A Message Transmission Control Scheme for Efficient Information Sharingin Disruption Tolerant Networks

Hirofumi UedaSystem Platforms Research Laboratories

NEC CorporationKanagawa, Japan

Email: h-ueda@cb.jp.nec.com

Norihito FujitaSystem Platforms Research Laboratories

NEC CorporationKanagawa, Japan

Email: n-fujita@bk.jp.nec.com

Abstract—Recently, information dissemination techniques inDelay/Disruption Tolerant Networks (DTNs) have actively beenstudied as a technology that enables robust communicationwithout pre-installed infrastructures. To achieve the robustcommunication in DTNs, Epidemic routing is widely used.It provides robust data transfer by disseminating data to allnodes. However, it generates many duplicate control messageexchanges. When a node tries to share lots of small datasuch as node position information, message exchange overheadbecomes large. In this paper, we propose a message transmis-sion control scheme that can reduce the number of duplicatecontrol messages. The proposed scheme decides whether tosend the control message by estimating which control messagesneighbor nodes received to prevent sending duplicate controlmessage. Through simulation experiments, we demonstrate thatthe proposed scheme decreases the number of control messageswith data dissemination speed kept.

Keywords-Disruption Tolerant Networks; Epidemic routing;Message transmission control; Information sharing;

I. INTRODUCTION

Recently, information dissemination using Wireless Ad-hoc networks has actively been studied as a techniquethat enables sharing information among users without pre-installed network infrastructures [1],[2],[3].

Especially, this technique is expected to be implementedas a communication method for rescue efforts in mountainsand disaster areas [4],[5],[6]. For example, an applicationthat informs users of other users’ positions in real time isuseful, because using this information makes their rescueefforts smooth. This application characteristically requires acommunication method with good performance to quicklydisseminate lots of small information such as GPS positiondata and sensor data in the network.

On the other hand, the target network consists of wirelesslinks whose available bandwidth and communication areaare significantly limited. Furthermore, the wireless links areoften disconnected by user movements and obstacles. There-fore, the information dissemination has to be performedin a manner robust against link disruption [7]. In suchan intermittently-connected network environment, Epidemicrouting has been used as a robust communication method

[8]. In Epidemic routing, a node periodically exchangesthe control message (i.e., the Summary Vector message)including the data list of its possessed data, and obtains datathat it does not possess by requesting them to the neighbornode. By repeating this process, the data are transferredone after another, and are disseminated to all nodes in thenetwork.

However, data dissemination speed depends on the con-trol message exchange interval because data transfers areexecuted only at the timing of periodic control messageexchanges in basic Epidemic routing [9]. Thus, the nodecannot immediately transfer the data to the neighbor nodewhen it receives new data. Slow data dissemination speedhas been a problem in basic Epidemic routing. To solve thisproblem, there are several extensions for Epidemic routing,in which a node voluntarily exchanges the control messagewhen it detects new data in control messages receivedfrom neighbor nodes [10],[11]. We define such Epidemicrouting which starts the control message exchange not onlyperiodically but also voluntarily as Event-driven Epidemicrouting. This Event-driven Epidemic routing makes the nodestart the control message exchange when a node receivesnew data. Thus, Event-driven Epidemic routing can improvethe data dissemination speed by transferring more data perunit time than basic Epidemic routing.

However, Event-driven Epidemic routing generates manyduplicate control messages exchange like flooding, becausemultiple nodes voluntarily send control message when theyreceive new data at the same time. When the node exchangessmall data such as node position data, the control messageexchange overhead becomes large because the amount ofcontrol messages is relatively dominant compared to theamount of the transferred data in the data disseminationprocess (control message exchange and data transfer). There-fore, Event-driven Epidemic routing needs to reduce thenumber of duplicate control messages to improve the controlmessage exchange overhead.

In this paper, we propose a message transmission controlscheme that can reduce the number of control messages withthe data dissemination speed kept. In the proposed scheme,

2010 International Conference on Intelligent Networking and Collaborative Systems

978-0-7695-4278-2/10 $26.00 © 2010 IEEE

DOI 10.1109/INCOS.2010.37

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2010 International Conference on Intelligent Networking and Collaborative Systems

978-0-7695-4278-2/10 $26.00 © 2010 IEEE

DOI 10.1109/INCOS.2010.37

392

a node estimates which possessed data list neighbor nodeshave already received based on its control message receptionlog, and cancels the transmission of its control message ifneighbor nodes have received the same possessed data list asit possesses. The proposed scheme can reduce the number ofduplicate control messages and improve the control messageexchange overhead.

