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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/TMC.2014.2320255, IEEE Transactions on Mobile Computing

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A P2P-based Market-guided Distributed Routing Mechanism forHigh-Throughput Hybrid Wireless Networks

Haiying Shen*, Senior Member, IEEE , Ze Li, Student Member, IEEE , Lei Yu

Abstract—In a hybrid wireless network that combines a mobile ad-hoc network and an infrastructure network, efficient and reliable datarouting is important for high throughput. Existing routing schemes that simply combine ad-hoc and infrastructure routings inherit thedrawbacks of ad-hoc routing including congestion and high overhead for route discovery and maintenance. Although current reputationsystems help increase routing reliability, they rely on local information exchanges between nodes to evaluate node reputations, so theyare not sufficiently effective and efficient. A challenge here is if we can coordinately develop an efficient routing algorithm and effectivecooperation incentives for reliable routing. To handle this challenge, this paper presents a peer-to-peer (P2P)-based Market-guidedDistributed Routing mechanism (MDR). MDR takes advantage of widespread base stations to coordinately realize highly efficientdata routing, and effective reputation management and trading market management for reliable data routing. The packets from asource node are distributively transmitted to base stations directly or indirectly, and then they are transmitted to the destination. Thebase stations form a P2P structure for reputation collection and querying to avoid local information exchanges, and for managingthe service transactions between nodes in the trading market. By leveraging the single-relay transmission feature, base stations canmonitor the actual transmitted packets of relay nodes to more accurately and efficiently evaluate their reputations and execute tradingmarket management, as well as detect falsely reported reputation information. We further propose market-based policies to strengthencooperation incentives. Simulation results show that MDR outperforms the traditional hybrid routing schemes and reputation systemsin achieving high throughput.

Index Terms—Hybrid wireless networks, Routing, Reputation systems, Trading market model

F

1 INTRODUCTIONA hybrid wireless network is a combination of a mobilead-hoc network (MANET) and an infrastructure net-work. In such a network, base stations (BSes) in theinfrastructure act as relays for mobile nodes (MNs) in theMANET for long distance communications and Internetaccess, while MANET extends the coverage of the infras-tructure network [1]. Examples of promising applicationsfor hybrid networks include mobile file/video sharingnetworks and vehicular networks [2].

Equipped with both a high-power 3G interface anda low-power WiFi interface, current smartphones (e.g.,iPhone and Android phones) are capable of seamlesslyswitching between MANET and 3G cellular network.Mobile devices are quickly growing in their capabilities,and their growth rate seems to be outpaced by theneeds of sophisticated (e.g., multimedia) applicationswith requirements of a high throughput capacity. It wasreported recently that the mobile data traffic grows atan annual rate of 40% between 2009 and 2014 and isexpected to reach 40 billion gigabytes by 2014. To achievehigh data throughput and support bandwidth-intensiveapplications, an efficient and reliable routing scheme isincreasingly needed.

Most of the routing schemes used in hybridnetworks [3]–[13] simply combine existing routingschemes in MANETs and infrastructure networks. Amessage is forwarded in MANET through WiFi linksto a node closer to a BS, and then it is forwarded tothe BS, and then to the BS where the destination MNresides based on routing algorithm in cellular networks.Finally, it is forwarded to the destination node. Suchrouting inherits the problems in ad-hoc routing, such ascongestion and high overhead for route discovery andmaintenance [1], which prevents hybrid networks from

• * Corresponding Author. Email: [email protected]; Phone: (864) 6565931; Fax: (864) 656 5910.

• The authors are with the Department of Electrical and Computer Engi-neering, Clemson University, Clemson, SC, 29634.E-mail: {shenh, zel, leiy}@clemson.edu

Controlled Application Interface

Cellular phones

Environmental monitoring

Video/audio streaming

Locality-aware P2P-based infrastructure (LP2P)

Hybrid Wireless NetworkMANET Infrastructure network

Trading Market Model (TMM)

Distributed Routing Algorithm (DRA)

Efficient and Accurate Reputation Mgt (EARM)

Applications

Fig. 1: A high-level view of the MDR mechanism.

achieving a high throughput. Although BSes are widelyspread over a hybrid network, most previously proposedhigh-throughput routing algorithms are mainly focusedon the routing in one single BS and fail to takeadvantage of the dispersed BSes for higher efficiency.

Reliable routing is faced with a severe challenge posedby selfish nodes that tend not to forward data in orderto save resources of their own. To avoid selfish nodes, arouting algorithm can choose high-reputed nodes as re-lay nodes by using reputation systems [14]–[24]. In mostcurrent reputation systems, each node locally evaluatesother nodes’ reputation values based on reputation in-formation exchanged between neighbors. This frequentinformation exchange generates high overhead and rep-utation evaluation based on local partial information(i.e., partial forwarding activities of a node) may result inan insufficiently accurate reputation value. Calculating anode’s reputation based on all reputation information onthis node (i.e., all forwarding activities of this node) canmore accurately reflect the node’s cooperative behavior.Furthermore, the reputation systems cannot avoid falselyreported reputation information and cannot effectivelyprovide incentives for cooperation.

To increase the throughput of hybrid networksthrough highly efficient and reliable routing, a challengehere is if we can take advantage of the widespread BSesto coordinately develop an efficient routing algorithm



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and effective cooperation incentives for reliable routing;the routing algorithm facilitates the implementation ofthe cooperation incentives to overcome aforementioneddrawbacks. To handle this challenge, we propose a peer-to-peer (P2P)-based Market-guided Distributed Routingmechanism (MDR). MDR takes advantage of widely-scattered BSes to facilitate highly efficient single-relaydistributed data routing, in which the segments of amessage are transmitted directly or indirectly to BSesin a distributed manner through multiple relay nodes.The BSes form a P2P structure for reputation collectionand querying to avoid local information exchanges, andfor managing the service transactions between nodes inthe trading market. By leveraging the single-relay trans-mission feature, BSes can monitor the actual transmittedpackets of relay nodes to evaluate their reputation andexecute trading market management, as well as detectfalsely reported reputation information. Thus, a node’sreputation is based on i) its actual relaying behavior, andii) all rather than partial reputation information, whichcan calculate more accurate reputation. Specifically, MDRconsists of four components: a locality-aware P2P-basedinfrastructure (LP2P), a distributed routing algorithm(DRA), an efficient and accurate reputation managementsystem (EARM), and a trading market model (TMM).Figure 1 shows a high-level architecture of MDR. LP2Psupports the efficient operations of EARM, TMM andDRA. EARM and TMM provide cooperation incentivesand hence enhance routing reliability in DRA.(1) LP2P. LP2P is an auxiliary component for other com-

ponents. By leveraging the widely-scattered BSes,we construct a locality-aware structured P2P on theinfrastructure component of a hybrid network tosupport efficient operations for other components.

(2) DRA. DRA divides a source stream into segmentsusing erasure coding [25] and distributively trans-mits them to selected reliable neighbors through twohops to BSes. The single-relay transmission featureenables BSes to easily monitor the routing behaviorsof nodes for the EARM and TMM management,which in turn provide cooperation incentives forreliable routing. LP2P helps to efficiently marshalsegments to the mobile destination.

(3) EARM. Instead of using local information exchange,EARM relies on LP2P to collect all rather thanpartial reputation reports on a node for moreaccurate reputation evaluation and efficientreputation querying. Moreover, rather than relyingon the reports from other nodes, EARM calculatesreputation of a node based on the number ofits actual relayed messages which avoids falselyreported information by taking advantage of thesingle-relay feature of DRA.

(4) TMM. In TMM, a node pays credits to a relaynode for forwarding service and earns credits byforwarding others’ messages. Also, nodes adjusttheir routing service price to adaptively controltheir loads. Two market-based policies are furtherproposed to strengthen cooperation incentives.TMM fosters the effectiveness in deterring selfishbehaviors and providing cooperation incentives.

As far as we know, MDR is the first work that takesadvantage of widespread BSes to coordinately realizeefficient distributed routing, reputation managementand trading market management to enhance routingefficiency and reliability. The rest of this paper isorganized as follows. Section 2 details the MDRmechanism with descriptions of the different MDR

components. Section 3 briefly discusses our strategies tohandle node misbehaviors that exploit the vulnerabilitiesof MDR to gain unfair benefits. Section 5 shows theperformance of MDR in experiments. Section 6 presentsa review of related work. Finally, Section 7 concludesthe paper with remarks on our plans for future work.

2 MDR: P2P-BASED MARKET-GUIDED DIS-TRIBUTED ROUTING MECHANISMIn this section, we introduce the four components ofMDR, respectively.2.1 Locality-aware P2P-based Infrastructure (LP2P)As shown in Figure 2, MDR builds LP2P for the sub-strate of the infrastructure component of a hybrid net-work. The overlay network provides two main functionsInsert(ID,object) and Lookup(ID) to store an ob-ject to a node responsible for the ID of the object, and toretrieve the object based on its ID, respectively. In LP2P,the logical proximity abstraction derived from the over-lay network matches the physical proximity informationin reality, which enables BSes to communicate with theirphysically closest nodes for high efficiency.

