Computer Communications 34 (2011) 2174–2183

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Computer Communications

journal homepage: www.elsevier .com/ locate/comcom

Testbed results of an opportunistic routing for multi-robot wireless networks

Dong Min Kim a, Young Ju Hwang a, Seong-Lyun Kim a,⇑, Gwang-Ja Jin b

a Radio Resource Management and Optimization Laboratory, School of Electrical and Electronic Engineering, Yonsei University, 262 Seongsanno,Seodaemun-Gu, Seoul 120-749, Republic of Koreab Vehicle-IT Convergence Research Department, Electronics and Telecommunications Research Institute (ETRI), 138 Gajeongno, Yuseong-gu, Daejeon 305-700, Republic of Korea

a r t i c l e i n f o

Article history:Available online 13 January 2011

Keywords:MobilityOpportunistic routingRandom basketball routingTestbedMulti-robot networks

0140-3664/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.comcom.2011.01.009

⇑ Corresponding author. Tel.: +82 2 2123 5862; faxE-mail addresses: dmkim@ramo.yonsei.ac.kr (D

yonsei.ac.kr (Y.J. Hwang), slkim@ramo.yonsei.ac.kr, slgjjin@etri.re.kr (G.-J. Jin).

a b s t r a c t

Opportunistic routing is a candidate for multihop wireless routing where the network topology and radiochannels vary rapidly. However, there are not many opportunistic routing algorithms that can be imple-mented in a real multihop wireless network while exploiting the node mobility. It motivates us to imple-ment an opportunistic routing, random basketball routing (BR), in a real multi-robot network to see if itcan enhance the capacity of the multihop network as mobility increases. For implementation purposes,we added some features, such as destination RSSI measuring, a loop-free procedure and distributed relayprobability updating, to the original BR. We carried out the experiments on a real multi-robot networkand compared BR with AODV combined with CSMA/CA (routing + MAC protocol). We considered bothstatic and dynamic scenarios. Our experiments are encouraging in that BR outperforms AODV + CSMA/CA, particularly in dynamic cases; the throughput of BR is 6.6 times higher than that of AODV + CSMA/CA. BR with dynamic networks shows 1.4 times higher throughput performance than BR with static net-works. We investigate the performance of BR in the large-scale network using NS-2 simulation. We verifythe effect of node density, speed, destination beacon signal and loop-free procedure. According to thelarge-scale simulation, the end-to-end throughput grows with the node speed.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

In wireless communications, mobility has been a successfulapplication, but it comes with radio fading, which has been anobstacle to overcome for spectral efficiency (bits/Hertz). However,the two aspects (pros and cons) of mobility have recently been re-viewed from another angle. Exploiting node mobility can increasethe capacity of a wireless network even if it has negative impacton the link-level wireless communications. Such a viewpoint wasraised by random beamforming [1], and later by opportunisticscheduling (see [2] and literature therein), by information-theoretic multihop capacity [3], and summarized as multiuserdiversity [4]. More fundamental results on multiuser diversity canbe found in [5]. The general belief is that multiuser diversity gainsmore benefits when the node mobility prevails up to a certain level.

The current wireless network moves toward multihopping,which allows nodes to relay packets of other nodes to their desti-nations. This includes ad hoc radio networks, sensor networks,wireless mesh networks, mobile multihop relay systems andmulti-robot wireless networks. In addition to coverage extension

ll rights reserved.

: +82 2 313 2879..M. Kim), yjhwang@ramo.

kim@yonsei.ac.kr (S.-L. Kim),

for small-scale radio transmitters (as in ad hoc radio networksand sensor networks), a major reason for adopting such multihoptransmission is for capacity enhancement [6] supported by linkadaptation, which may pay off with increased system complexity.A practical example of such a multihop network can be found inmobile multihop relay, IEEE 802.16j [7], where the coverage as wellas the capacity exceeds the complexity of the system. While multi-robot wireless networks started with communication models be-tween robots and single base station, it has now evolved towardgroups of robots communicated by multihop wireless ad hoc net-works. A group of robots can achieve a common mission in a coop-erative distributed way by communicating each other [8].

There is also an unsolved issue in multihop communications. Inmultihop wireless networks, there must be routing algorithms de-fined either explicitly or implicitly to determine the path that apacket travels. Most of the routing algorithms have been designedunder static settings where nodes are assumed to be stationary.Unfortunately, in practice, those routing algorithms may not beoptimal in many senses under dynamic environments, resultingin frequent route changes [9].

For a multihop network, node mobility would increase the net-work capacity much more than in the static case [3]. One questionis whether it is possible to design a multihop routing algorithmthat can fully take advantage of node mobility. With mobilenodes, the network topology changes often, and radio channels

D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183 2175

dynamically fluctuate. In this case, a given routing path for multi-hop transmission cannot be assured as optimal, and new pathshave to be set according to the changing environments over time.These frequent route changes would cause excessive signalingoverhead for the network. Therefore, a routing strategy requiringless signaling would be more advantageous for multihop wirelessnetworks with mobile nodes.

