Computer Communications 34 (2011) 548–555

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Computer Communications

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

A hierarchical architecture for detecting selfish behaviour in communitywireless mesh networks

Nikhil Saxena *, Mieso Denko 1, Dilip BanerjiDepartment of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1

a r t i c l e i n f o

Article history:Available online 5 May 2010

Keywords:Wireless mesh networksReputation managementWireless networksCooperative networks

0140-3664/$ - see front matter � 2010 Published bydoi:10.1016/j.comcom.2010.04.040

* Corresponding author. Address: Department ofScience, University of Guelph, Room 312, Reynolds G2W1. Tel.: +1 519 824 3105.

E-mail address: nsaxena@uoguelph.ca (N. Saxena)1 IEEE senior member.

a b s t r a c t

Wireless mesh networks (WMNs) consist of dedicated nodes called mesh routers which relay the trafficgenerated by mesh clients over multi-hop paths. In a community WMN, all mesh routers may not bemanaged by an Internet Service Provider (ISP). Limited capacity of wireless channels and lack of a singletrusted authority in such networks can motivate mesh routers to behave selfishly by dropping relay traf-fic in order to provide a higher throughput to their own users. Existing solutions for stimulating cooper-ation in multi-hop networks use promiscuous monitoring or exchange probe packets to detect selfishnodes and apply virtual currency mechanism to compensate the cooperating nodes. These schemes failto operate well when applied to WMNs which have a multi-radio environment with a relatively statictopology. In this paper we, propose architecture for a community WMN which can detect selfish behav-iour in the network and enforce cooperation among mesh routers. The architecture adopts a decentral-ized detection scheme by dividing the mesh routers into manageable clusters. Monitoring agentshosted on managed mesh routers monitor the behaviour of mesh routers in their cluster by collectingperiodic reports and sending them to the sink agents hosted at the mesh gateways. To make the detectionmore accurate we consider the quality of wireless links. We present experimental results that evaluatethe performance of our scheme.

� 2010 Published by Elsevier B.V.

1. Introduction

Wireless mesh networks (WMNs) are emerging multi-hop wire-less networks which provide a cost-effective solution to extend thecoverage of existing wireless networks. The architectural compo-nents of a WMN include mesh clients, mesh routers and gateways.Mesh routers form the wireless backbone providing services to themesh clients by relaying packets to and from the Internet. WMNsoften have one or more mesh gateways which provide backhaulconnectivity to the Internet. Nodes in WMNs have the capabilityfor dynamic self-organization and self-configuration. These attri-butes provide WMNs many advantages such as reliability, scalabil-ity and low upfront cost [1]. Due to these properties, WMNs havefound their application in various scenarios such as communitynetworking, pervasive healthcare, office and home automation,emergency rescue operations and ubiquitous wireless networkaccess.

In this paper, we have considered a community based-WMNwhich is used to provide Internet connectivity to community users.

Elsevier B.V.

Computing and Informationuelph, Ontario, Canada N1G

.

The mesh routers in the network can be managed by Internet Ser-vice Providers (ISP) or by independent users. Such networks caneither be fully managed, semi-managed or unmanaged [2]. In fullymanaged networks, all mesh routers are managed by an ISP andthere exists a prior trust relationship among them. In semi-man-aged network, part of mesh routers belong to the ISP while somemesh routers may belong to independent users which cannot as-sume each other to be trustworthy. An unmanaged network isformed by independent users in an ad hoc manner as it is notadministered by any authority.

Since the unmanaged mesh routers in semi-managed networksmay not share a priori trust relationship between each other, theymay drop packets originating from other mesh routers to increasetheir share of available bandwidth. Mesh routers which show suchbehaviour exploit the network services without contributing to it,and hamper the performance of other users.

Existing solutions for stimulating cooperation in multi-hop net-works have certain limitations. Reputation based schemes [3–5]use the idea of promiscuous overhearing which fails in a multi-channel environment of WMNs. Scheme based on the concept ofvirtual currency [9] fail with WMNs due to their static topology.The probe based schemes [7,8] incur communication overheadand congest the mesh gateway and therefore have limitedscalability.