Specifically, we use the landmark method describedin [26] to build LP2P. The landmark clustering techniqueis based on the intuition that nodes located close toeach other are likely to have similar distances to a fewselected landmark nodes. Given m̃ landmark BSes thatare randomly scattered in the network, each BS measuresits physical distances to landmarks and uses the vector ofdistances < d1, d2, . . . , dm̃ > as its coordinate. Two physi-cally close BSes have similar landmark vectors. A Hilbertspace-filling curve [27] is a technique for dimensionreduction of vectors while still preserving the relativedistances among points in a multi-dimensional space.We then use the technique to map landmark vectorsto real numbers, called Hilbert numbers. The closenessof the BSes’ Hilbert numbers represents the physicalcloseness of the BSes on the network. Then, we directlyuse a BS’s Hilbert number as its ID for constructingstructure P2P infrastructure and P2P routing. Based onthe Insert(ID,object) and Lookup(ID) functionsprovided by the structured P2P, we can efficiently oper-ate the information stored in the distributed BSes.

2.2 Distributed Single-Relay Routing Algorithm(DRA)As shown in Figure 2, to send a message D from thesource node to the destination, DRA is comprised of fivesteps as follows:(1) The source node first uses the erasure coding tech-nique to encode the message D into D1 to Dnr codedsegment.(2) The source node sends these segments to differentcapable neighbors selected in a distributed manner. Todo that, the source node first broadcasts a request withthe segment length. Its neighbors with sufficient capacityfor the forwarding reply to the source node. The sourcenode relies on EARM and TMM for reliable and cooper-ative relay node selections (details will be presented inSections 2.3 and 2.4). It then sends its segments to theselected relay nodes.(3) The relay nodes carry the segments and send thesegments to BSes when they enter their coverage areas.(4) The BSes then forward the segments to the BS wherethe destination resides [28]. To locate the destination BS,DTR takes advantage of LP2P for destination tracking.Each MN has a P2P ID which is the consistent hash value



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Relay nodeMoving pathBackboneTransmission

D Data Base stationDest. node Source node

B

D3

Si

D1

D1

D1

D1

D1D1

D2

D1

D2

D2

D2

D2

D2

D3

D3

D3D3

D4

D(m-1)

D4

D4

D2

Dnr

D(nr-1)

Dnr

Dnr

Dm

D(nr-1)

B3

B4

B5

Bn

B1

B2

O1

O1 Object

N1

Fig. 2: MDR in a hybrid wireless network.

of its IP address. It has an owner BS which is the ownerof its ID in the P2P. Each MN’s location is maintainedin its owner BS. Basically, every time a MN ni moves toanother cell, the BS in the cell, denoted by Bi, reports toni’s owner BS by the P2P function Insert(IDni

, Bi).The destination BS Bi can be obtained by asking ni’sowner BS with Lookup(IDni

).(5) The BS where the destination resides forwards thesegments of a message to the destination node, and thedestination reassembles the message.

In DRA, only one single relay is used to forward asegment between the source node and BS. Such routinghelps to generate a higher throughput with two-hopshort path length and adaptive selection of relay nodesbased on EARM and TMM. It also avoids dynamicallymaintaining the paths among mobile nodes. On the otherhand, due to the single relay routing, the transmissionbehavior of relay nodes can be easily monitored by theBSes, which ensures efficient and accurate reputationevaluation.

Erasure coding based segmentation strengthensDRA’s tolerance ability to forwarding failure and delayin the distributed packet trading process. The erasurecoding technique breaks a message D of length |D| intom segments and recodes them into nr coded segments.The length of each segment is |D|m and m coded segmentsare sufficient for reconstructing the original message.Thus, only 1

r of the nr coded segments are required toreconstruct the message, where r = nr

m is a replicationfactor. DRA can tolerate (1− 1

r ) forwarding failures basedon the feature of erasure coding. That is, even if nr −msegments cannot be forwarded to their destination intime, DRA is still able to reconstruct the original data.

2.3 Efficient and Accurate Reputation Management(EARM)A challenge in reliable data routing is how to avoidselfish nodes. EARM helps achieve this objective whileoffering incentives for node cooperation in routing. Com-pared to traditional reputation systems, EARM has thefollowing advantages: (1) Rather than depending onfrequent local information exchange among neighbors,which does not guarantee the accuracy of reputation val-ues due to partial reputation information for reputationcalculation and incurs a high overhead, EARM relies onLP2P to efficiently collect all reputation information oneach node that helps calculate more accurate reputationvalues; (2) Taking advantage of the single-relay feature ofDRA, EARM calculates a node’s reputation value basedon its actual number of forwarded bytes rather thanother nodes’ feedback, which may be falsely reported

by misbehaving nodes; and (3) Relying on LP2P, EARMoffers efficient global reputation querying.

Reputation value calculation. As the BSes aremaintained by authorized telecommunication companiesor government which are generally trustworthy, we useBSes to serve as authorities to supervise the transactionsbetween source nodes and relay nodes in order toimprove the throughput of hybrid wireless networks.In EARM, the reputation is measured by a valuebetween 0 and 1. Every node is initially considered tobe untrustworthy with zero initial reputation value. ABS increases the reputation value of a relay node whenreceiving a forwarding segment from the node. That is,R ← min{R + β · l, 1}, where R is a node’s reputationvalue (0 ≤ R ≤ 1), β denotes a constant and l denotesthe length of the received segment. In order to reflectthe recent behaviors of nodes, like current reputationsystems, BSes periodically decrease the reputationvalues of their nodes by R = γR, where γ (γ < 1) is adiscount factor for a node’s past behaviors.

In order to wisely use channel resources to enhancea system’s throughput while rewarding relay nodes, wefurther propose reputation-based bandwidth allocation.Specifically, a BS assigns more bandwidth to higher-reputed MNs by BW = BWmin + ηR, where BW de-notes the assigned bandwidth, BWmin is the minimumbandwidth which every node can get in the system, andη = BWmax − BWmin such that the nodes with thehighest reputation R = 1 can get maximum bandwidthBWmax allowed by the system . This algorithm providesincentives for node cooperation in data forwarding whileimproves system throughput.

Reputation value collection and querying. A BScalculates the local reputation value of node ni in itsown range based on the number of bytes ni forwards toit, and periodically reports the value to LP2P by usingInsert(IDi,Ri). Based on the P2P object assignmentpolicy, ni’s local reputation values are then collected inits owner BS. The owner BS calculates the average ofthe local reputation values of ni as ni’s global reputationvalue, and stores it locally.

When node nj queries for node ni’s reputation value,it asks its closest BS. If the BS is not the owner of ni, it ex-ecutes Lookup(IDi). Using the P2P routing algorithm,the request will be forwarded to node ni’s owner BS thathas node ni’s global reputation value. Since the queriesfor reputation are always for the MNs in a BS’s coveragearea, the BS can cache queried node reputation values forsubsequent querying from its MNs in order to reducethe query delay. The P2P-based reputation system offersefficient global reputation information collection andquerying. Also, calculating a node’s global reputationbased on all of its actual forwarding activities enhancesreputation evaluation accuracy.

2.4 Trading Market Model (TMM)2.4.1 Basic Trading Market ModelTMM manages data transmission operations betweensource nodes and relay nodes for reliable and efficien-t data transmission. Each node is assigned a certainamount of credits initially when it joins the system.Source nodes pay credits to relay nodes and relay nodescharge source nodes for data forwarding services. Sincethe data forwarding cost is directly related to the datalength, TMM uses the product of the data length andunit service price per byte to determine the forwardingservice price. Each node determines its service price



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Price of packet service

Quantity of packet service

Low demand line

Supply line

Old equilibrium price

New equilibrium price

High demand line

Fig. 3: Equilibrium price in the market.



Demand line

Supply line

Equilibrium price

Quantity of demand

Quantity of supply

cp

Fig. 4: Pricing ceiling in the market.



Price that buyers pay

Demand line

Supply line 1

Supply line 2

Price that sellers receive

Price without tax

Quantity of supply without tax

Quantity of supply with tax

Fig. 5: Equilibrium price with taxing.

based on the supply and demand equilibrium. Specifical-ly, a node considers two factors: its QoS and the businesscompetition between nodes. For the former, higher-QoSnodes tend to claim higher prices and vice versa. Forthe latter, in order to attract more business to earn morecredits and higher reputations, nodes should offer lowerprice and vice versa.

These two factors can be reflected by the queuinglength in a node’s queue. Long queuing length of abenign node means it may not have additional capacityfor offering high QoS to more requests and has alreadyreceived many requests for accumulating its reputation.In this case, the node can increase its service priceto avoid being overloaded and offering low QoS. Incontrast, short queuing length of a benign node meansit has sufficient capacity to offer a high QoS and receivemore requests to increase its reputation. In this case,the node can decrease its price to attract more requests.Accordingly, we propose a price determination functionby leveraging the polynomial price function in the e-conomics area [29] that keeps the supply and demandequilibrium. We use P to denote a node’s forwardingservice price per byte. Then,

P = p̄+

δ∑i=1

α ·(lqLq

)i, (1)

where lq and Lq are the lengths of the occupied part andentire part of its queue respectively, p̄ is the base price,and α and δ are the scaling parameters for the price. Thisprice determination function enables nodes to adjusttheir routing service price to adaptively control theirloads. Formula (1) indicates that if a node receives fewrequests (i.e., has a short queuing length), it decreasesits price to attract more requests. On the other hand, if anode receives a large amount of forwarding requests (i.e.,has a long queuing length), it raises its price to avoidbeing overloaded.