1.1. Related works

Multi-robot wireless networks have been studied recently inthe automatic control and robotics society. Some experiments havebeen conducted on real testbeds with robots through academic re-searches and related projects such as Swarm-bots project [10]. In[11], synergies among mobile robots and wireless sensor networkswere shown by testbed implementations, where the mobile robotsare allowed to help gathering data from sensor nodes. The authorsof [12] realized communication-aware motion control methods,which let the mobile robots measure the SNR and adapt their mo-tions to the channel quality. Suzuki proposed a method usingmobility to assist their sensing tasks [13] for multi-robot sensornetworks. In this scheme, to reduce the total latency, the move-ment of a node in the networks can be controlled by the local stateof the neighbor nodes. In [14,15], the authors have tested and com-pared different ad hoc routing protocols for wireless communica-tion between mobile robots. Also McLurkin, the manager ofswarm project, designed and implemented distributed algorithmsfor multi-robot communications through extensive robot experi-ments [16]. In the above works, each mobile robot communicateswith a sensor node or a base station and there are no relayingschemes taking the mobility and the opportunistic routing [17]into account at the same time.

In wireless ad hoc networks (where nodes are usually stationary),there are two types of routing protocols. One is the table-drivenrouting, where each node has the routing table for all of the nodesin the network and carries out routing based on the table. In theseroutings, maintaining the network-wide information is a seriousburden for the nodes when the topology changes frequently. Theother is the on-demand routing, where a route is obtained by thenode demanding data traffic, and only information for real routesis maintained. These routings are preferred because they can reducethe routing overhead with topology changes. One of these types ofprotocols is ad hoc on-demand distance vector (AODV) [20]. AODVdecreases signaling overhead by preserving the route informationat the intermediate nodes. Unfortunately, even AODV may not beoptimal under node mobility environments [21].

In order to set up the new paths flexibly according to changingnetwork environments, the routing protocols designed for mobilenodes should not only be light with respect to routing overhead,but also self-configurable. In these routings, a node may relay thesame packet more than once, and a source node may relay itsown packets due to its position change. One promising directionis a per-hop-based (i.e., hop-by-hop) routing, where each node for-wards its packet to its neighboring nodes under favorable positions.The term ‘‘favorable’’ can be defined differently, depending onwhich objective is set for the network performance (e.g., energy,delay, jitter, throughput, and so on). This kind of routing is in gen-eral known as opportunistic routing (see [17,22–24] and literaturetherein).

1.2. Our contribution

The main focus of the paper is to see the impact of the nodemobility on the end-to-end throughput. In [25], it is shown thatthe cross-layer optimized opportunistic routing achieves the per-node throughput increases as the node mobility increases to a cer-

tain point. Since the results are based on key theoretic assump-tions, it motivated us to implement an opportunistic routing,random basketball routing (BR) in a real network to see if it can en-hance the capacity of the multihop network as mobility increases.

In this work, we realize BR, for the multihop communicationsbetween mobile nodes. To verify BR’s adaptability to the changesof topology and node density, we implement BR on multi-robotwireless networks where the robots are densely distributed andmoving, and provide the relay probability update procedure. In[18], the authors uses BR as a communication protocol for cooper-ative multi-robot path finding based on our preliminary imple-mentation [19]. The multihop communications protocolsdesigned for mobile robots should be not only light in terms ofoverhead but also self-configurable according to the robotmovement.

BR was originally suggested [25] for finding a capacity scalinglaw in a random multihop wireless network, as motivated by Gup-ta and Kumar [26]. BR is a simple per-hop-based multihop routingthat incorporates node mobility into the routing protocol design.BR is also fully self-configurable in the sense that the next for-warder is determined adaptively at each hop, without knowledgeof the entire network topology. One special feature of BR is thatit integrates MAC and multihop routing in a cross-layer optimizedmanner. In BR, there is a key parameter called relay probability, p,and we can handle MAC as well as routing by simply controllingthe relay probability. These distinctions make BR attractive forwireless sensor networks in which complexity of each node isquite low so that executing a typical closed-loop control with fullfeedback and measurement is rather hopeless.

We propose signaling protocols, frame subculture and the otherpractical algorithms, such as the relay probability update proce-dure, for implementation of BR. We carry out the experiments ona real multi-robot network and compare BR with AODV, which iscombined with carrier sensing multiple access/collision avoidance(CSMA/CA). We apply both static and dynamic scenarios. For thedynamic case, we use mobile robots in a closed area to investigatethe impact of node mobility. Our experiments are encouraging inthat BR outperforms AODV + CSMA/CA (routing + MAC protocol),in particular in dynamic cases; the throughput of BR is 6.6 timeshigher than that of AODV + CSMA/CA. BR with dynamic networksshows 1.4 times higher throughput performance than BR with sta-tic networks. These results demonstrate that mobility increases thecapacity of multihop wireless networks. We investigate the perfor-mance of BR in the large-scale network using NS-2 simulation. Weverify the effect of the node density, speed, the destination beaconsignal and the loop-free procedure. According to the large-scalesimulation, BR increases the end-to-end throughput of multihopwireless networks, especially when nodes are mobile.