N. Saxena et al. / Computer Communications 34 (2011) 548–555 549

In this paper, we propose a hierarchical report based monitoringarchitecture which enforces cooperation by detecting and punish-ing selfish mesh routers in the network. The contribution of theproposed scheme are: (a) it detects presence of selfish mesh rou-ters. (b) It uses a hierarchical reporting architecture which makesthe scheme more scalable by reducing the communication over-head. (c) It makes use of link quality metric to differentiate be-tween intentional packet drop and packet drop due to poor linkquality.

The rest of this paper is organized as follows. Section 2 dis-cusses the related work. Section 3 discusses the proposed scheme.Misbehaviour detection algorithm and reputation systems are pre-sented. Section 4 provides performance evaluation using simula-tion experiments. Detailed discussions of the simulation resultsare presented in this section. Finally Section 5 provides conclusionand our future work.

2. Related work

Non-cooperation is a major problem in a multi-hop networkdue to its effect on protocol performance. Despite a large volumeof work on cooperation in wireless ad hoc networks, relatively,not much research work has been conducted to enforce coopera-tion in wireless mesh networks. This section discusses some ofthe work done to avoid packet forwarding attacks in multi-hopwireless networks in general and their limitations when appliedto Wireless mesh networks. We focus on schemes based on repu-tation computation, virtual currency, and exchange of periodicreports.

Reputation based schemes observe the behaviour of theirneighbouring nodes through promiscuous overhearing and accord-ingly assign them a reputation rating which are used for identify-ing the selfish nodes. Nodes often have components such aswatchdog [3] which buffers all packets before transmission andthen overhears its neighbour’s transmission to check whether itis forwarded or not. In schemes like CORE [4] and CONFIDANT[5] results from the watchdog component are fed to the reputationsystems which update the reputation ratings on network nodesbased on their cooperation and participation in packet forwarding.Reputation values can be periodically shared by different reputa-tion components and nodes with low ratings are excluded or theirpackets denied forwarding. However, such schemes cannot be ap-plied to wireless mesh networks due to their multi-radio and mul-ti-channel characteristics. This is because a watchdog componenttuned to a certain channel cannot observe communication on otherchannels. Moreover promiscuous monitoring cannot differentiatebetween intentional packet drop and packet drop due to a trans-mission collision.

In probing based schemes the sender and destination nodes ofeach flow exchange probe packets to detect and identify selfishnodes. Awerbuch et al. [6] proposed a scheme based on this ideawhich uses end-to-end acknowledgments for every successfulpacket received. If the number of acknowledgements lost in a timewindow exceeds a certain threshold, the source starts the searchfor selfish node. A set of intermediate nodes are specified as probednodes such that they form non-overlapping intervals along the for-warding path. The probed nodes along with the destination mustsend back an acknowledgment for every packet. Once a fault is de-tected in an interval it is further sub-divided till the selfish node islocalized. The limitation of this scheme is that selfish nodes canidentify probe message and relay them to avoid detection. To coun-ter this limitation Kargl et al. [7] proposed a mechanism called iter-ative probing in which each node shares a key with every othernode in the network. Every packet header contains a field whichcontains the probe command to identify if the packet is a probe

packet or random padding. This field is encrypted with the privatekey of the probed node hence none of the other nodes can identifya probe packet. In case of packet dropping the source probes eachof the intermediate nodes iteratively and the first node to send anacknowledgement is detected as the selfish node. When the num-ber of packets dropped by nodes increases beyond an acceptablethreshold, they are excluded from the network. In the scheme pro-posed by Shila and Anjali [8] the detection threshold is calculatingby considering the characteristics of each link forming thesource–destination path, which helps in differentiating packet lossdue to intentional dropping and loss due to collisions. Probe basedschemes cause high overhead in the network due to exchange oflarge number of probe messages. Moreover since the main trafficin a WMN is uplink/downlink; the mesh gateway is involved inmost of the traffic flows and hence has to initiate probing of eachflow. This can cause creation of bottleneck near the mesh gateway.