As described in Section 2.2, the source needs to select arelay node for each segment. With neighbors’ reputationsreturned by EAR, the source node considers the higher-reputed nodes with higher priority because such nodeshelp to achieve higher routing reliability. The sourcenode then chooses the neighbors whose service chargesare affordable. For two neighbors with the same repu-tation, the source selects the one with the lower price.Therefore, the amount of credits owned by the sourceCa should satisfy Ca ≥

∑nr

i=1 Pi · li, where Pi and li arethe service price of selected neighbors and data lengthfor the ith segment, respectively. Each node ni’s ownerBS manages ni’s credit account and conducts credittransfer for ni’s forwarding transactions. A source nodeni pays its relay nj by notifying ni’s owner BS alongwith the transmitted packet size and price. ni’s ownerBS transfers credits to nj ’s owner BS after they confirm

the correctness of the information from the source node.We will introduce the information correctness validationin Section 3.

If the source node cannot afford the service chargeof any relay node, it needs to earn more credits byforwarding data for others. Thus, TMM not only servesas an effective means to provide cooperation incentives,but also deters the behaviors of uncooperative nodesby starving their credits. Our previous game theoreticalanalysis work [30] confirms the effectiveness of the nodecooperation incentives in price-based systems. TMM alsobalances node load by enabling nodes to automaticallyadjust their service price based on the supply and de-mand equilibrium.

2.4.2 Market-based PoliciesIn the economic market, the supply and demand deter-mine the price for the packet forwarding service (servicein short). Figure 3 shows the relationship between themarket equilibrium price and the demand/supply in themarket according to the economic theory [31]. The supplyline illustrates the relationship between the service priceand the quantity of the service supplied in the market.The quantity of the service in supply increases as theservice price increases. That is, if the service price ishigh, more nodes are encouraged to provide services,which leads to service quantity increase in the market.The demand line illustrates the relationship between theservice price and the quantity of services in demand.We see that the quantity of service in demand increasesas the price decreases in the market. The supply lineshifts up when the quantity of the service the producercan provide at every price decreases. The demand lineshifts up when the consumer increases the quantitydemanded at every price, referred to as an increase indemand. The crossing point of the supply line and thedemand line is the equilibrium price that balances thedemand and supply in the market. The figure indicatesthat an increase in demand leads to a higher marketequilibrium price.

Based on the previous basic trading market model, ifwe completely let the nodes autonomously determinethe price in the market, a low-traffic region generateslow equilibrium price and a high-traffic region generateshigh equilibrium price. This is because the quantity ofpacket service in demand in low-traffic region is lowerthan that in the high-traffic region. Given a supply line, alower demand incurs a lower market equilibrium price,as shown in Figure 3. As a result, cooperative nodesin a low-traffic region cannot earn many credits sincethey cannot sell their services at a specified high price,and thus cannot buy services. In contrast, uncooperativenodes in a high-traffic region can still sell service at ahigh price because the service demand in this region ishigh. Such a result may discourage cooperative nodes



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to continuously be cooperative in low-traffic regions andfail to punish wealthy and uncooperative nodes in high-traffic regions. To deal with this problem, we proposetwo policies to control the price to provide more effectivecooperation incentives.

Policy 1: Set a price ceiling in the market and givehigher-reputed nodes higher priority to buy services.The price ceiling is an offered price threshold that everynode cannot exceed, and it should be lower than themarket equilibrium price. We first analyze the effect ofprice ceiling on the market equilibrium price. Figure 4shows how a price ceiling affects the demand and supplyin the market. We see that the quantity of supply is lessthan the quantity of demand after setting a price ceiling.This leads to a shortage of the supplies, especially thehigh-QoS supplies. With Policy 1, suppliers offer theirlimited services to the nodes with higher reputation.Uncooperative nodes in high-traffic regions, even thosewith many credits, would have few chances to receiveservices, thus getting punished. Also, as the price ofhigh-QoS service is bounded by the ceiling price that islower than the equilibrium price, cooperative nodes ina low-traffic region can have more chances to sell theirservices and also purchase high-QoS services. Policy 1thus provides high incentives for uncooperative nodesto be cooperative and creates more chances for high-reputed nodes to purchase high-QoS services in bothlow-traffic and high-traffic regions. We cannot set theceiling price too low as the supply shortage would crashthe market and lead to a low system throughput. Thus,we propose Policy 2 below to further ensure that thecooperative nodes in low-traffic regions can always buyhigh-QoS services.

Policy 2: Levy tax on nodes based on their revenuesand subsidize cooperative nodes. Since the transactionsof nodes are monitored by their owner BSes, the ownerBSes can tax sellers on their transactions by directlyreducing credits from their accounts. Figure 5 illustrateshow taxing affects the equilibrium price in the market.We see that when tax is levied on sellers, there is ashift from supply line 1 to supply line 2 because thesellers must increase their service price in order to offsetthe tax on the goods. This supply line shift leads to anincrease of the equilibrium price. Thus, uncooperativenodes’ chances to sell their services are reduced and thequality and quantity of services they are able to buy arereduced. These levied tax credits can be subsidized to thecooperative nodes with low revenues in the low-trafficregions. The subsidy can help these cooperative nodesbuy more high-QoS services.

3 MISBEHAVIOR PREVENTIONIn this section, we briefly discuss our strategies to handlenode misbehaviors that exploit the vulnerabilities ofMDR to gain unfair benefits.Misbehavior 1: Packet forging and modification. Sincea node’s reputation R is determined by the size andthe number of its forwarded packets, a selfish relaynode may send bogus packets or insert junk data intothe packet to earn higher R. To prevent these attacks,we use symmetric key to ensure the authenticity andintegrity of the packets, considering that the public keyauthentication [32] consumes immense energy of the n-odes and leads to long transmission delay [33]. Basically,every node shares a symmetric key with all the BSesand sends messages with Message Authentication Code(MAC) computed by this key. Then, once receiving apacket from a relay node, the BS verifies the authenticity

and integrity of the packet by recomputing MAC withthe key of the source node.

An important issue for the symmetric key approach isthe management of the symmetric keys shared betweennodes and the BSes. To ease the key management andupdate, we use one-way-hash chain [34] to generate thesymmetric keys. When a node n joins in the network,it generates a one-way-hash chain via a globally knowncryptographically secure hash function h(∗) as follows:HL

h←− HL−1h←− . . .

h←− Hih←− Hi−1

h←− . . .h←− H0 = h(rn) (2)

where Hi = h(Hi−1), rn is a random generated key bynode n and L is the length of the hash chain. Then, thenode signs rn by its private key and encrypts rn with thepublic key of the BS which rn is sent to. This BS thensends rn to all other BSes. Here we assume all BSes aresecure since BSes are usually maintained by authoritiesand it has a high capacity (e.g., computing and network-ing capacities) to ensure security [35]. Node n uses eachhash value in the order from HL to H0 along the chain asthe key for a period. When the period expires, n choosesthe next hash value as the key. If the hash values in thehash chain are used up, a node re-generates a new hashchain with a new random generated key rn and securelysends it to the BS. When node n needs to send messageP , it uses current key Hi to compute MAC for P alongwith a nonce value Nonce as MAC(Hi, P |Nonce), andthen sends packet {P,Nonce, i,MAC(Hi, P |Nonce)} tothe relay node. When receiving a packet, the BS dropsthe packet if the nonce in it is repeated with old packets;if not, it then computes Hi = hi+1(rn) and re-computesthe MAC of Pj to check whether it is consistent withthe MAC in the packet. The packet is considered to beforged if MACs are not consistent.

With one-way-hash chain, each BS only needs to main-tain a single initial random key rn for each node n sincethe used key can be computed on-the-fly with key index.The cost for the key updating is small, since the next keyindex is used as key updating and only when the chainis used up, public-key based encryption and signatureare conducted for securely transmitting initial randomkey. For the security, even if the previous hash valuesused for authentication (i.e., Hj (j > i)) are exposed,they still cannot derive Hi due to the one-way propertyof cryptographically secure hash functions. The securityproperty of one-way-hash chain has been exploited inprevious works [36], [37] to provide authentication ofrouting requests or transaction requests for credit-basedcooperation in MANET. The hash values are disclosedin order such that the hash value Hi−1 can be verifiedby checking Hi = h(Hi−1) where Hi has been disclosedbefore. In contrast, we use one-way-hash chain to reducethe overhead of key management.