This paper is organized as follows. Section 2 explainsthe protocol overview of Epidemic routing and discussesits problems. Section 3 describes our proposed schemethat reduces the number of control messages. Section 4shows simulation model and results. Section 5 introducesthe related work. Finally, section 6 concludes with a briefsummary.

II. CLASSIFICATION OF EPIDEMIC ROUTING

Epidemic routing has been used as a robust data deliverymethod in intermittently-connected networks where wirelesslinks between nodes are often disconnected. We define basicEpidemic routing that periodically exchange the control mes-sage as Periodic Epidemic routing, and also define Epidemicrouting that starts data exchanges not only periodically butalso voluntarily as Event-driven Epidemic routing. In thissection, we describe the features of each Epidemic routing.

A. Periodic Epidemic routing

Fig. 1 shows the protocol sequence of periodic Epidemicrouting. To exchange the control message, a node period-ically broadcasts Summary Vector Messages (SVM) thatincludes the data list in its possession (Fig.1 (1)). The SVMconsists of the index lists such as hash values and IDs ofdata. When a node receives the SVM from neighbor node,it recognizes the data that it does not have by comparing itsown SVM to the received SVM. Then, the node sends a datarequest message to the neighbor node that sent the SVM(Fig.1 (2)). Thus, the node can receive only missing datafrom the neighbor node (Fig.1 (3)). Repeating this process,the Periodic Epidemic routing can disseminate the data inthe network widely.

However, the data dissemination speed depends on theSVM exchange interval because the interval is fixed. Whilea shorter exchange interval makes the data disseminationspeed faster, it makes the number of SVMs per unit timelarger. This means there is a trade off between the data dis-semination speed and the amount of traffic data. Therefore,it is difficult for the Periodic Epidemic routing to achievequick data dissemination performance with limited networkresources.

B. Event-driven Epidemic routing

Fig. 2 shows the protocol sequence of the Event-drivenEpidemic routing. This sequence is similar to the periodicone, but it also broadcasts the SVM voluntarily to informneighbor nodes of its new possessed data when a node

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receives new data from a neighbor node. After receivingnew data (Fig.2 (3)), it broadcasts its updated SVM toinform other neighbor nodes of its new possessed data (Fig.2(4)). Then, the other neighbor nodes obtain new data in thesame way as in Fig.2 (2),(3). By repeating this sequence(Fig.2 (2)-(4)), the data is transferred from one node to thenext. Thus, the Event-driven Epidemic routing can quicklydisseminate the data without waiting for the next round ofperiodic SVM exchanges.

As previously explained, Event-driven Epidemic routingis useful for quick data dissemination. However, Event-driven Epidemic routing generates many duplicate SVMs(theSVMs that include the same possessed data list), becausea node sends its own SVM to neighbor nodes whenever itreceives new data and updates its SVM. Especially, the num-ber of duplicate SVMs is significantly increased in severeenvironments such that a large number of nodes participatesin the network, and the similarity of possessed data amongnodes is low (i.e., nodes possess different data from oneanother). This is because the number of SVM exchangeopportunities and the number of neighbor nodes that sendduplicate SVMs become large. By sending duplicate SVMs,the SVM exchange overhead becomes high.

III. PROPOSED SCHEME

In this section, we describe an SVM transmission controlscheme for the Event-driven Epidemic routing. The proposedscheme focuses on reducing the number of duplicate SVMsin severe environments: (a) the case that a large numberof nodes participates in the network, (b) the case thatthe similarity of possessed data among nodes is low. Theproposed scheme has a feature in which a node decideswhether to send its own SVM by estimating which possesseddata list neighbor nodes have already received only using itsSVM reception log.

The proposed transmission control scheme consists of twophases: (i) waiting phase in which a node records receivedSVMs in a log, and (ii) decision phase in which a nodedecides whether to send its own SVM. Fig. 3 shows thespecific behavior of the proposed scheme in which node Ccancels the transmission of its own SVM.

At the initial step, nodes A, B, C and D have the data X,

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Y and Z. Then, node A updates the data X to data X′ andstarts the periodic SVM exchange with nodes B and C (Step.1). After receiving data X′ from node A, nodes B and Cvoluntarily start to broadcast their own SVMs to inform nodeD of their new possessed data. In the existing Event-drivenEpidemic routing, both nodes B and C simply broadcast theirown SVMs to node D. As a result, node D receives duplicateSVMs from nodes B and C. In the proposed scheme, bothnodes B and C transition to the waiting phase to checkwhich possessed data list neighbor nodes have received. Inthe following, we focus on the behavior of node C.