Schemes like Nuglets [9] and Sprite [10] are based on virtualcurrency approach. The basic idea is to keep account for the for-warding services of the mesh routers. Buttyan and Hubaux [9] pro-posed a simple trading model using cryptographically securedvirtual currency. This encourages cooperation among nodes sincevirtual currency enables them to forward their own packets inthe network. However, this scheme relies on the presence of tam-per proof hardware to verify the authenticity of virtual currency.Moreover such schemes are not effective in a WMN where themesh routers at the periphery might not get a chance to forwardpackets and by earning any virtual currency. In Sprite [10] a centraltrusted auditing server detects selfish behaviour by collecting re-ceipts of all delivered packets from the network nodes. Thisscheme incurs high communication overhead since network nodeshave to submit a receipt for every received packet, and offers poorscalability due to presence of a single auditing server.

Santhanam et al., [11] proposed a scheme called Distributed-Self Policing Architecture for Fostering Node Cooperation (D-SAF-NC). In the scheme every mesh router sends periodic traffic reportsto the sink nodes.To enforce cooperation, selfish mesh routers areexcluded from the network after a certain number of offences. Un-like the Sprite architecture, D-SAFNC does not require a centralauditing server since it uses the mesh gateways to aggregate andprocess traffic reports from nodes. However one of its majorassumptions is the presence of sufficient number of gateways inthe network which ensures that every mesh router is within atwo hop neighbourhood of a gateway. With the increase in thenumber of mesh routers, the traffic caused by reporting nodescan congest the gateways and hence hamper the networkperformance.

3. The proposed scheme

In this section we discuss our proposed scheme for detectingselfish behaviour in community wireless mesh networks. We aimat making the detection of selfish mesh routers more accurateand efficient by overcoming the limitations of existing schemes.One of the limitations of existing schemes is that they do not con-sider the quality of the wireless links, which makes it challengingto differentiate between packet loss due to selfish intent and pack-et loss due to the characteristics of the wireless medium. This cancause increase in false positives and decrease in the detection rate.To increase the accuracy of detection rate, our scheme takes thecharacteristics of individual links into account. Another limitationwith most of the schemes is their huge dependence on the gate-ways. Due to the traffic pattern of WMN, the gateways are oftencongested with data and control packets transmitted to and fromthe gateways. In our scheme we reduce the involvement of gate-ways by delegating a set of managed mesh routers to assist in

550 N. Saxena et al. / Computer Communications 34 (2011) 548–555

the detection process and ultimately reduce the number of reportsbeing sent to the gateway.

The proposed scheme monitors the behaviour of mesh routersby using the traffic reports submitted by the mesh routers. Theproposed scheme follows a hierarchical reporting architecture.The decision making unit is hosted at the mesh gateway. In orderto reduce the workload of gateway and number of reports reachingit, the managed mesh routers of the network participate in thedetection process. Based on the traffic reports, the behavioural his-tory of mesh routers is maintained in form of their reputation. Thedecision of penalising a mesh router is made based in its reputationvalue. Since the behaviour of mesh routers is monitored throughtraffic reports rather than promiscuous overhearing, the proposedscheme can be used in a multi-channel environment. In the sequel,we will discuss the reporting architecture of the proposed scheme,the description of reports and the details of reputationmanagement.

3.1. Hierarchical reporting architecture

The proposed reporting architecture is based on a monitoringarchitecture called DAMON [12] (Distributed Architecture for Mon-itoring Multi-hop Mobile Networks). DAMON monitors the state ofmulti-hop networks for operations such as fault detection and iso-lation. It uses an agent–sink based monitoring architecture formulti-hop networks. The monitoring agents, installed on networknodes, collect the state of the network by either sniffing the trafficflowing through the wireless medium or by collecting state infor-mation from the neighbouring nodes. The information collectedby the monitoring agents is aggregated and processed by the sinknodes.