Nonce value helps to prevent the relay nodes fromsending a message multiple times to the same BS, but itcannot detect old messages if a relay node can moveto the cell of another BS to replay messages. Thus,we propose a log comparing strategy that comparesthe packet transmission activities observed by BSes andreported by the sources. A BS maintains a log recordingthe forwarding activities (packet size, nonce, time andsource) of relay nodes. After each authentication on apacket forwarded by a relay node, the BS updates itslog. Each BS reports the log of each relay node ni toni’s owner BS periodically by executing Insert(IDi,AIDi

), where IDi is the ID of ni and AIDidenotes

the log information of ni. A source node also keepsa log of its own packets that have been sent out byeach relay node. Once it is in the range of a BS, it



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sends its log to the BS. The BS executes Insert(IDi,AIDi). Consequently, the logs of source nodes and ofBSes about a specific relay’s activities will be gatheredand compared in the relay node’s owner BS. If the ownerBS observes that there is an inconsistency between thetwo logs, it is likely that ni replayed the packet. Then,ni’s reputation is decreased.Misbehavior 2: False transaction reporting. Since asource node needs to pay for the packet forwardingservice, it may report a smaller number of packets toits owner BS than the actual number in order to payless. The source’s owner BS and relay’s owner BS serveas the proxies for the relay service payment. The relay’sowner BS checks the correctness of the source’s reportedpacket size before payment. Recall that the relay’s ownerBS receives logs from other BSes that receive the packetsfrom the relay. In the relayed packets, MACs can serve asa signature of the source node, which prevents the sourcenode from falsely reporting the number of packets it hassent. If the relay’s owner BS detects that the number ofpackets reported by source node n is less than the actualnumber of packets sent by n reported by other BSes, itnotifies the source’s owner BS, which will reduce thereputation of n.Misbehavior 3: Colluding. In a collusion, colluders re-port high reputation values for each other in order toboost their reputations. In TMM, since R is calculatedbased on actual forwarding activities recorded by BSesrather than reported reputation by nodes, such collusionmisbehavior can essentially be avoided. Two nodes canboost their reputations by repeatedly forwarding packetsfor each other and paying credits back and forth. Inthis case, these nodes truly consume resources to relaypackets though the packets may not be useful. Here,the colluders consume the same amount of resourcesto gain the same reputation and credits as other coop-erative nodes. Since there is no extra benefit from thiscollusion, the colluders do not have incentives for suchbehaviors and still follow the MDR policy. In a Sybilattack, an attacker creates a large number of pseudony-mous identities to boost its own reputation. Similarly, inorder to earn reputation and credits, a node must trulyrelay packets without extra benefits, which provides noincentives to launch Sybil attacks.Misbehavior 4: Packet dropping. Since the reputationis calculated based on the number of segments received,for the nodes that do not want a high reputation, theycan drop the packets on purpose to maintain their repu-tation value to a certain degree. Previous price systemscannot monitor the packet dropping behavior. In MDR,as explained previously, based on the sequence of thehash value in hash chain, BSes can censor the droppingbehaviors of the mobile nodes by counting the numberof used hash values in the hash chain. The log comparingstrategy can also detect the packet dropping behaviorsby comparing the logs reported by source nodes (thatsend packets to a relay node) and by BSes (that actuallyreceive packets from the relay node).4 PERFORMANCE ANALYSISIn this section, we analyze the performance of MDR. Westudy the effect of DRA and TMM on the improvementof system throughput, respectively. We assume that thenodes are independent and identically distributed (i.i.d.)in the system.4.1 Cost AnalysisCommunication Cost. In MDR, BSes communicate witheach other through wired WLAN, so the transmission

cost among BSes is negligible. We consider the commu-nication cost of mobile nodes. Considering that the com-munication cost via WiFi interface is lower than that via3G interface, we weight the message complexity throughWiFi and 3G with c1 and c2, respectively. Suppose themaximum number of nodes in a node’s neighborhoodis ∆. To send a message, the source first sends a for-warding request to its neighbors and receives replies,which incurs O(c1∆) message complexity. The sourcethen queries the reputations of its neighbors through 3Gto the closest BS, which incurs O(c2∆) message com-plexity. Thus, the cost of control messages for a messagetransmission is O((c1 + c2)∆). This control overhead isamortized over all messages of data stream sent fromthe source node to the destination. Each message D isencoded into nr segments and sent to different relaynodes, resulting in O(c1nr) messages. Through single-relay transmission, these segments are sent to the BSesand then forwarded by BS to the destination via 3G,with O(c2nr) messages. Thus, the transmission cost fora message is O((c1 + c2)nr).

Suppose there are Ns sources in the network, eachhaving Mi (i = 1, 2, ..., Ns) messages to send to adestination. The control overhead is O((c1 + c2)∆Ns).The total cost including the control coverhead is O((c1 +c2)(∆Ns+nr

∑i=1NsMi)). Then, the percentage of con-

trol overhead is ∆∆+nrM

, where M is the average numberof messages from every source. Thus, we can see that thecontrol overhead in MDR becomes very low when M islarge enough, which is also verified in the simulationevaluation shown in Figure 6(d).Computation Cost. The cost of the cryptographic op-erations at mobile nodes is small. The one-way hashfunction SHA-1 computation cost of one-way-hash chainis amortized over the whole duration of one-way-hashchain usage. The only expensive computation cost isthe public key based signature and encryption for theinitial random key in the hash chain, which, however,occurs only when the current hash chain is used up.Suppose that the length of symmetric keys is 160 bitsand SHA-1’s energy cost is 5.9 µJ/bytes [38]. We useSHA-1 as one-way hash function. Then, generating thehash chain of length 50 needs 5.9 mJ. An RSA public-keybased operation needs about 300 mJ [38]. Then, it takesabout 300 mJ to start to use another hash chain of length50, and the cost of public-key based operations is high.However, considering that the hash chain is sufficientlylong and each hash value is used as a key for a timeperiod, the cost of public-key operation amortized toevery symmetric key can be very small, which is about300/50=6 mJ in our example.

4.2 Routing Performance AnalysisThe benefit of using a single relay node on the routingpath from a source to a BS enables efficient and moreaccurate reputation management and also avoids dy-namically maintaining the paths among mobile nodes.One side-effect is that the throughput highly dependson the delay for the relay node to meet any BS. DRAreduces this delay by using the erasure coding technique,in which a message is coded into multiple segments intransmission to increase the probability of segments be-ing delivered quickly, and reduce the delay and improvethe throughput.

Proposition 4.1: The expected transmission delay of amessage in the erasure coding-based DRA is

E(Tr) = t̄ ·m∑i=1

1

nr − i + 1, (3)



7

where Tr is the transmission delay of a message from thesource to the destination in DRA and t̄ is the expectedtransmission delay of a single segment.

Proof Suppose that the arrival of segments follows thePoisson process, i.e., the arrival interval of each segmentis independent and exponentially distributed in DRA.Therefore, among m′ segments, the probability that theith segment’s transmission delay (Ti) is longer than t is

Pr(Ti > t) = e−t/t̄ (1 ≤ i ≤ m′).

We use Ti to denote the delay of the ith arrivingsegment. We use Tmin = min(T1, T2, ...Tm′) to denote thetransmission delay of the shortest-delay segment. Thus,the cumulative distribution function for Tmin with delayless than t is

Pr(Tmin ≤ t)

= 1− Pr(min(T1, T2, ..., Tm′) > t)

= 1−Πm′i=1Pr(Ti > t) = 1− e−m′·t/t̄.

Then, the expected delay for the shortest-delay segmentis

E(Tmin) =t̄

m′.

The delay of the ith arriving segment in nr segmentsequals the minimum delay of the segment in the remain-ing (nr − i+ 1) segments. Then

E(Ti) =t̄

nr − i + 1. (4)

The expected transmission delay of the first m arrivingsegments using erasure coding is

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m∑i=0

1

nr − i + 1.

From Proposition 4.1, we can see that a larger numberof coded segments for a message transmission lead toa decreased expected transmission delay, as long as thenetwork is not overloaded by the increased traffic. Thereduced transmission delay may not lead to higher net-work throughput, because it is at cost of increased traffic.Thus, we further consider the relationship between thenetwork throughput and the traffic intensity.

Proposition 4.2: The throughput of a hybrid networkequals to min(Ta, Ta(1 − AN/N !∑N

i=0(Ai/i!)))(i ∈ {0, 2, ..., N}),

where A is the traffic intensity, N is the number of chan-nels in the infrastructure network, Ta is the throughputof the MANET component.

Proof For a pair of S-D nodes, the traffic from a source n-ode in the MANET goes through infrastructure networkto the destination nodes. Blocking rate in the infrastruc-ture network refers to the failure probability of a ran-dom channel access. It is approximately AN/N !∑N

i=0(Ai/i!)[39].

Then, the throughput of the infrastructure network isTa(1 − AN/N !∑N

i=0(Ai/i!)). Therefore, the throughput of the

whole network is min(Ta, Ta · (1− AN/N !∑Ni=0(Ai/i!)

)).

Proposition 4.3: DRA using the erasure coding tech-nique produces higher reliability and efficiency thanDRA without using the technique.