Waiting phase : In this phase, node C calculates thewaiting time to send its own SVM. Node C also recordsreceived SVMs in a log for the waiting time. Here, let usassume that node B’s waiting time has expired first. Node Bbroadcasts its own SVM to inform neighbor nodes of its newdata (Step. 2). When node C receives the SVM from nodeB, node C records this SVM in its SVM reception log (e.g.,from B: X′,Y, Z). Node C focuses only on recording thereceived SVM for the waiting time. After node C’s waitingtime is expired, node C transitions to the decision phase.

Decision phase : In this phase, node C estimates whichpossessed data list neighbor nodes have already receivedto decide whether to send its own SVM (Step. 3). In theestimation scheme we proposed, a node presumes that itsneighbor nodes know all the possessed data list that isincluded in its SVM reception log. In Fig. 3, node C’s unionof SVM reception log includes all data in its own SVM (theunion of SVM reception log: (X′,Y, Z from node A ∪ X′,Y, Zfrom node B � it own SVM: (X′,Y, Z). Thus, node C decidesthat it does not have to send its own SVM and cancels thetransmission. If node C has data W that neighbor nodes donot have (the union of SVM reception log: X′,Y, Z ∈ itsown SVM: X′,Y, Z,W), node C broadcasts its own SVM toinform neighbor nodes of the missing data W.

As previously explained, the proposed scheme uses onlySVM to decide whether to send its own SVMs. It meansthat the number of SVM exchanges depends on not thenumber of nodes but on the similarity of possessed dataamong nodes. Thus, the proposed scheme keeps the numberof SVMs constant even if the number of nodes is varied.

IV. PERFORMANCE EVALUATION

A. Simulation model

We describe the simulation model used to investigatehow the number of nodes and the similarity of possesseddata among neighbor nodes affect the number of SVMs.The simulation parameters are listed in Table I. Duringsimulation time, nodes randomly move according to theRandom Way Point model [12]. Convergence time and thenumber of messages, defined as the performance evaluationcriteria, are given as follows:

• Convergence time: The time required for all nodes tohave the same data,

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Figure 3. Behavior of proposed scheme

• Number of messages: The number of SVMs transmittedper unit time during the convergence time.

The convergence time means data dissemination speed,and the number of messages means the SVM exchangeoverhead. We examined the performance of the PeriodicEpidemic routing, the Event-driven Epidemic routing andthe proposed scheme. As previously mentioned, the PeriodicEpidemic routing only periodically broadcasts the SVM,the Event-driven Epidemic broadcasts the SVM not onlyperiodically but also voluntarily based on received new data.We call the periodic Epidemic routing “the Periodic”, andthe Event-driven Epidemic routing “the Event”.

We set the transmission delay for SVM exchanges basedon the size of an SVM and the link bandwidth, and assumethat the time for the sequence of data request and transfer((2),(3) in Fig.1 and Fig.2) is zero in order to understandthe effect of the proposed scheme clearly.

B. Simulation results

B.1(a) Impact of the number of nodes on the convergencetime and the number of messages

Fig. 4 and Fig. 5 show the number of SVMs and theconvergence time as a function of the number of nodes,respectively.

First, we focus on the results on the number of SVMsshown in Fig. 4. The Periodic shows the smallest number

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Table ISIMULATION PARAMETERS

Simulation time 3000 [sec]Simulation area 1 [km2]Num. of nodes 100∼500 default: 300Communication range 50 [m]Bandwidth 2 [Mb/s]Summary vector hash length 128 [bit]Num. of data 500Similarity of possessed data 5∼90 [%] default: 50Average speed of nodes 4 [m/sec]Epidemic interval 60 [sec]

of SVMs and linear increase with increase in the numberof nodes. This is because it performs only periodic SVMexchanges and does not generate duplicate SVMs causedby voluntary SVM exchanges. Then, the number of SVMsduring each SVM exchange interval is the same as thenumber of nodes. Therefore, the Periodic shows a linearincrease.