Based on this idea we propose a hierarchical reporting architec-ture which consists of monitoring agents (MAs) and sink agents(SAs). The MAs collect traffic reports from the neighbouring meshrouters. They are hosted by the mesh gateways and all the man-aged mesh routers in the network. Since the unmanaged mesh rou-ters are installed by independent users, they cannot be trusted toparticipate in the detection scheme, and therefore do not host aMA. Similar to the idea of sink nodes in the DAMON architecture,the proposed architecture has SAs which collect and process re-ports transmitted by the MAs and detect presence of selfish meshrouters. The distributed nature of Mesh Gateways and their abun-

MR1MR3

MR2

MR5MR6

MR7

MR12

MR11MR10

GW1

MA

MA

MA

MR14 MR15

MR16

GW2

SA

C1

C3

Internet

Fig. 1. Network architecture showing mesh routers logic

dance of computational resources make them well suited for host-ing the SAs. The hierarchical reporting architecture restricts thenumber of reporting packets reaching the mesh gateways. TheMAs process the node profiles locally, and transmit a single relayreport to the SAs. This greatly reduces the communication over-head of the detection scheme compared to other schemes [11]and alleviates the communication bottleneck by reducing thenumber of packets received by the gateway.

The process of selecting MAs and assigning mesh routers tothem can be easily done if the network is divided in clusters. TheMA can be hosted at the cluster-heads (CH) and the mesh routerswould be required to send their node profiles to their correspond-ing CHs. The reporting architecture adopts the lowest ID clusteringalgorithm [17]. As per the requirement of our scheme, only man-aged mesh routers can be elected as a CH. All mesh routers period-ically broadcast a ‘‘HELLO” message containing their network IDsto discover the presence of their neighbours. The managed meshrouters recognize the ID of another managed mesh router due toa prior trust agreement between them. Using this information,the managed mesh routers make a set of all neighbouring managedmesh routers and itself. If a managed mesh router has the lowestnetwork ID among its neighbouring managed mesh routers, it de-clares itself as a CH. Otherwise, it waits for other managed meshrouters to make their decision, after which it decides to either joinan existing cluster, or declare itself a CH. A managed mesh routerannounces its decision of being a CH, or joining another CH bybroadcasting a ch_bcast message. This message contains the net-work ID of that mesh router as well as its CH. The ch_bcast messagealso contains a field called hopcount which contains the currentdistance from its origin. On receiving these messages, the unman-aged mesh routers cache the network IDs of all CH and their dis-tance in number of hops. The unmanaged mesh routers join theclosest CH. This technique results in the formation of non-overlap-ping clusters having managed mesh router as their cluster-heads.Fig. 1 shows the hierarchical reporting architecture of the proposedscheme. The mesh routers are grouped into clusters and one meshrouter in each cluster is chosen as a cluster-head. In this architec-ture, MR1 declares itself as a cluster-head since its network ID isthe lowest among its neighbouring managed mesh routers. Afterhearing MR1’s decision, MR2 decides to join its cluster. WhenMR3 hears MR2’s decision of joining some other cluster, it decidesto be a cluster-head itself. Similarly MR15 declares itself as a clus-ter-head. MR16 joins the cluster headed by MR15, and MR17 decides

MR4

MR8

MR9

MR13

Mesh Gateway

Managed Mesh Router

Unmanaged Mesh Router

Monitoring Agent

Sink Agent

MAMR17

MA

SA

SA

C2

C4

ally divided into clusters and reporting architecture.

N. Saxena et al. / Computer Communications 34 (2011) 548–555 551

to be a cluster-head. The unmanaged mesh routers join the nearestcluster-head. This results in formation of the following four clus-ters: C1, C2, C3 and C4, headed by MR1, MR3, MR15, and MR17,respectively. The figure also shows MAs hosted on the cluster-heads, and SAs hosted on the mesh gateways.

3.2. Description of reports and formats

The previous subsection discussed the entities involved in thereporting architecture. In this subsection we discuss the detailsof traffic reports that flow between them. The detection scheme in-volves two types of reports: node profiles and relay reports. Fig. 2shows the flow of traffic reports between MAs and SAs. The nodeprofile of a mesh router contains details of its transactions withneighbouring mesh routers. Then mesh routers send their nodeprofiles to the MAs of their cluster. The node profiles are processedby the MAs to generate cluster relay reports. These reports containinformation about the packets relayed by the mesh routers. Thesereports are aggregated by the SAs to create master relay reports. TheSA uses this report to detect presence of selfish mesh routers in thenetwork.