The rest of the paper is organized as follows. In Section 2, wepropose signaling processes for BR. In Section 3, we describe theimplementation details, such as packet structure/type, a loop-freeprocedure (for the stationary case) and the relay probability updatealgorithm. We examine how BR operates on a real testbed withexperiments in Section 4 and large-scale simulation is conductedin Section 5. We conclude the paper in Section 6.

2. Random basketball routing

2.1. Description of the routing protocol

Random basketball routing (BR) has a key parameter p, which iscalled the relay (receiving) probability [25]. For a given time slot, asource node having data is either in transmission mode with prob-ability 1 � p or in receiving mode with probability p. When asource node is in transmission mode, it sends its packets either

broadcastNodeIDtype

responseNodeID dstRSSI

destNodeIDsourceNodeID

sendNodeID recvNodeID

hopCount

16 bits

Fig. 2. BR packet structure.

2176 D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183

to a relay node or directly to the destination (e.g., whichever isclosest). When a source node is in receiving mode, the node listensto the other transmitting nodes as a candidate relay node. In thecase that the node in receiving mode with probability p, is not cho-sen as a relay node, the node remains idle during that slot.

Fig. 1 shows the signaling process of BR for a source node intransmission mode to choose a relay. If a network is static or haslow mobility, destination node D periodically broadcasts a beaconsignal so that the strength of the signal is measured and stored byeach receiving node. When node S wants to transmit some packetsin transmission mode (determined by the probability 1 � p), itsends out a Request-to-Send (RTS) to its neighboring candidate re-lay nodes (determined by the relay probability p) within the radiorange. The RTS frame header should have the identification (ID) ofnode S, the owner of the RTS. Each neighboring candidate relaynode, after receiving RTS, waits for several short random time slotsto avoid collision and then sends out an acknowledgment (ACK). Inthe ACK packet header, there should be at least two pieces of infor-mation: one is the measured signal strength of the periodic beaconsignal from the destination and the other is the ID of the candidaterelay node (i.e., the owner of ACK). After receiving ACK packetsfrom neighboring candidate relay nodes, node S compares themand chooses a relay node (to pass data packets) that reports thestrongest received beacon signal from D. If the destination is faraway from the source and its neighbors, none of them will havea reasonable measurement of the signal strength of the beacon sig-nal. In this case, the source node chooses a relay node that hasstrongest signal strength of ACK itself. If the network involves alarge number of destinations and has a high mobility, the destina-tion beacon signal would not be used because lots of broadcastpackets downgrade system performance. The detailed performancemeasurement results are presented in Section 5. In [25], the origi-

Fig. 1. Signaling for choosing a relay.

nal basketball routing algorithm was suggested with optimizedparameters for the capacity scaling laws. However, the practicalversion of BR with respect to mobility has been an open issue.On the other hand, we try to show practical applicability of BR in[19], where a method of destination RSSI measuring is suggested.

When node S transmits a packet to a relay node, the packet issuccessfully received on the condition that the received SINR at areceiving node is not less than a target SINR. If the transmissionfails, the network adopts the binary exponential backoff (BEB)[27] for collision resolution. In BR, such a retransmission occursindependently of the probability 1 � p, after the random backoffslots. On the other hand, if node S does not receive any ACK, thenode runs BEB and sends out the RTS again.

In BR, we can handle not only routing but also MAC of the net-work, by simply controlling the relay probability. When p = 0, anextreme case, there is no relay, and the routing is reduced to sin-gle-hop transmission, where every node simultaneously transmits.As the probability p increases, there are more relay nodes (i.e., few-er transmitting nodes) around a transmit node, reducing the aver-age transmission distance and the delay from retransmissions.However, the transmission probability 1 � p of a node also de-creases and the opportunity for the transmission itself becomessmaller. In the other extreme case, p = 1, no node is transmitting.Therefore, an optimal relay probability exists, at which the maxi-mum network throughput can be obtained. In [25], the optimalprobability that maximizes the end-to-end throughput is analyzedand derived, as a function of the node density of a random wirelessnetwork.1 However, in practice, it is rather difficult to measure thenode density by each node, and the node density itself varies accord-ing to the mobility. Thus, we need to have a procedure that deter-mines the relay probability, adaptive to the environment, which isdiscussed in Sections 3 and 4.