The Event that performs voluntary SVM exchanges showsthe largest number of SVMs, and it is increased with thesquare of the number of nodes. This is because the Eventgenerates duplicate SVMs by voluntary SVM exchanges inaddition to the periodic SVM exchanges, and the numberof SVMs depends on both the number of SVM exchangeopportunities and the number of neighbor nodes sendingduplicate SVMs. The number of SVM exchange opportu-nities is increased in proportion to the number of nodes (N )because the encounter rate between nodes is larger in thehigher node density. Then, the number of neighbor nodesis also in proportion to the number of nodes (N ). Hence,the number of SVMs in the Event is shown as follows: thenumber of SVMs ≈ the SVM exchange opportunities (∝N )× the number of neighbor nodes sending duplicate SVMs(∝N ). Therefore, the number of SVMs is increased with thesquare of the number of nodes (N2).

On the other hand, the proposed scheme shows half thenumber of SVMs of the Event, and a linear increase like thePeriodic while it performs voluntary SVM exchanges. Theproposed scheme prevents a node from sending a duplicateSVM by estimation of which possessed data list neighbornodes have already received using the SVM reception log.This makes the number of neighbor nodes sending duplicateSVMs constant because the number of SVM exchangesdepends on the similarity of possessed data between nodes.The number of SVMs in the proposed scheme is shown:the number of SVMs ≈ the SVM exchange opportunities(∝N ) × the number of neighbor nodes (C is a constantnumber). Therefore, the proposed scheme shows almost lin-ear increase (C ×N ) by significantly suppressing voluntarySVM exchanges. We do an additional evaluation to makesure this estimation is correct in section B.1(b).

Next, we focus on the convergence time shown in Fig.5. The Periodic shows the longest convergence time. Its

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data dissemination speed is the slowest among the threeschemes. This is because the Periodic has fewer opportu-nities for SVM exchanges than the other two schemes dueto periodical SVM exchanges.

The Event shows the shortest convergence time. Its datadissemination speed is the fastest among the three schemes.This is because the Event makes the data disseminationspeed faster with voluntary SVM exchanges. This makesthe Event have more opportunities for SVM exchanges thanthe Periodic. Thus, its convergence time archives half thatof the Periodic.

On the other hand, the proposed scheme shows almostthe same short convergence time as the Event while thenumber of SVMs is smaller than that of the Event (Fig.4). As explained above, the proposed scheme reduces thenumber of SVMs by half that of the Event with almost thesame convergence time as the Event kept even if the numberof nodes is varied.

B.1(b) The reduction of the number of neighbor nodessending duplicate SVMs

In the previous section, we explained that the numberof SVMs is proportional to the square of the number ofnodes (N2): the number of SVMs ≈ the SVM exchangeopportunities (∝N ) × the number of neighbor nodes sendingduplicate SVMs (∝N ). On the other hand, the proposedscheme makes the number of nodes which send duplicate

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Table IIADDITIONAL SIMULATION PARAMETERS

Network topology Full meshedNum. of nodes 10∼100Similarity of possessed data 50 [%]

SVMs constant. It makes the number of SVMs increaselinearly with short convergence time kept. In this subsection,we show how the proposed scheme affects the number ofneighbor nodes that send duplicate SVMs.

To investigate this effect, we perform an additional simu-lation experiment in which we focus on one SVM exchangeevent. In this simulation experiment, one node updates itspossessed data, and then starts the periodic SVM exchangewith its neighbor nodes as shown in Fig. 3. We assumethat the network topology is full-meshed. To make sure thatthe number of nodes that send duplicate SVMs is constantin the proposed scheme, we evaluate the message overhead,which is defined as the number of SVMs transmitted per unittime until finishing the SVM exchange when the one SVMexchange occurs in the network. The additional simulationparameters are listed in Table II and other parameters arethe same as those in Table I.

Fig. 6 shows the result of the message overhead. ThePeriodic shows a constant value. As explained in the pre-vious section, this is because the Periodic perform onlythe periodic SVM exchanges without the voluntary SVMexchange. Thus, the one SVM exchange event is finishedby one SVM transmission performed by the data updatingnode. On the other hand, the Event shows a linear increasewith increase in the number of nodes. This is because a nodesends its own SVM whenever it receives new data. If a nodesends new data to its neighbor nodes, SVMs as many as thenumber of them are sent. Therefore, the number of SVMsis proportional to the number of nodes.

