe case where a file is composed of one block.

1) Phase 1: AP to MN: The AP periodically advertises thelist of the available information files by broadcasting Beaconpackets. If a MN within the radio coverage area of AP isinterested in receiving one of the files, it broadcasts a Requestpacket specifying the associate tag. In particular, starting fromthis moment, the MN periodically sends a broadcast Requestpacket for the same file until it will complete the download.

When the AP receives the Request, it chooses a uniquegeneration seed for the generation of the set of ESs tobe transmitted. In addition, the packet also contains all theparameters that the rateless decoder needs to recover theinformation. After receiving the packet offer, the MN sendsan ACK packet to confirm the settings of the AP for thetransmission. Then, the AP can begin the data transmissionand will continue until one of the following stop conditionsoccur:

• The MN is out of the radio coverage range.• The MN generates a Stop packet.

If the MN realises that it is out of the AP’s radio coverage, itconsiders the transmission finished. Then, if it has not receivedyet enough packets to decode, it computes a digest on thereceived ESs set and considers the set certified, because it hasall the ESs present in the network. Starting from this moment,the MN can disseminate the received set of ESs.

If the same MN associates again to the AP and requeststhe same file, a new session will be opened and a newgeneration seed will be created. In this way, the AP increasesthe availability of the information on network and there is noneed for a content reconciliation phase.

If the MN has received enough packets to decode, then itcomputes the digest of the recovered information and checksif it corresponds to the one present in the recovered file. Ifso, then the node has successfully received the source and,if still connected to the AP, then a Stop packet is generated.Moreover, the node will be able to provide the received fileto other MNs generating new disjoined sets of ESs. However,if the digests o not match, then the data file is corrupted andwill be completely discarded.

2) Phase 2: MN to MN: In the dissemination between twoMNs, there are two possibilities: The first one is when bothnodes do not have enough ESs in order to decode the original

information, the second one is when one of the nodes has thewhole file.

In the first case, once a MN receives a broadcast Requestpacket from another MN, it responds with a Offer packetcontaining the list of the generation seeds of the certifiedsets belonging to the requested file and specifying that it doesnot have the whole file. The requesting node will choose oneof the seeds and the sending node will start to transmit theESs belonging to the set until one of the conditions presentedin Sect. III-B1 occur or if the sending node has sent all theESs belonging to that set. In the latter case, the sending nodetransmits a EoS packet containing the digest of the providedset. At this point, the receiver, computes the digest of the setbased on the ESs that it has received. If the computed digestmatches the one contained in the EoS packet, then the setis certified and can be disseminated. However, if there is nomatch between the two digests, then the node will not be ableto disseminate the set but the ESs will be equally useful forthe decoding of the information block.

In the case where one node owns the whole information, thisnode answer to a Request by providing a unique generationseed and specifying that it owns the whole information. Fromthis point the communication algorithm works exactly asspecified in Sect. III-B1.

IV. SIMULATIONS AND RESULTS

In order to evaluate the performance of CORP, we modelthe system as a wireless ad hoc network with several MNsand a fixed AP. We consider the nodes to be equippedwith omnidirectional radio transmitters with a nominal 216meters radio-range and with 2Mbps maximum bandwidth. Thetransmission is UDP based and the performance of the systemis simulated with QualNet. We assume that MNs can movefreely (with speed ranging from 10 to 20 m/s and maximumpause-time of 15 seconds) within a 3000x3000m wide areaaccording to the RWP model. Instead, the AP is fixed andlocated in the center of the map, in order to maximize thenumber of times a MN can communicate with it.

The application that we consider for simulation is theadvertisement by a restaurant of a special menu for lunch.The menu is contained in a 512KB image file that will bedelivered to all the vehicles in the area that are interested inthis service. We consider simulations of 15 minutes with anumber of interested vehicles ranging from 2 to 100 and thenwe compare CORP to other two approaches. The first approachis a basic dissemination of the information from the AP usingrateless codes. This approach is not collaborative and vehiclescan recover the information as soon as they have receivedenough ESs. We will call this approach BRP. The secondapproach is DDRP, where nodes can exchange informationonly if they have decoded the file.

Among all the rateless codes implementations presentedin literature, for these simulations we chose LT codes [4]because they are the first to implement the digital fountainidea. In detail, the parameters we used are (K, δ, c, l) =(8000, 0.5, 0.04, 64), where K indicates the number of source

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Parameter ValueSimulation Time 900 sNumber of MNs 2 to 100MN Mobility Model RWPMN Pause Time 15 [s]MN Speed Range 10-20 [m/s]Information Blocks 1Source Information K 8000 packetsData Packet Payload Size 64 BInitial Distribution MNs on map UniformLT parameters: c, δ 0.04, 0.5LT ES Size 64 B

TABLE I: Simulation Parameters.

packets of l bytes that form the information, while δ and care the parameters of the Robust Soliton Degree Distribution.We chose to transfer small data packets because, in vehicularnetworks, fading, multipath and high dynamism can dramati-cally increase the bit error rate and, therefore, the packet lossrate [6]. Obviously, by increasing reliability, the drawback isthe decrease of efficiency due to the high packet overhead. InTab. I we present the main simulation parameters.