Proof Since the MNs in the system are i.i.d., the prob-ability that the transmission time τ of each segment isless than a certain time t conforms to the exponentialdistribution [40]. That is, F (t) = P (τ < t) = 1 − e−t/t̄,where t̄ is the average transmission time. nr denotes thenumber of coded segments in the erasure coding datasegmentation, and m coded segments are sufficient for

original data reconstruction. Thus, the probability thatm segments among the nr segment can be forwarded tothe destination within time t is

P (x ≥ m) =

nr∑x=m

CxnrF (t)

x(1− F (t))

nr−x.

In the case that a message is divided into nr segmentswithout erasure coding, the probability that nr non-coded segments are transmitted to a BS within time t isF (t)nr . Because

∑nr

x=m CxnrF (t)

x(1− F (t))

nr−x > F (t)nr ,segmentation using erasure coding technique leads tohigher transmission reliability and efficiency than seg-mentation without using the technique.

5 PERFORMANCE EVALUATIONThis section demonstrates the distinguishing propertiesof MDR through simulations on NS-2 [41]. We usedthe Distributed Coordination Function (DCF) of IEEE802.11 as the MAC layer protocol, two-way propagationmodel in the physical layer, and the constant bit ratetraffic model for all connections. The default settings arepresented below unless otherwise indicated. We set thetotal number of MNs and BSes to 50 and 5, respectively,and set their transmission range to 250m and 500m,respectively. The BSes were uniformly distributed in an1000 × 1000 square area. To simulate the node mobility,we used the Random Way Point model [42], in whicheach MN randomly selects and moves toward a desti-nation point with a speed randomly selected from [1-20]m/s. The bandwidth of each node was set to 54M-bit/s. We randomly chose one source-destination pairevery 10 seconds to transmit data for 50 seconds. Thedata generating and forwarding rate was set to 1Mbit/s.We randomly assigned a reputation value R ∈ [0, 1] toeach node. We set the percent of the selfish node to 20%that always drop their received messages. The warm uptime was set to 100s. For each experiment, we ran tentests and report the average results.

We compared MDR with a routing scheme proposedin [3], denoted by Hybrid, which directly combines thead-hoc routing with infrastructure routing. We also com-pared MDR with the Confidant [16] reputation system inMANETs, in which each node evaluates the reputationof its neighbors based on their packet forwarding and re-ceiving rates and exchanges reputation information withits neighbors. Unless otherwise specified, MDR does notinclude the market-based policies. In the experiments,the metric throughput (kbps) is used to evaluate thethroughput capacity of a routing scheme. Overhead rate isdefined as the percent of the control messages among thesuccessfully forwarded messages. Message delivery delayis the average delay of all segments of a message arrivingat the destination.

5.1 Comparison of ThroughputIn the experiment, we measured the throughputof MDR, MDR with neither EARM nor TMM(MDRw/oRep), MDR with Confidant (MDRw/Conf ),Hybrid with Confidant (Hybridw/Conf ) and Hybrid withoutConfidant (Hybridw/oConf ). Figure 6(a) demonstrates thethroughput of different methods over a time period. Wesee that the throughput of MDR remains almost constantover time and it is also much higher than Hybrid. Thisresult confirms that MDR is superior to Hybrid dueto its DRA, EARM and TMM by relying on LP2P. Wealso find that the throughput of Hybridw/Conf increasesslightly after 30 seconds. This is because as time elapses,



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some message holders are closer to the destinations andthe interference on transmissions lessens.

We also observe that MDR generates a higher through-put than MDRw/oRep. This is because in MDRw/oRep,source nodes cannot avoid selfish nodes while MDRenables source nodes to choose highly-reputed nodesto forward segments. This figure also shows that MDRleads to a higher throughput than MDRw/Conf. Thisimplies that EARM and TMM have a greater effective-ness than Confidant in guiding reliable relay selectionsand encouraging node cooperation. Unlike Confidant,which uses local feedback exchange for reputation cal-culation, EARM collects all feedback information of aMN for a global reputation calculation. Thus, it producesa more accurate reputation to reflect a node’s behav-ior. Although Hybridw/Conf can improve the systemthroughput of Hybridw/oConf due to the aid of Confidant,its throughput is still lower than that of MDRw/oRepbecause some messages were not successfully forwardeddue to broken links in multi-hop transmission. Thisresult shows the higher performance of DRA over multi-hop routing caused by its fewer routing hops and shorterpath lengths in transmission. In summary, the resultsindicate that MDR leads to a higher throughput thanprevious routing algorithms in hybrid networks and thatEARM and TMM are effective in enhancing the reliabil-ity of data routing with previous reputation systems.

5.2 Comparison of ScalabilityTo evaluate the scalability of MDR compared with Hy-brid, we measured their throughput in networks with noselfish nodes. The number of nodes in the networks wasset to 10, 30 and 50. Figure 6(b) shows that as the numberof nodes increases, the throughput of Hybrid decreaseswhile the throughput of MDR remains stable. The resultsshow that MDR has a higher scalability than Hybrid.This is due to MDR’s distinguishing features includingdistributed routing, relay node selection based on EARMand TMM, short transmission distance and path lengthby relying on LP2P. These features contribute to reliableand efficient data routing. In Hybrid, messages are routedin a multi-hop manner and are easily congested atgateway nodes due to the single routing path, whichleads to many transmission failures.

Figure 6(c) demonstrates the throughput of MDR andHybrid versus the number of source nodes when thenumber of MNs in the network is 100. We can observethat the throughput of MDR increases dramatically,but that of Hybrid only increases marginally with thegrowth in the number of source nodes. More sourcenodes generate more traffic. The gateway in Hybridcould easily become a bottleneck due to the singlerouting path, leading to high packet dropping rate.MDR outperforms Hybrid since it distributes loadsamong several nodes by sending segments to different

gateway nodes in the hybrid network. In addition, theresults demonstrate that MDR produces an increasein throughput almost linearly with the number ofsource nodes, indicating that the system’s throughput inMDR is comparatively stable. The considerably higherthroughput of MDR compared to Hybrid in high trafficillustrates the effect of the distributed routing in MDR.

Figure 6(d) shows that the overhead rate of Hybridis much higher than that of MDR. In addition, theoverhead rate of MDR remains nearly the same whereasthat of Hybrid increases sharply as the number ofsource nodes grows. Recall that MDR does not needto maintain and discover routes for transmissions. Itsnumber of control messages remains the same and itsoverhead rate is very low regardless of the transmissionload. In contrast, Hybrid is under the heavy burden ofdiscovering and maintaining routes, which generatesmany control messages. These results confirm that MDRproduces much less overhead than Hybrid.5.3 Evaluation of the Erasure Coding-based DRAIn this experiment, we compare the effectiveness oferasure coding with replication in their ability to enhancerouting reliability. With replication [43], multiple replicasof a segment are transmitted to increase the reliability.Erasure coding-based DRA needs to transmit nr codedsegments for a message D consisting of m segments.To achieve the same transmission overhead as erasurecoding-based DRA (i.e., total nr segments for a messagetransmission), replication-based DRA replicates everysegment of the m segments for nr−m

m times, and thesereplicas are distributively transmitted to the destination.Because the whole message is divided to m segments, itneeds to transmit m different segments to the destinationfor a successful message transmission. Specifically, Wedivide each message into m = 10 segments in MDRand MDRw/oRep. We use MDRw/EC-2 and MDRw/RP-2 to denote MDR using erasure coding and replicationtechniques with replication factor r=2, respectively. Thatis, MDRw/RP-2 creates a replica for each segment in msegments.

We define the success rate as the percent of the re-ceived non-duplicated segments used for message re-covery among all original segments. Figure 7(a) com-pares the success rate of different methods versus thepercent of selfish nodes in the system. The figure showsthat the success rate follows MDRw/EC-2>MDRw/RP-2>MDR>MDRw/oRep. For MDRw/EC-2, since any mreceived segments can recover the original message, itssuccess rate is the highest and it can tolerate up to50% selfish nodes in the system. For MDRw/RP-2, onlym different segments can recover the original message.Therefore, its success rate is much lower than MDRw/EC-2, especially when selfish nodes constitute a large por-tion of the network. Using the replication technique,



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MDRw/RP-2 produces a higher success rate than MDR.MDR increases the success rate of MDRw/oRep by for-warding the segments to high-reputed nodes. We can seethat the success rates of all methods drop as the numberof selfish nodes increases since more selfish nodes leadto more dropped messages. Since MDRw/oRep does nothave any mechanism to avoid transmitting segments toselfish nodes, its success rate decreases dramatically.

To evaluate the effect of the number of segment copieson the effectiveness of erasure coding and replication,we varied the value of the replication factor r andtested the success rate accordingly. Figure 7(b) showsthat MDRw/EC-2 and MDRw/EC-3 exhibit approximatelythe same performance except for when the percentage ofselfish nodes in the network reaches 60%. This meanswhen the selfish nodes occupy no more than half ofthe total nodes, r = 2 is sufficient to ensure successfultransmission. Also, higher r with more segment copieshelps enhance routing reliability. The figure also showsthat MDRw/RP leads to lower success rate than MDR-w/EC, and MDRw/RP-3 generates higher success ratethan MDRw/RP-2. This is because to successfully trans-mit a message, MDRw/RP requires the arrival of differentm segments while MDRw/EC only requires the arrivalof any m segments. This is also the reason that morereplicas in replication help achieve a higher success rate.