3.2.1. Node profileThe node profile is traffic report generated by every mesh router

at a specified interval (TInterval) and sent to its MA. The node profileof a mesh router (MRR) contains information about packets sentand received by it with each of its neighbouring mesh routers(MRN). The node profile includes the following fields: Network IDof reporting node (IDR), network ID of neighbouring node (IDN),number of packets send to neighbouring node (Tx (MRR, MRN)),number of packets sent to the neighbouring node which terminateat the neighbouring node (TTx (MRR, MRN)), number of packets re-ceived from the neighbouring node (Rx (MRR, MRN)), number ofpackets received from the neighbouring node which originate fromneighbouring node (ROx (MRR, MRN)), link quality between thereporting node and the neighbouring node LQ (MRR, MRN). A meshrouter gathers this information by examining the address fields inthe headers of incoming and outgoing packets.

Unlike wired links which are either available or broken withcertainty, the wireless links can have intermediate loss ratios.The loss ratio of wireless links depends on factors such as the loca-tion of the radios, their surroundings, and inter-flow interference.Link quality is a measure of probability that transmission of a pack-et over a wireless link would be successful. For our scheme we usethe EAR [13] technique for estimating the quality of network links.EAR is implemented at the network layer and at the device driverlevel of each node and runs in a fully-distributed fashion to main-tain up-to-date link quality information of neighbouring wirelesslinks. EAR makes use of unicast based active probing techniquewhich is more accurate than the broadcast active probing tech-nique [14,15], since it uses the same data rate for probing a link

Gather Cluster Relay

Reports

Transmit Cluster Relay

Reports

Process Node Profiles to Generate

Cluster Relay Report

Gather Node Profiles from Mesh Routers

Create Master Relay Report

Identify and Exclude Selfish Mesh Routers

Sink Agent

Monitoring Agent

Fig. 2. Activity diagram showing flow of traffic reports.

as that for actual data transmissions over the link. EAR employsboth active and passive measurement techniques. When there isenough egress traffic, EAR passively monitors the traffic transmis-sion, and when the egress traffic is low, it transmits probe packetsand monitors their transmission. Thus EAR reduces probing over-head by opportunistically exploiting large amount of relay andegress traffic in the wireless mesh networks. EAR calculates thelink quality (delivery ratio) using Eq. (1) [13].

di ¼ ð1� aÞ � di�1 þ a� NS

NTð1Þ

where:

� di is the smoothed delivery ratio� a is the smoothing constant� NS is the number of successful transmissions during the mea-

surement period of the ith cycle� NT is the total number of transmissions during the measure-

ment period of the ith cycle.

During the passive measurement the values NS and NT corre-spond to the actual data traffic, while they correspond to the probepackets during active measurement.

3.2.2. Relay reportsAfter receiving the node profiles from all mesh routers of its

cluster, the MA processes them to generate a cluster relay report.A relay report contains information about the packet relayingbehaviour of the mesh routers. The relay reports contain the num-ber of packets that a mesh router was supposed to forward (Ex-pected Forward Count) and the number of packets that it actuallyforwarded (Actual Forward Count) since the previous detectioncycle.

EFC (Eq. (2)) of a mesh router represents the number of packetsit is supposed to forward. This value is obtained by counting the to-tal number of packets sent to the concerned mesh router by itsneighbours (Tx). However the packets which are destined for theconcerned mesh router (TTx) are not supposed to be relayed byit, and are therefore deducted from EFC. Each of these values ismultiplied with the link quality of the corresponding wirelesslinks. This is done to adjust the values according to the forwardingcapability of the wireless links.

EFCA ¼XfTxðMRi;MRAÞ � TTxðMRi;MRAÞg � LQðMRi;MRAÞ ð2Þ

where

� EFCA is the Expected Forward Count for MRA

� MRi represents each of the neighbouring mesh routers of MRA

� Tx, TTx and LQ are fields from the node profile submitted byMRi.