2.2. Packet structure of BR

Fig. 2 shows our design of a BR packet to support the protocolsin Fig. 1, where we assigned 8 bits to each field of the packet, ex-cept the hopCount field (16 bits). The current field sizes aredependent on our experimental settings. Tables 1 and 2 containmessage types of a BR packet and a brief description of each packetfield, respectively.

When nodes are powered on, they listen to a channel to receivepackets. A TYPE_DSTBCAST message is periodically broadcast by adestination so that the destination informs other nodes of its ownexistence. After receiving TYPE_DSTBCAST, each node measuresRSSI and stores it in dstRSSI. According to the relay probability,a source node broadcasts a TYPE_SRCBCAST message to choose

1 The random wireless network is first defined in [26] as a wireless network wherevery communication pair of a source and a destination node is randomly located in aiven service area, and the pair communicates possibly by multihop relay.

eg

Table 1Message type of a BR packet.

Message Type Description Elements

TYPE_SRCBCAST RTS message broadcastNodeID

TYPE_DSTBCAST Location informing message broadcastNodeID

TYPE_RESPONSE ACK to RTS message broadcastNodeID

responseNodeID

dstRSSI

TYPE_ROUTING Data message sourceNodeID

destNodeID

sendNodeID

recvNodeID

hopCount

TYPE_ACK ACK to data message responseNodeID

Table 2BR packet field description.

Message field Description

type Message typebroadcastNodeID Broadcasting node IDresponseNodeID Responding node IDdstRSSI Destination message RSSIsourceNodeID Message generator IDdestNodeID Message receiver IDsendNodeID Current forwarder IDrecvNodeID Next forwarder IDhopCount Number of forwardings (relays)

D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183 2177

the next forwarder. The neighboring nodes, after receivingTYPE_SRCBCAST, decide whether to send out TYPE_RESPONSE ornot, depending on their relay probabilities. Then, the source nodecompares its own dstRSSI and those in received TYPE_RESPONSE.If the source dstRSSI is the largest, the source node transmitsTYPE_ROUTING directly to the destination. Otherwise, the sourcenode sends TYPE_ROUTING to the relay node that has the largestvalue of dstRSSI (Fig. 1). The node that receives TYPE_ROUTINGsends a TYPE_ACK packet to the source node, and checks if dest-NodeID is the same as its local address. If that is the case, the rout-ing is completed; otherwise the relay node that has just receivedTYPE_ROUTING broadcasts TYPE_SRCBCAST (according to the re-lay probability) and the above procedure is repeated.

Fig. 3. Message-looping resolution in static networks.

3. Loop-free procedure and adaptive relay probability updatealgorithm

With all the merits of BR, there are also some drawbacks. WithBR, a node can relay the same packet more than once, and even thesource node can relay the packet it generated. This feature is to uti-lize the node mobility in routing design. One problem, however, isthat there might be some loops on the routing paths created by BR,and traffic could circulate within a loop. This can occur especiallyin static environments. In the case that some nodes are gathering,apart from the other nodes in a static network, their packets are ex-changed only among the nodes, not forwarded to their destina-tions. Then the packets in a loop are dropped after all. In thissection, we describe a simple loop-free algorithm to cope withsuch traffic circulation within the network, which can occur dueto the randomness of BR.

As previously mentioned, in BR, the optimal relay probability isa function of the node density over the network area [25]. Whenthe number of nodes increases in a multihop wireless network,each node raises the relay (receive) probability to reduce the inter-ference and collision from simultaneous transmissions. In contrast,if there are few nodes in the network, the interference between

nodes is not that severe, and the nodes can lessen their relay prob-ability to achieve higher throughput by encouraging simultaneoustransmission. Thus, the relay probability should be set adaptivelyto the node density. This density-aware update is important, espe-cially in the case that the node topology changes frequently, forexample when nodes are moving. Therefore, we also suggest a sim-ple and efficient relay probability update algorithm, which oper-ates adaptively to the node density.

3.1. Loop-free procedure

To avoid having a packet run into message loops, we set aLOOP_THRESHOLD for the network and use the hopCount field inthe packet structure (Fig. 4). Each node counts for a received packetto check how many times the received packet has been forwardedby updating the hopCount field in that packet. If hopCount ex-ceeds LOOP_THRESHOLD at a node, the node applies the loop-freemechanism as follows: the node no longer relays the receivedpacket toward the node to which it forwarded the packet before.Instead, the node finds the other forwarder around it. The proce-dure of this loop-free algorithm is illustrated in Fig. 3. This algo-rithm is motivated by a traditional loop-free procedure (see [28]and literature therein) and easy to implement for multi-robot sen-sor node which has limited computing capability due to the simplestructure. The detailed performance of loop-free procedure isinvestigated in Section 5.3.