Figure 7(c) plots the average, maximum and minimumdelay of MDRw/EC and MDRw/RP versus the number ofcopies per segment (i.e., r) when the percentage of selfishnodes in a system is 40%. The figure shows that as rincreases, the delay of MDRw/EC decreases whereas thedelay of MDRw/RP increases. Also, MDRw/EC exhibits asmaller variance than MDRw/RP. More segments beingtransmitted in the system lead to a higher queuing delayof the messages in the nodes. Since MDRw/RP requiresthe destination to receive m different segments of amessage before recovering it, the total transmission delayis increased. MDRw/EC can recover the original messageusing the first m arriving segments, so more copies helpreduce the delay. This is also the reason why MDRw/EChas a smaller variance in delay than MDRw/RP. Fig-

ure 7(d) further shows the total number of segments inMDRw/EC-2, MDRw/EC-3, MDRw/RP-2 and MDRw/RP-3. We see that MDRw/EC-3 and MDRw/RP-3 generatemuch more traffic than MDRw/EC-2 and MDRw/RP-2.From the previous results, we know that in MDRw/EC,when the percent of selfish nodes is small, r does notneed to be set to a large value which otherwise producesmore segments and hence more forwarding overhead. InMDRw/RP, larger r leads to higher transmission successrate, but at the cost of more forwarding overhead.

5.4 Evaluation of the EARM Reputation SystemTo test the performance of EARM in preventing falsereputation reports compared to Confidant, we measuredthe throughput of MDR and MDRw/Conf with the pres-ence of false reputation reporting nodes. In this experi-ment, we randomly chose a number of nodes to act asmisbehaving nodes that always report a high reputationvalues for their low-reputed neighbors in an attempt toincrease their reputation values in reputation exchangein MDRw/Conf. Figure 8(a) shows the throughput ofMDR and MDRw/Conf with a different number of misbe-having nodes. We can see that the throughput of MDR issignificantly higher than MDRw/Conf. Confidant is unableto identify false information by relying on neighborinformation exchanges for reputation evaluation. Thus,a node’s reputation value may not be accurate enoughto reflect its behavior and the selfish nodes may beconsidered as reputed nodes for data forwarding. As aresult, MDRw/Conf leads to lower throughput. EARMcalculates a node’s reputation value based on globalinformation of its actual forwarded data length withthe aid of LP2P. Thus, EARM provides more accuratereputation evaluation for nodes. Even if relay nodessend bogus packets or insert junk data into the packetsto earn higher reputation or drop packets, the hashchain mechanism can detect such misbehavior. Further,MDR also compares the source nodes’ reported relayactivities with the BSes’ reported relay activities to avoidsuch misbehavior. In this way, MDR also can detect



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false information from source nodes that report fewerforwarded packets in order to pay less.

In this experiment, one selfish source node contin-uously sent out messages and two other high-reputedsource nodes periodically sent out messages. The selfishnode initially only had 100 credits and the high-reputednodes had sufficient credits to support their data trans-mission. Figure 8(b) shows the percent of successfullytransmitted messages of selfish nodes among all success-fully transmitted messages. We can observe that MDRleads to a lower percent rate than MDRw/oRep. Hybrid-w/oRep and Hybridw/Conf produce the highest percentrates. Since every node needs to pay credits for messageforwarding in MDR, when the selfish node’s creditsare used up, it is unable to transmit its messages. Inother methods, the selfish node’s messages constitute alarge percent of all the transmitted messages. The reasonHybrid generates a higher percent rate than MDRw/oRepis because the selfish node’s large amount of messagesare likely to congest node channels, leading to messagedrops. MDR’s distributed routing avoids generating con-gestions.

The next experiment investigates how a node’s reputa-tion level impacts the throughput in MDR. We assignedthree reputation levels, A,B and C, to a certain percent-age of nodes in the system. Nodes with A,B and C levelshave probabilities randomly chosen in [1, 0.9], [0.9, 0.8]and [0.8, 0.7] to forward a received segment, respectively.We use “A-100%” to represent the scenario that 100%nodes have reputation level A and the same is applied toother notations. Figure 8(c) illustrates the throughput ofvarious scenarios. We can see that more high-reputed n-odes lead to higher throughput. Recall that high-reputednodes can receive more bandwidth from BSes and low-reputed nodes tend to drop transmission data. Therefore,more high-reputed nodes in the system help increasethe throughput. These results imply that the throughputof a system can be enhanced by choosing relay nodeswith high reputation levels. The results also confirm theeffectiveness of assigning different bandwidth to nodesbased on their reputations in MDR.

In Confidant, neighbors exchange reputationinformation. After node ni locally updates the reputationof its neighbor nj , node ni sends the update to itsneighbors, which update node nj ’s reputation andnotify their neighbors. This process is repeated. InEARM, BSes that have received messages from node njsend its updated local reputation to node nj ’s ownerBS using Insert(IDi,Ri), which calculates node nj ’sglobal reputation. In order to evaluate the overhead ofboth methods, we varied the number of updates of eachnode during the simulation time from 10 to 50 with anincrement step of 10 and recorded the total number ofnodes that have received or forwarded all the updates.

We set the number of hops for update forwarding inConfidant to 3. Figure 8(d) shows that the total numberof nodes receiving/forwarding the updates increases asthe number of initiated updates grows. Also, the exper-imental result in Confidant is much higher than MDR.In MDR, when a BS wants to update the reputation ofanother node, it only needs to send one message whichis forwarded for logNb hops on average, where Nb isthe number of BSes. Reputation exchange in Confidantgenerates many messages. The result implies that bytaking advantage of the BSes in a hybrid network,EARM leads to a much lower overhead than Confidantin reputation information collection and update.

5.5 Evaluation of the Basic TMM Trading MarketModelIn Formula (1) for the price determination, we set δ = 4,α = 0.5 and p̄ = 1. We randomly selected theseparameter values within reasonable value ranges. Weassigned each node 500 credits initially and chose 7source nodes every second for message transmission. Weset the transmission rate of nodes to 0.2Mbps. Figure 9(a)shows the price of three nodes randomly selected fromnode groups with R=1, 0.6, and 0.2, respectively. Wecan see that for the node with R = 1, its price isquickly raised to 3. This is because the node’s highreputation attracts many service requests when its priceis affordable. According to Formula (1), a longer queuinglength leads to a higher price. Then, its price fluctuatesaround 3, which is the supply and demand equilibriumpoint. When the node receives more service requests, itincreases its price to reduce the number of requests andwhen it receives fewer service requests, it decreases itsprice to attract more service requests. After that, the pricegradually drops. This is because more and more nodescannot afford the high-reputed node’s service as theyare consuming their credits. Short queuing length leadsto low price, which helps attract more requests. Whensome nodes cannot afford a high price, they will selectlower-reputed nodes that offer lower prices. Therefore,the price of the node with R = 0.6 increases later on.The node’s supply and demand equilibrium price pointis 2.7. For the same reason, the price of the node withR = 0.2 subsequently increases. As the figure shows, thenode gradually reduces its price to attract more requestswhen it has not received requests for 60 seconds.

Figure 9(b) shows the change of the service price ofthe selected three nodes when the forwarding rate is1Mbps. We observe that the prices of the nodes withR = 0.6 and R = 0.2 stay at the base price of 1, while theprice of the node with R = 1 is greater than 1 before 60seconds. Compared to Figure 9(a), a higher forwardingrate in Figure 9(b) implies shorter service latency for eachsegment. Then, the queues of high-reputed forwardingnodes are less likely to be congested, leading to a lower



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price. Comparatively, a reasonable price and a high QoSof the high-reputed node attract most service requests,while the lower-reputed nodes hardly receive servicerequests and thus keep the base price. The figure alsoshows that the price of the high-reputed node fluctuatesbetween 2-3 due to the adaptive price adjustment.

Figure 9(c) plots the credits of each node when thesimulation ran 100 seconds versus the node’s reputationwith the forwarding rate was set to 1Mbps, 0.5Mbpsand 0.25Mbps, respectively. “Poly. (1Mbps)” denotesthe linear regression curve for the experimental resultswith forwarding rate 1Mbps based on the Polynomialdistribution model [40]. The same applies to the otherdenotations. The figure shows that the number of creditsof a node increases as the node reputation increaseswhen the forwarding rate is 1Mbps and 0.5Mbps. Ahigher-reputed node receives more requests, whichleads to higher price and more earned credits. Thefigure also shows that higher forwarding rate leads to ahigher credit increasing rate. A higher forwarding ratefor a node leads to a shorter queuing length and lowerprices, which attracts many requests. It is intriguing tosee that when the forwarding rate decreases to 0.25, thenodes with median reputation values have more credits.This is because when the packet forwarding rate issmall, the queue of a forwarding node is very likely tobecome congested. Then, a high-reputed node increasesits price to reduce the number of requests it receives.Subsequently, many nodes resort to median-reputednodes with lower price.