In Fig. 1(a) we present the delivery ratio as a function ofthe number of cooperative MNs in the simulated area after 15minutes. In this paper we consider the delivery ratio as the ratioof MNs that decoded the information over the total number ofinterested vehicles in the area. We notice that CORP performsbetter than the other approaches. While the CORP curve ismonotonically increasing, the delivery ratios of the other twoapproaches first increase, then decrease. By using CORP, thecollaboration of only 20 MNs is sufficient to recover the sourceinformation with a probability of 96%. Moreover, when thenumber of interested nodes further increases, the delivery ratiois always 1. On the other hand, when the DDRP approach isadopted, the average delivery ratio curve is increasing onlywhen the number of MNs is lower than 70. However, also forfew nodes, its cooperation policy is not satisfactory for theinformation reception. In particular, when more than 70 MNsare collaborating, the delivery ratio decreases. This happensbecause too many MNs are connected to the AP at the sametime. Therefore, the amount of packets transmitted to eachnode from the AP decreases, making the time to retrieve thefile longer. Finally, we can observe that an BRP approachperforms worse than the others. While the delivery ratio isalmost constant for a number of nodes less than 60, whenthere are more collaborating nodes, the performance worsensbecause multiple MNs at the same time ask data to the AP.This yields to a reduction of the rate that the AP allocates toeach connection. Furthermore, in the presence of contempo-rary connections, the access contentions on the channel reducethe available bandwidth, thus further decreasing the deliveryratio.

In Fig. 1(b) we present the evolution of the delivery ratio asa function of the time for different numbers of collaboratingnodes. We considered three density scenarios, i.e., when thenumber of nodes is 4 (dotted lines), 20 (dashed lines) or80 (continuous lines). We can observe that the collaboration

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(b) Time evolution of the dissemination.

Fig. 1: Delivery ratio as a function of cooperative nodes andtime.

policy used in CORP allows a great improvement of thedelivery ratio with respect to the one used in DDRP. Inparticular, the collaboration of only 4 MNs allows similarperformance to the one that can be achieved by using DDRPwith 80 MNs. In addiction, we can see that after 10 minutes,all the 80 MNs have received all the information if CORP isused, while only a negligible fraction of those have, if usingthe other approaches.

In Fig. 2(a) we present the average number of sets ownedby each MN. In particular, we plotted both the total numberof sets and the number of certified sets. As we can see, thetrend for both curves is linear. Moreover, the ratio between thenumber of certified sets and the total number of sets is almostconstant. This means that the channel conditions do not changesignificantly. In particular, we can observe that 70% of the setsare certified, thus allowing a fast dissemination.

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sets of ESs. We observe that the set size decreases as thenumber of nodes increases. This behavior is due to the fact thatthe AP distributes the bandwidth among the MNs in its radiorange. This yields to delivering less packets per connection,i.e., the sets are smaller. On the other hand, having small setshelps the dissemination speed.

V. RELATED WORK

The extant literature presents many proposed solutions fordata dissemination over vehicular networks (presented by theauthors [7]). The most interesting solution exploits networkcoding in order to obtain high bandwidth efficiency. However,packet loss can deeply affect the performance of these meth-ods. This problem has been recently addressed by Koetter andKschischang [8], where errors and erasures can be tolerated.This method generates codes on a vector space and avoids theoverhead of the encoded set description while simultaneouslyproviding highly efficient transmissions. Unfortunately, this is

not a desirable fit for our scenario as the the recoding phaseof network coding is computationally more expensive than asmart selection of the received information to be forwarded toother nodes.

Byers et al. [9] study the content reconciliation problem inwired networks. In their study, two nodes compute the resem-blance of their information in order to understand if the com-munication can be useful. The authors also present appropriatetools for an effective content reconciliation. Although themethods presented in their paper are very interesting, we arguethat the performance in wireless vehicular networks wouldbe poor. For example, to perform the content reconciliationmethods, nodes would be required to periodically exchangepackets to update the neighbors on its own representation ofthe information. Additional time would also be required tocompute the information that is to be sent. In sum, the contentreconciliation phase would require too much time.