3.2. Adaptive relay probability update algorithm

As the nodes self-configure their routes in BR, the relay proba-bility update algorithm should also be accomplished distributively.For this purpose, in our algorithm each node updates its own relayprobability utilizing the RTS signal, without additional control sig-nal overhead. The proposed algorithm is motivated by distributedtransmission power control [29]. The adaptive update algorithmconsists of the following three steps:

Step 1. When each source node i wants to transmit its packets, itcounts the number of responses for its RTS signal, Rðkþ1Þ

i .Step 2. Update the relay probability pðkþ1Þ

i :pðkþ1Þi ¼min

max pmin;Rðkþ1Þ

i

RðkÞi

pðkÞi

� �; pmax

� �.

Step 3. k k + 1 and go to Step 1.

Within the pre-defined update range [pmin,pmax], the nodes con-trol their relay probability according to the number of receivedACK signals as responses to their RTS signals. When the numberof received answers becomes higher, a node can think that thenumber of nodes increases as well. On the contrary, if a node

69

9M

9M7

8

5

4

2

3

1 dst

4M

Fig. 4. Circular-shaped topology.

Table 3Experiment parameters.

Parameter Value

RESPONSE_WAIT_TIME 0.5 sACK_WAIT_TIME 0.2 sBCAST_TIME 10 sLOOP_THRESHOLD 10 hopTransmission range 6 MRadio frequency 2.4 GHzMaximum data rate 250 kbps

2178 D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183

receives fewer responses from neighboring nodes, the node regardsit as a decrease of the number of nodes in the network. Eventhough a node may not know exactly the change of the nodedensity just by counting the ACK signals, this algorithm eventuallyreflects the density variation from a time-averaging viewpoint. Inthe update range, pmin and pmax are basically set to 0 and 1, respec-tively; however, we can determine the values agreeable to thenetwork environment.2

4. Experiments on multi-robot testbed

We have implemented BR on a multi-robot testbed, which con-forms to the IEEE 802.15.4 physical layer specifications.3 Each robotruns TinyOS and is equipped with an 8-bit RISC ATmega128 and aCC2420 chip as a microcontroller and a radio transceiver, respec-tively. Key parameters for our experiments are specified in Table 3.

BR integrates MAC and routing into one, making itself light andsimple. To examine the performance of BR, we compare BR with itscounterparts, where CSMA/CA for MAC protocol and AODV for therouting protocol, are used, respectively. CSMA/CA is implementedas B-MAC [30] on our multi-robot testbed, utilizing a CC2420 radiomodule. B-MAC executes carrier sensing for channel reservationand has a backoff timer. Unlike BR, however, the backoff windowsize is fixed at 200 ms, and the timer is set in blocks of 10 ms.The backoff window size of binary exponential backoff in BR is ini-tially set at 1 ms and does not increase above 1024 ms. On theother hand, the AODV routing corresponds to TinyAODV in HSN re-lease 3 version [31] in our multi-robot testbed, which has severalmajor simplifications to implement the AODV protocol lightly:RREP messages are only generated by the destination, and onlythe hop count metric is used to select a route. In addition, routesnever expire and route errors are detected by the link-levelacknowledgments.

2 According to the node density, pmin, pmax and the initial value of a relayingprobability p should be selected properly.

3 All communication hard- and softwares in our experiments are based on ZigbeXbundle manufactured by Hanback Electronics, Co., Ltd., Korea (http://en.han-back.co.kr/).

To investigate how BR operates solely as a MAC protocol and asa routing protocol separately, we performed two different experi-ments using different topologies and metrics, which were suitablyselected for the comparison of each function (routing and MAC). InSection 4.1, to examine the MAC functionality of BR, we consider acircular topology, where boundary nodes access the center node. Inthe topology, the center node is congested and we would like toinvestigate how such congestion is mitigated by BR. Because therouting is only one-hop communication, we can remove the effectof routing in this experiment. In Section 4.1, we see that BR per-forms better than CSMA/CA in terms of packet delay. On the otherhand, to see the performance of BR as a routing algorithm, we useda cross shaped topology, where efficient routing plays an impor-tant role in throughput and the lifetime of the network. In Sec-tion 4.2, we found that BR is an energy efficient routing,compared to AODV, and prolongs the lifetime of the network bybalancing usage of each node. In Section 4.3, we made a compre-hensive experiment to examine throughput performance. We ap-plied both static and dynamic scenarios where, for the dynamiccase, we use mobile robots in a closed area to investigate the nodemobility impact.

4.1. BR versus CSMA/CA MAC

To examine how BR operates as a MAC protocol, we placednodes in our testbed circularly and let them transmit their packetsto the destination by single-hop (Fig. 4). The size of the area ofinterest is 9 by 9 square meters. The transmission range of nodesis 6 m. The destination is located at the center of the circle with ra-dius 4 m. We set the relay probability for this network. As men-tioned in Section 3, the relay probability is dependent on thenode density, and thus an appropriate relay probability should beset in this network. As shown in Fig. 5, it is found that the optimalrelay probability, with which the network throughput can be max-imized, increases with the number of nodes in this network (0.1 for3 nodes and 0.2 for 18 nodes). For each fixed relay probability, wemeasured the average time delay for a packet to arrive at the des-tination for different values of the number of nodes. The tendencyis important rather than the actual value because actual value canbe varied with different parameter settings while overall tendencyis independent. When the node density increases over the network,each node should heighten its relay probability to reduce interfer-ence and delay from it.