Figure 9(d) shows the change of price and creditsversus the simulation time when the forwarding rateis 1Mbps. The figure shows that there is a positivecorrelation between price and credits in the nodes withreputation equals to 1 due to the same reasons explainedin Figure 9(a). The figure also shows that although thelow-reputed nodes gain a small amount of credits, theirprice is kept at the base price 1. Because the pricesof the high-reputed nodes are low, low-reputed nodesreceive few requests and they keep the lowest price inorder to attract more requests.

5.6 Evaluation of the Market-based PoliciesIn this section, we evaluate the effectiveness of thetwo market-based policies on enforcing the effectivenessand fairness of cooperation incentives. We randomlyassigned the nodes in the system with a reputation valuewithin [0.1-1]. A node with reputation R has probabilityof R to forward a packet to a BS. The entire simulationarea was divided into two regions, a low-traffic regionand a high-traffic region. We randomly assigned 30% ofthe nodes to the low-traffic region and 70% of the nodesto the high-traffic region. Initially, we set the queuelength of each node to 50 messages and assigned 100credits to each node. Each node determines its serviceprice based on Formula (1) with α = 0.8 and δ = 6.The price ceiling was set to 1.5 and the tax rate for allnodes was set to 5%. We randomly selected these param-eter values within reasonable ranges. Each node whosereputation value is larger than 0.6 is subsidized with 50credits in every 10 seconds. The total simulation timewas 60 seconds. In each second, every node sent out 5packets to a neighboring node. A node chose its neighborwith the highest reputation value within its transmissionrange to send packets. When node nj receives severalrequests from other nodes, it accepts the requests in theorder from requesters with higher reputations to lowerreputations until its queue is full. Node nj then sends its

service price to its selected requesters. If a requester canafford the price, it sends its packet to node nj ; otherwise,it informs node nj . Node nj then selects other requestersuntil its queue is full, and sends “reject” messages toother requesters. The requesters whose packets fail tosend out due to full queues or unaffordable prices willtry to send out these packets in the next second. Weuse MDR-w-Policy (High) and MDR-w-Policy (Low)to denote the MDR protocol with the proposed twomarket-based policies in the high-traffic region and low-traffic region, respectively. We also use MDR-w/o-Policy(High) and MDR-w/o-Policy (Low) to denote the MDRprotocol without the two market-based policies in thehigh-traffic region and low-traffic region, respectively.

For all pairs of service receiver-provider, we groupedthe service providers for the service receivers with thesame reputation value, and then calculated the averagereputation of each group of providers. Figure 10(a) plotsthe average reputation value of each service providergroup versus the reputation value of the group’s corre-sponding service receivers. We see that in MDR-w/o-Policy (High) and MDR-w/o-Policy (Low), the aver-age reputation values of the service providers main-tain around 6. In contrast, in MDR-w-Policy (High)and MDR-w-Policy (Low), higher-reputed nodes receiveservices from higher-reputed service providers and viceversa. That is, the two market policies can enable morecooperative nodes to receive higher-QoS services even inthe low-traffic region, while constraining more uncoop-erative nodes to lower-QoS services.

In MDR-w/o-Policy, as no service priority is givento cooperative nodes, wealthy uncooperative nodes canalso buy services from high-reputed nodes. Also, asthere is no tax, some uncooperative nodes have moreopportunities to earn many credits to buy high-QoS ser-vices. In addition, as no subsidy is given to cooperativenodes in the low-traffic region, poor cooperative nodescan only buy services from low-reputed nodes due tohigh service price of high-reputed nodes. Therefore, thereputation value of the service requesters does not affecttheir received QoS. In MDR-w-Policy, the price ceilingpolicy (in Policy 1) bounds the service price, so thatpoor cooperative nodes can afford the service from high-reputed nodes. As explained in Section 2.4.2, the priceceiling policy will lead to a shortage of the servicesprovided by high-reputed nodes, and with the servicepriority policy (in Policy 1) that high-reputed nodes havehigher priority to buy services, the higher-reputed n-odes in MDR-w-Policy can purchase more services fromhigher-reputed nodes compared to MDR-w/o-Policy. Wecan also see from the figure that in MDR-w/o-Policy, theaverage reputation values of the service providers in thelow-traffic region is slightly lower than those in the high-traffic region. This is because the nodes in the low-trafficregion do not have enough credits to purchase high-QoS services. In MDR-w-Policy, the average reputationvalues of the service providers in the low-traffic regionis higher than those in the high-traffic region for low-reputed service requesters. As the high reputed nodes inthe low-traffic region are not likely to be congested sincethe traffic is low, more low-reputed nodes can purchaseservices from high-reputed nodes.

We grouped service receivers based on their reputationvalues, and calculated the sum of successfully deliveredtraffic of the nodes in each group. Figure 10(b) showsthe total successfully delivered traffic of each group ofservice receivers versus each group’s reputation value.We can observe similar experimental results from the



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figure as those in Figure 10(a). That is, the total success-fully delivered traffic in both MDR-w/o-Policy (High)and MDR-w/o-Policy (Low) maintains nearly constan-t, while in MDR-w-Policy (High) and MDR-w-Policy(Low), higher-reputed nodes have higher total success-fully delivered traffic while lower-reputed nodes havelower total successfully delivered traffic. The reasons arethe same as those in Figure 10(a). Also, MDR-w-Policy(High) generates more total successfully delivered trafficthan MDR-w-Policy (Low) due to the heavier trafficin the high-traffic region. The experimental results inFigure 10(a) and Figure 10(b) imply that the two marketpolicies enable high-reputed nodes to receive higher-QoSand vice versa no matter in the low-traffic region or inthe high-traffic region. Without the two policies, the QoSreceived by both high-reputed and low-reputed nodesis similar and every node has the same chance to buyservice from high-reputed nodes.

Figure 10(c) shows the traffic delivery rate of eachof the groups of service receivers versus the group’sreputation value. We can see that as the reputation valueof the service receivers increases, the traffic delivery rateincreases in both MDR-w-Policy (High) and MDR-w-Policy (Low), but the traffic delivery rates of MDR-w/o-Policy (High) and MDR-w/o-Policy (Low) maintain n-early constant. The reason is the same as Figure 10(a).The figure also shows that the low-reputed nodes inMDR-w-Policy (High) have lower traffic delivery ratethan in MDR-w-Policy (Low). Since in the high-traffic re-gion, the low-reputed nodes have low priority in servicecompetition and they cannot receive subsidy, they haveto buy service from low-reputed nodes which have lowpacket forwarding probability. In the low-traffic region,nodes do not receive many requests, thus the low-reputed nodes are able to purchase service from high-reputed nodes, which leads to a higher traffic deliveryrate for low-reputed nodes. These experimental resultsfurther confirm the effectiveness of the two market-basedpolicies in strengthening cooperation incentives.

Figure 10(d) shows the total amount of successfullydelivered traffic in MDR-w-Policy and MDR-w/o-Policy.

We see that the experimental results in the figure areconsistent with those in Figure 10(b). That is, MDR-w-Policy generates more successfully delivered trafficthan MDR-w/o-Policy and the high-traffic area gener-ates more successfully delivered traffic than the low-traffic area. The latter result is caused by the reason thatmore traffic is initiated in the high-traffic area than inthe low-traffic area. In MDR-w-Policy, because of thetwo market-based policies, reputed-nodes are able tobuy high-QoS service with high probability of successfulforwarding, generating much successfully delivered traf-fic. In contrast, in MDR-w/o-Policy, both high-reputednodes and low-reputed nodes are not able to buy high-QoS services due to lack of credits. In the low-trafficarea, nodes have few chances to earn credits due to fewservice requests. The price offered by the nodes in thehigh-traffic region is normally higher than the price inthe low-traffic region because the high service demandin the high-traffic area makes high-reputed nodes havehigher prices based on Equation (1). Therefore, nodesalso have fewer chances to buy high-QoS in the high-traffic region in MDR-w/o-Policy.

In order to show the effect of the individual market-based policy, we conducted experiments with only Policy1 and with only Policy 2, respectively. Figure 11 showsthe experimental results with the same metrics as inFigure 10. MDR-w/-Policy1 and MDR-w/-Policy2 rep-resent MDR with only Policy 1 and with only Policy 2,respectively. Comparing each figure in Figure 11 withcorresponding figure in Figure 10, we see that eachpolicy is effective in encouraging cooperation and dis-couraging selfish behaviors, and the combination of theeffect of both policies strengthen the final effectiveness.

6 RELATED WORKIn recent years, extensive research has been conductedon hybrid wireless networks (i.e., multi-hop cellularnetworks). Lin et al. [3] proposed the first hybrid wirelessnetwork, in which the service infrastructure is construct-ed by fixed BSes and multi-hop wireless transmissionin the MANET through MNs to BSes is used. Shin et



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al. [4] developed an infrastructure using multi-antennaBSes to improve the throughput scaling in networkswith randomly located wireless nodes. Asadi et al. [5]proposed a two-tier uplink forwarding scheme in hybridwireless networks. Wu et al. [6] developed an analyticalmodel to investigate the QoS of the hybrid wirelessnetworks. Wang et al. [7] studied achievable multicastthroughput for the hybrid wireless network. Shila etal. [8] established the same cell routing policy with multi-hop uplinks and studied the bounds on throughput anddelay of this policy. The research in [9]–[11] tries toimprove the capacity of conventional cellular networksby allowing ad-hoc communications between certainsources and destinations without the help of BSes so asto relieve their relay burden. Li et al. [12] attempted toexplore the capacity of multi-hop cellular networks withall traffic going through BSes and ad hoc transmissionsonly acting as relay. Lorenzo et al. [13] presented anapproach to optimize the throughput of multicast inmulti-hop cellular networks by applying a hexagonaltessellation to partition the cell into smaller subcells.