Yet another solution, Bullet, is presented by Kostic etal. [10]. This method can use either rateless or multipledescription coding to efficiently disseminate data. It efficientlysolves the problem of choosing the best nodes to talk to in anoverlay mesh network. Moreover, it uses a simplified versionof TCP to fulfill the requirements of multimedia transmissionthat is also TCP friendly. Performance analysis shows thatBullet is efficient on wired networks but again performs poorlyin vehicular networks for reasons mentioned earlier.

The work presented by Maymounkov and Mazieres [11]simplifies the content reconciliation phase by sequentiallylabeling the received packets and by exchanging the sequencenumber ranges in order to perform a simple content reconcil-iation. Unfortunately, this solution can work only in wirednetworks where packet loss is almost negligible. In fact,when loss happens, the number of subranges of packets tobe transmitted grows, thus decreasing the efficiency.

Finally, a method for P2P streaming in wired networks ispresented by Wu and Li [3]. rStream is based on the encodingof the source using rateless codes. According to this method,every node forwards the information after having decodedit. Furthermore, availability of information is assured if eachnode generates a set of symbols that is independent of theothers in the network. This allows symbols to be receivedfrom several nodes at the same time without the need forcontent reconciliation. Unfortunately, a similar approach invehicular networks would perform poorly because it is basedon the implicit assumption that connections between sourcenode and interested nodes have sufficient time to receive allthe encoded information needed for recovery. However, thismay not happen in vehicular networks because a node canbe connected to APs or vehicles for only a limited time.Therefore, the transmission of the encoded information maynot be quickly completed.

VI. CONCLUSIONS

In this paper, we presented a new protocol for data dis-semination in vehicular networks. Our approach exploits thecollaboration among vehicles in order to quickly disseminate

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contents on a wide area. By performing a simple contentreconciliation phase based on certified sets of encoded sym-bols, we minimized the computational cost of this phase andmaximized the amount of time needed for the transmission.Furthermore, we increased the availability of the informationin the network by using unicast connections and rateless codes.

CORP allows an efficient dissemination in terms of bothreliability (Fig. 1(a)) and speed (Fig. 1(b)). In particular, themore the vehicles cooperate, the faster the dissemination.However, the protocol performs better than other solutions thatwe considered even when only a small number of vehiclescooperates. For example, for 30 MNs in a 3000x3000 m widearea (i.e., on average a car every 550 m2) the information isdelivered to all nodes in only 15 minutes.

Future efforts will be conducted on the analysis of the per-formance in real scenarios and protocol’s characterization asa function of key parameters. In particular, we will investigatethe impact of the beaconing time for neighbors discovering,the frequency of the advertisement message sent by the APto promote the content, the frequency of the periodic requestpackets, the influence of the transmission parameters (such aspower transmission), the packet size to be transmitted and thecontent size to be disseminated. Moreover, in real applications,security issues have yet to be addressed. Efforts will be donealso in this direction.

ACKNOWLEDGMENT

The authors wish to thank Betina Yanez and AbhishekGhosh for their precious help.

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[3] C. Wu and B. Li, “rStream: resilient peer-to-peer streaming with ratelesscodes,” in Proceedings of the 13th annual ACM international conferenceon Multimedia. ACM New York, NY, USA, 2005, pp. 307–310.

[4] M. Luby, “LT Codes,” in Proceedings of the 43rd Symposium onFoundations of Computer Science. IEEE Computer Society Washington,DC, USA, 2002.

[5] P. Maymounkov, “Online codes,” Research Report TR2002-833, NewYork University, Nov, 2002.

[6] J.Korhonen, Y. Wang, “Effect of packet size on loss rate and delay inwireless links,” in Wireless Communications and Networking Confer-ence, 2005 IEEE, vol. 3, 2005.

[7] P. Cataldi, A. Tomatis, G. Grilli and M. Gerla, “A Novel Data Dissem-ination Method for Vehicular Networks with Rateless Codes,” in IEEEWCNC, Budapest, Hungary, 2009.

[8] R. Koetter and F. Kschischang, “Coding for Errors and Erasures inRandom Network Coding,” Information Theory, IEEE Transactions on,vol. 54, no. 8, pp. 3579–3591, 2008.

[9] J. Byers, J. Considine, M. Mitzenmacher, and S. Rost, “Informed contentdelivery across adaptive overlay networks,” Networking, IEEE/ACMTransactions on, vol. 12, no. 5, pp. 767–780, 2004.

[10] D. Kostic, A. Rodriguez, J. Albrecht and A. Vahdat, “Bullet: HighBandwidth Data Dissemination Using an Overlay Mesh,” in Proceedingsof the 19th Symposium on Operating System Principles, October.

[11] P. Maymounkov and D. Mazieres, “Rateless Codes and Big Downloads,”Lecture Notes In Computer Science, pp. 247–255, 2003.

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