In the case that nodes are often added/removed in a multihopwireless network, for example in a mobile ad hoc network, control-ling the relay probability is needed in particular. The problem ishow we find the optimal relay probability distributively. We pro-pose an adaptive relay probability update algorithm for this prob-lem. Our algorithm is also applicable to the static networks asshown in Fig. 6. The update range [pmin,pmax] of our algorithmcan be adjusted according to the state of the network. Here wedetermine 0.05 and 0.5 for pmin and pmax, respectively, since theoptimal relay probability usually exists below 0.5 in this network(Fig. 5). Fig. 6 represents how the relay probability of a node

0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60

time

rela

y pr

obab

ility

(%)

max 50%, 3 nodesmax 50%, 18 nodes

Fig. 6. Adaptive relay probability update within the range [0.05,0.5].

0 1 2 3 4 5 6 7 8 9 10

4600

4800

5000

5200

5400

5600

5800

6000

6200

6400

6600

number of nodes

dela

y (m

s)

B−MACBR

Fig. 7. Delay performance of BR compared with CSMA/CA.

10 20 30 40 500

2000

4000

6000

8000

10000

12000

relay probability (%)

dela

y (m

s)

3 nodes18 nodes

Fig. 5. Delay as a function of the relay probability for different node densities.

4 All robot hard- and softwares in our experiments are based on Bioloid robotsmanufactured by Robotis Inc., Korea (http://www.robotis.com).

D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183 2179

updates by the adaptive algorithm. It is shown that the values after50 iterations are almost the same as the optimal values repre-sented in Fig. 5.

In the topology of Fig. 4, we compare BR with B-MAC, a realizedversion of CSMA/CA MAC protocol. Fig. 7 shows the average delaytaken for a node to transmit a packet to the destination. Whenthere are many nodes in the network, BR outperforms CSMA/CA,yielding smaller delay. In our experiments, packet length is con-stant, 38 bytes. Throughput can be calculated by a reciprocal of de-lay. Thus, the smaller delay means the better per-node throughput.In CSMA/CA, which is a contention-based MAC, one time slot isallocated for only one node, and the node transmits its packet to-ward the destination without any interference. With BR, however,several nodes may transmit at the same time, if the interferencebetween them is not too severe, due to the properly controlled re-lay probability. Therefore, BR allowing simultaneous transmissionscan be better than CSMA/CA as the number of nodes increases. Incase of a small number of nodes, however, the merit of simulta-neous transmission from BR disappears, and CSMA/CA can evenoutperform it. This is because, in BR, each node can send its packetonly when it turns on in transmission mode according to the relayprobability, and the nodes may obtain fewer chances for transmis-sion than CSMA/CA. Thus, BR as a MAC protocol is advantageous

especially for congested networks. Of course, BR decreases thenumber of simultaneous transmitting nodes as the node densityincreases, but the decrease is not more than that of CSMA/CA.

4.2. BR versus AODV routing

We investigate the performance of BR as a routing protocol bycomparing it with TinyAODV, the simplified version of AODV, onour multi-robot testbed. As shown in Fig. 8, we placed the nodesin a cross-shape, with the center node more crowded and the outernodes less so. In the topology of Fig. 8, every node has its ownpackets and tries to send them by multihop transmission. The des-tination of the horizontally located nodes is the node at the end ofthe right side, and the destination of the perpendicularly locatednodes is the node at the downside of the topology. Here we exam-ine how BR operates as an energy-efficient routing algorithm bymeasuring the utilization of each node in the network. We countedhow many times the node is used in multihop transmission for apacket from node 1 to its destination at the end of the oppositeside. Fig. 9 shows that the nodes are more evenly utilized withBR than with TinyAODV, but sacrifice the number of hops. In Tiny-AODV, the source node always routes its packet to the destinationalong the pre-established path via the center node. Consideringthat nodes are crowded around the center node, the center nodeis constantly utilized for relaying and the energy of the center nodeis likely to be exhausted. As in sensor networks, in the case thatnodes have limited power resources, the excessive utilization ofspecific nodes becomes a serious problem, causing node breakages.In contrast, since BR routes packets by randomly chosen forward-ers based on the relay probability, several neighboring nodes ofthe center node also participate in forwarding the packet fromnode 1. Even if the number of hops rises, BR prevents traffic fromthronging into some specific nodes. This load balancing effectmakes BR an attractive energy-efficient routing protocol.