However, most of the hybrid wireless networks sim-ply combine the transmission modes of MANETs andinfrastructure networks for routing. Thus, they inheritthe drawbacks of ad-hoc transmission modes, such ascongestion and a high overhead for route discoveryand maintenance. MDR synergistically integrates the twodata transmission modes by taking advantage of thewidespread BSes while avoiding the drawbacks of ad-hoc routing. Wei and Gitlin [1] proposed a two-hoptransmission scheme to eliminate multi-hop route main-tenance overhead. However, their work focuses on one-cell networks while MDR is geared towards multi-cellnetworks. The single-relay feature of the MDR routingscheme also facilitates the effective reputation manage-ment and trading market management in MDR.

Cooperation incentives are needed to encourage co-operation between MNs in routing. Li et al. [44] pro-posed a self-enforcing incentive scheme in hybrid cel-lular networks, which comprises a global stimulatingpolicy among coalitions and local allocating rule withineach coalition. In multi-hop cellular networks, Kim [45]developed a trust model, in which the BSes appropri-ately react to selfish relay stations to maximize networkperformance. Reputation systems and price systems aretwo main methods to provide cooperation incentives.Many reputation systems [14]–[24] have been proposedfor wireless networks. In most current reputation sys-tems, each node periodically exchanges local reputa-tion information with its neighbors and aggregates itto yield others’ reputation values, which are referredfor forwarder selection in routing. However, using localpartial information for reputation evaluation may resultin an insufficiently accurate reputation value. Also, thisfrequent information exchange generates high overhead.Furthermore, the reputation systems cannot avoid falselyreported reputation information and cannot effectivelyprovide incentives for cooperation. MDR’s novelty re-lies on a P2P structure to avoid frequent informationexchange and provide more accurate reputation valuesbased on globally collected information and nodes’ ac-tual relayed messages.

In the price-based systems [46]–[59], nodes are paidfor offering packet forwarding service and pay forreceiving forwarding service. The payments can bein money, stamps, points or similar objects of value.The previous price-based works focus on the paymentmethod while MDR focuses on price determination

based on supply and demand equilibrium, which canserve as a complement to these works. MDR novellyallows nodes to adaptively adjust their price to controltheir loads. It also exploits the integration of the marketmodel and reputation system for fostering cooperationincentives. As far as we know, MDR is the first workthat investigates the market policies in cooperationencouragement in hybrid wireless networks.

7 CONCLUSIONSWe propose a P2P-based Market-guided DistributedRouting mechanism (MDR) to improve the throughputof hybrid wireless networks, where channel resources arestringent and nodes may not cooperate in data forward-ing. Current routing algorithms for hybrid networks donot fully exploit the BSes for efficient routing. Also, cur-rent reputation systems are not sufficiently efficient andeffective for reliable routing. We fully utilize the BSes byforming them into a locality-aware P2P overlay (LP2P),on which we develop a distributed routing algorithm(DRA), efficient and accurate reputation system (EARM)and trading market model (TMM). DRA splits packetstream based on erasure coding, transmits data in adistributed manner, selects relay nodes guided by EARMand TMM, and relies on LP2P to collect distributedsegments at the destination. EARM is superior to currentreputation systems due to its efficient reputation infor-mation collection, querying based on LP2P, and moreaccurate reputation values calculated based on globalinformation of actual relayed packets of relay nodes.TMM strengthens the incentives for node cooperationin routing by new market-based policies. These MDRcomponents coordinately contribute to efficient and reli-able routing for high throughput. In our future work, theimpact between DRA, EARM and TMM will be furtherstudied. Like other reputation systems, EARM in MDR isnot completely bullet-proof though we briefly discussedthe strategies to prevent misbehaviors. Misbehaviors togain fraudulent benefits in EARM and correspondingstrategies to prevent the misbehaviors will be investigat-ed. Also, we will investigate how to adapt EARM andTMM to multi-hop routing.

ACKNOWLEDGEMENTSThis research was supported in part by U.S. NSF grantsIIS-1354123, CNS-1254006, CNS-1249603, CNS-1049947,CNS-0917056, CNS-1025652 and Microsoft Research Fac-ulty Fellowship 8300751. An early version of this workwas presented in the Proceedings of SECON’12 [60].

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Haiying Shen received the BS degree in Computer Science and Engineering from Tongji University, China in 2000, and the MS and Ph.D. degrees in Computer Engineering from Wayne State University in 2004 and 2006, respectively. She is currently an Assistant Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University. Her research interests include distributed and parallel computer systems and computer networks, with an emphasis on peer-to-peer and content delivery networks, mobile computing, wireless sensor networks, and grid and cloud computing. She was the Program Co-Chair for a number of international conferences and member of the Program Committees of many leading conferences. She is a Microsoft Faculty Fellow of 2010 and a member of the IEEE and ACM.

Cheng-Zhong Xu received B.S. and M.S. degrees from Nanjing University in 1986 and 1989, respectively, and a Ph.D. degree in Computer Science from the University of Hong Kong in 1993. He is currently a Professor in the Department of Electrical and Computer Engineering of Wayne State University and the Director of Sun’s Center of Excellence in Open Source Computing and Applications. His research interests are mainly in distributed and parallel systems, particularly in scalable and secure Internet services, autonomic cloud management, energy-aware task scheduling in wireless embedded systems, and high performance cluster and grid computing. He has published more than 160 articles in peer-reviewed journals and conferences in these areas. He is the author of Scalable and Secure Internet Services and Architecture (Chapman & Hall/CRC Press, 2005) and a co-author of Load Balancing in Parallel Computers: Theory and Practice

Haiying Shen received the BS degree in Com-puter Science and Engineering from Tongji Uni-versity, China in 2000, and the MS and Ph.D.degrees in Computer Engineering from WayneState University in 2004 and 2006, respective-ly. She is currently an Associate Professor inthe Department of Electrical and Computer En-gineering at Clemson University. Her researchinterests include distributed computer system-s and computer networks, with an emphasison P2P and content delivery networks, mobilecomputing, wireless sensor networks, and cloud

computing. She is a Microsoft Faculty Fellow of 2010, a senior memberof the IEEE and a member of the ACM.

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Temporal Similarity Data Storage in Wireless Sensor Networks,”in Proc. of ICCCN, 2009.

Haiying Shen received the BS degree in Com-puter Science and Engineering from Tongji Uni-versity, China in 2000, and the MS and Ph.D.degrees in Computer Engineering from WayneState University in 2004 and 2006, respectively.She is currently an Assistant Professor in theDepartment of Electrical and Computer Engi-neering, and the Director of the Pervasive Com-munications Laboratory of Clemson University.Her research interests include distributed com-puter systems and computer networks, with an

emphasis on peer-to-peer and content delivery networks, mobile com-puting, wireless sensor networks, and grid computing. Her researchwork has been published in top journals and conferences in theseareas. She was the Program Co-Chair for a number of internationalconferences and member of the Program Committees of many leadingconferences. She is a member of the IEEE and ACM. She is MicrosoftResearch Faculty Fellow of 2010.

Lianyu Zhao received the BS and MS degreesin Computer Science from Jilin University, China.He is currently a Ph.D. student in the Depart-ment of Electrical and Computer Engineeringof Clemson University. His research interestsinclude wireless sensor network, routing proto-cols, applications and security issues in P2Pnetworks.

Ze Li received the BS degree in Electronics andInformation Engineering from Huazhong Univer-sity of Science and Technology, China, in 2007.He is currently a Ph.D. student in the Depart-ment of Electrical and Computer Engineeringof Clemson University. His research interestsinclude distributed networks, with an emphasison peer-to-peer and content delivery networks,wireless multi-hop cellular networks, game the-ory and data mining. He is a student member ofIEEE.

Ze Li received the BS degree in Electronics andInformation Engineering from Huazhong Univer-sity of Science and Technology, China in 2007,and the Ph.D. degree in Computer Engineeringfrom Clemson University. His research interestsinclude distributed networks, with an emphasison peer-to-peer and content delivery networks.He is currently a data scientist in the MicroStrat-egy Incorporation.

Lei Yu received the PhD degree in computerscience from Harbin Institute of Technology, Chi-na, in 2011. He currently is a post-doctoral re-search fellow in the Department of Electrical andComputer Engineering at Clemson University,SC, United States. His research interests includesensor networks, wireless networks, cloud com-puting and network security.

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A P2P-based Market-guided Distributed Routing …cs.virginia.edu/~hs6ms/publishedPaper/Journal/2014/A P2P...A P2P-based Market-guided Distributed Routing Mechanism for High-Throughput