The proposed scheme shows a constant value like thePeriodic. This is because a node sends its own SVM onlywhen it estimates that it has data which its neighbor nodesdo not possess. Then, the latest number of SVM exchangesdepends on the similarity of possessed data among nodes.Therefore, the number of SVMs is constant as long as thesimilarity of possessed data is not varied. The proposedscheme keeps the number of neighbor nodes that sendduplicate SVMs to constant. This makes the number ofSVMs smaller than that of the Event as explained in theprevious section.

B.2 Impact of similarity of possessed data among neighborson the convergence time and the number of messages

Fig. 7 and Fig. 8 show the number of SVMs and theconvergence time as a function of the similarity of possesseddata among neighbor nodes, respectively.

First, we focus on the number of SVMs shown in Fig.7. The Periodic shows the smallest number of SVMs, and

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Figure 6. Message overhead in one SVM exchange event

it is a constant value. This is because it does not send theSVM except for the SVM exchange intervals. Therefore,the number of SVMs is smaller than others which performvoluntary exchanges, and the Periodic shows a constantvalue.

The Event shows the largest number of SVMs, and it isdecreased with increase in the similarity of possessed data.This is because the Event generates duplicate SVMs, andthe SVM exchange opportunities are decreased. As the sim-ilarity becomes higher, the number of data which neighbornodes do not have is decreased. This means that a nodedoes not have to perform voluntary SVM exchange, becauseneighbor nodes already have the same data. Therefore, thenumber of SVMs is decreased with increase in the similarityof possessed data.

On the other hand, the proposed scheme shows about 80%the number of SVMs of the Event even if the similarityof possessed data is varied. This is because the proposedscheme prevents a node from sending duplicate SVMsas previously explained. This makes the proposed schemereduce the number of SVMs while it performs the voluntarySVM exchanges like the Event. Then, the number of SVMsin the proposed scheme is decreased with increase in thesimilarity of possessed data like the Event. This is becauseSVM exchange opportunities are decreased as explained inpart of the Event.

As we described above, the proposed scheme can reducethe number of SVMs by 80% that of the Event even if thesimilarity of possessed data between nodes is varied.

Next, we focus on the convergence time shown in Fig.8. The Periodic shows the longest convergence time. Thereason for this is the same as that shown in Fig. 5: it performsonly periodic SVM exchanges. The Event shows the shortestconvergence time among the three methods. This is becauseit has a lot of SVM exchange opportunities by performingthe voluntary SVM exchanges in addition to the periodicSVM exchanges. Then, it disseminates data quicker thanthe Periodic. Therefore, its convergence time achieves halfthat of the Periodic. As for the proposed scheme, it showsalmost the same convergence time as the Event because it

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has the same SVM exchanges method as the Event.On the other hand, each scheme decreases their conver-

gence time with increase in the similarity of possessed dataamong nodes. This is because the number of data whichis common among nodes is increased as explained in Fig.7. This eliminates the need for nodes to have to exchangetheir SVMs many times. Therefore, the convergence timethat is the time required for all nodes to have the same databecomes short.

As discussed above, the proposed scheme can keep shorterconvergence time while reducing the number of SVMs evenif the similarity of possessed data among neighbor nodes isvaried.

V. RELATED WORK

Epidemic routing is known to impose large data trafficbecause it disseminates data to all nodes. Thus, severalstudies have proposed a scheme which prevents nodes fromtransferring unnecessary data which has already been deliv-ered to destination nodes.

The basic data deletion schemes (Blind deletion, Counterscheme and Coin scheme) [10] for Epidemic routing havebeen proposed. On the other hand, Cure-ACK messages likethe SVM are proposed to prevent future data retransmissions[13]. PRioritized EPdemic (PREP) [14] and ProPHET [2];these approaches prevent nodes which are unrelated to datatransfer from possessed unnecessary data. However, while

related works focus on cutting down the size of data traffic,the reduction of the number of control messages has notbeen addressed. The existing scheme shows the number ofcontrol messages drastically increases with increasing thenode density [13]. To address this problem, our approachcan be applied to reduce the number of control messages.

VI. CONCLUSION

In this paper, we proposed an SVM transmission controlscheme for Epidemic routing in severe environments wherea large number of nodes participates in the network andthe similarity of possessed data among nodes is low. Theproposed scheme has a feature in which a node decideswhether to send its own SVM by estimating which possesseddata list neighbor nodes have already received using itsSVM reception log. Through the simulation experiments, weshowed that the proposed scheme can reduce the numberof SVMs with data dissemination speed kept even if thenumber of nodes and the similarity of possessed data arevaried. In the next step, we will propose a new Epidemicrouting algorithm using this scheme.

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