4.3. Dynamic networks

To experiment the node mobility, we combined the communi-cation sensor node with the mobile robot, as shown in Fig. 10.4

The mobile robot moves randomly in the area, and it is programmedto change its direction by 90� clockwise as soon as it faces an

11

6

169 1715

9M

9M

7

8

14

5

10

4

12

13

2 31

2M 0.66M

Fig. 8. Cross-shaped topology.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

2

4

6

8

10

12

node ID

utili

zatio

n co

unt

TinyAODVBR

Fig. 9. Utilization of each node in AODV and BR.

Fig. 10. Communication sensor embedded mobile robot.

Fig. 11. Mobility test with mobile robots.

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Fig. 12. TinyAODV with dynamic netwoks.

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obstacle. In addition, the mobile robot randomly chooses a newdirection in every 2 s. The speed of a mobile robot is 0.5 m/s. Weuse 3, 5, 7 and 9 mobile robots, as shown in Fig. 11, in a4 m � 4 m square enclosed space, where one node is the destinationfor the remaining nodes.5

Figs. 12 and 13 contain the number of hops that a packet tra-verses on average to the destination, and the average delay ofthe packet delivery to the destination, respectively. As a referenceto our BR, we use TinyAODV with CSMA/CA. In Fig. 12, in case ofthe static network with three nodes, every node relays packetswhen TinyAODV is executed. In contrast, with BR, it is possible toskip a nearby node due to its opportunistic nature. We see thatthe average number of hops is less in the dynamic case for both

5 Moving pictures of the experiments can be downloaded from http://hertz.yonsei.ac.kr/br.avi.

BR and TinyAODV. This is because the random node mobility hasthe same impact as the disposition of the nodes to be evenly

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Fig. 13. Average delay of a packet at the destination as a function of the number ofnodes.

D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183 2181

located in the area. With more than three nodes, the average num-ber of hops (Fig. 12) is decreased by 0.8 hop (39%) in our BR, whenshifting from the static case (blue dash line) to the dynamic case(blue solid line). On the other hand, we cannot see any significantdifference between BR and TinyAODV in terms of the number ofhops for the dynamic case. However, when we check the packetdelay, the situation is different. In Fig. 13, we see that the packetdelay is much smaller in BR than in TinyAODV. The packet delayincreases as the nodes move in TinyAODV. However, with BR, wehave even less delay in the dynamic case than in the static case.The large delay in TinyAODV comes from the packet retransmis-sion, caused by the link failure, which becomes more serious whenthe nodes are moving. This says that BR adapts to the node mobil-ity sensitively.

We derived the throughput curves in Fig. 14. In the figure, theend-to-end throughput is the amount of transmitted bytes froma source to a destination during 1 s. In all cases, BR (blue lines withcircle symbols) shows better performance than TinyAODV (redlines with triangle symbols). Especially in dynamic cases, thethroughput of BR (blue solid line with circle symbols) is 6.6 timeshigher than that of AODV + CSMA/CA (red solid line with trianglesymbols). When using BR, throughput in dynamic networks (blue

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Fig. 14. End-to-end throughput in bytes per second as a function of the number ofnodes. The node speed is 0.5 m/s.

solid line with circle symbols) is 1.4 times higher than that in staticnetworks (blue dash line with circle symbols). Otherwise, whenusing TinyAODV, the result is reversed. Figs. 12–14 show thatour BR increases the capacity of multihop wireless networks, espe-cially when nodes are mobile.

5. Large-scale network simulation

In this section, we verify the performance of BR in the large-scale networks using NS-2 simulator (release version 2.34) [32].Nodes are independently and uniformly distributed in the500 m � 500 m rectangular area. We use 50, 100 and 150 nodes.The transmission range is 20 m for all nodes. The node densityand traffic load are relatively higher than our real experiment set-ting in Section 4. We use the relay probabilities derived in [25] forthe each node density. We use 0.63, 0.77 and 0.84 for the 50, 100and 150 nodes, respectively. The half of the nodes are the sourcenodes. The destination of a given source node is randomly chosenamong the other half. There are multiple source–destination pairs.We assume the random waypoint mobility model [33] with 5 spause time. We use 1 m/s, 5 m/s, 10 m/s and 15 m/s as the nodespeed. We investigate the effect of the node density, speed, thedestination beacon signal and the loop-free procedure in the fol-lowing subsections.

5.1. Effect of the node density and speed

End-to-end throughput as a function of node density is shownin Fig. 15. As the number of nodes increases, the end-to-endthroughput decreases. As the node speed increases from 1 m/s to10 m/s, the end-to-end throughput of BR increases more than 10times. As the nodes move faster, the network topology changesaccordingly and previous topology information is getting useless.BR does not gather the information about route to destination,but uses the neighbor information at the moment of transmission.As shown Fig. 15, BR is more effective in the high mobility situa-tion. As the node mobility increases, a node with packets has morechances of those packets reaching their corresponding destina-tions. This leads to an end-to-end throughput increment.

5.2. Effect of the destination beacon signal

We investigate the effect of the destination beacon signal bychanging node density and mobility. As the number of destinationnodes increases, the beacon signal increases too. As a result, therewill be lots of broadcast packets causing performance degradation.Fig. 15(a) shows the end-to-end throughput as a function of thenumber of nodes with the destination beacon. The node speed is1 m/s. With 50 nodes, the destination beacon signal can increasethe end-to-end throughput. However, with 100 and 150 nodes,the performance is better without the beacon procedure. With1 m/s mobility, 0.1 s beacon signal interval produces duplicatedinformation. To accurately update the destination, the beaconinterval should be in inverse proportion to the node speed. InFig. 15(b), the node speed increases 10 m/s. The destination beaconsignals improve the performance slightly with 50 nodes. In thiscase, the 0.1 s beacon period shows higher throughput than 1 s.This is due to the accurate destination update. However, with100 and 150 nodes, the performance degradation of beacon signal-ing becomes severe. This means that the destination beacon signalshould be refrained under the highly dense network with manysessions. The proper beacon interval should be the reciprocal ofthe node speed. However, if the node speed is high, it is betternot use the destination beacon signal. With the fast moving nodes,the direction to the destination changes rapidly and instantaneous

50 100 1500

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Fig. 15. End-to-end throughput as a function of the number of nodes. The destination beacon signal is broadcasted (1 s,0.1 s interval) or not.

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beacon signal strength may not help the routing, as compared tothe frequent beacon signaling overhead.

5.3. Effect of the loop-free procedure

In Section 3.1, we introduced a simple loop-free procedure toadapt BR to static or slow mobile networks. Fig. 16 shows the effectof the loop-free procedure on the end-to-end throughput. As thenode speed increases, the gain of the loop-free procedure de-creases. As a node moves faster, the chances for the node to meetthe destination will increase, which in turn it decreases the num-ber of hops required to reach the destination. Thus the conditionof triggering the loop-free procedure may hardly be met.

5.4. TCP considerations

Our NS-2 simulation results are based on UDP traffic. In themulti-robot networks, the exchanged data are, for example, aboutmap information for path planning, sensing information for explo-ration, etc. These types of data are short and burst, and may not re-quire packet level acknowledgement from its destination [18]. Thismeans UDP traffic is suitable for the multi-robot networks. Thecomputing capability of a micro robot is limited due to the power

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Fig. 16. End-to-end throughput as a function of the node speed. The number ofnodes is 100. The loop-free procedure is adopted or not.

constraints. Opportunistic routing allows consecutive packetsbeing transmitted along different paths, which causes out-of-orderpacket delivery at the destination [34]. BR may interact badly withthe end-to-end control scheme in traditional TCP. According to[35], hop-by-hop control mechanism is a promising approach formultihop wireless networks. BR would be more properly interactwith hop-by-hop control as shown in Fig. 1.

6. Conclusions

We tested an opportunistic routing, random basketball routing(BR), which was originally suggested in [25], in real environments.We proposed signaling protocols, frame subculture and the otherpractical items (e.g., the adaptive relay probability update algo-rithm). We carried out the experiments on real multi-robot test-bed, and compared BR with TinyAODV combined with CSMA/CA.We considered both static and dynamic scenarios. Even if BR reapsthe benefits of dynamic environments, we tested how it works un-der static situations by adding some features, like the loop-freeprocedure, to the original BR. Our experiments are encouragingin that BR outperforms AODV + CSMA/CA (routing + MAC protocol),in particular in dynamic cases; the throughput of BR is 6.6 timeshigher than that of AODV + CSMA/CA. BR with dynamic networksshows 1.4 times higher throughput performance than BR with sta-tic networks. We investigate the performance of BR in the large-scale network using NS-2 simulation. We verify the effect of nodedensity, speed, destination beacon signal and loop-free procedure.According to the large-scale simulation, the end-to-end through-put grows with the node speed.

We found from our real multihop wireless testbed that it is pos-sible to design a multihop routing algorithm that can fully takeadvantage of the node mobility, and the proposed practical versionof BR is a promising candidate routing/MAC protocol in static aswell as mobile multi-robot networks. Future work will focus onthe implementation of a capacity achieving real testbed for a largesize dynamic multihop network. Simulation is not enough to verifya new algorithm because simulations assume idealized and unreal-istic environments.

Acknowledgment

This work was supported jointly by Electronics and Telecom-munications Research Institute (ETRI), and the MKE (The Ministryof Knowledge Economy), Korea, under the ITRC (InformationTechnology Research Center) support program supervised by the

D.M. Kim et al. / Computer Communications 34 (2011) 2174–2183 2183

NIPA (National IT Industry Promotion Agency) (NIPA-2010-(C1090-1011-0006)).

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