AFC (Eq. (3)) of a mesh router represents the total number ofpackets actually forwarded by it. This value is obtained by countingthe total number of packets that neighbouring mesh routers re-ceive (Rx) from the concerned mesh router. However the packetsoriginating from the concerned mesh router (ROx) are not consid-ered as relay packets. Therefore these packets are deducted fromthe AFC value. Each of these values is divided by the link qualityof the corresponding wireless links.

AFCA ¼XRxðMRi;MRAÞ � ROxðMRi;MRAÞ

LQðMRi;MRAÞð3Þ

where:

� AFCA is the Actual Forward Count for MRA

552 N. Saxena et al. / Computer Communications 34 (2011) 548–555

� MRi represents each of the neighbouring mesh routers of MRA

� Rx, ROx, and LQ are fields from the node profile submitted byMRi.

The relay report calculated by the MAs is based only on the traf-fic information provided by the node profiles of its cluster. Meshrouters of the cluster could share communication links with meshrouters of another cluster. Therefore the relay information of amesh router is incomplete if only the node profiles of its clusterare considered. To complete the relay information, the MAs submitthe relay reports of their cluster to the SA at the mesh gateway. TheSA combines all the relay reports to form a Master Relay Report.The entries for a mesh router appearing in multiple cluster relayreports are added up.

3.2.3. An example scenarioThe following example illustrates the generation of node profile

and relay report.Fig. 3 shows a WMN with five mesh routers (MR1 to MR5). The

mesh routers are divided into 2 clusters (C1, and C2). We considertwo traffic flows in the network. The first flow originates frommesh router MR2 which transmits 100 data packets to MR3 viaMR1. The second traffic flow involves mesh routers from more thanone cluster. In this flow MR1 transmits 100 data packets to MR4 viaMR3. In this network all wireless links are simulated to have apacket success ratio of 0.8.

Table 1 shows the node profiles sent by the mesh routers ofcluster C1. Based on these values the MA of the cluster calculatesthe cluster relay report. To calculate the relay information of amesh router the node profiles of its neighbours are used. The MAconsiders all those node profile entries whose neighbouring IDfield (IDN) is the same as the ID of that mesh router. For instance,to calculate the relay information of MR1 the third and fourth rowsof Table 1 are considered.

EFC1 ¼XfTxðMRi;MR1Þ � TTxðMRi;MR1Þg � LQðMRi;MR1Þ

MR2

MR1

MR3

MR4

MR5

GW

Cluster C1

Cluster C2

Fig. 3. A mesh network with two domains and three clusters.

Table 1Node profile entries for cluster C1.

IDR IDN Tx TTx Rx ROx LQ

1 3 177 77 0 0 0.81 2 0 0 80 80 0.82 1 100 0 0 0 0.83 1 0 0 141 80 0.83 4 80 80 0 0 0.8

EFC1 ¼ fTxðMR2;MR1Þ � TTxðMR2;MR1Þg � LQðMR2;MR1Þþ fTxðMR3;MR1Þ � TTxðMR3;MR1Þg � LQðMR3;MR1Þ ð4Þ

EFC1 ¼ f100� 0g � 0:8þ 0 ¼ 80

Eq. (4) shows the calculation of EFC for MR1 as calculated by theMA of cluster C1. MR2 transmitted 100 data packets to MR1 forrelaying. Considering the packet loss due to link quality MR1 is ex-pected to forward 80 data packets.

AFC1 ¼XRxðMRi;MR1Þ � ROxðMRi;MR1Þ

LQðMRi;MR1Þ

AFC1 ¼RxðMR2;MR1Þ � ROxðMR2;MR1Þ

LQðMR2;MR1Þ

þ RxðMR3;MR1Þ � ROxðMR3;MR1ÞLQðMR3;MR1Þ

ð5Þ

AFC1 ¼ 0þ 141� 800:8

AFC1 ¼610:8¼ 76:25

Eq. (5) shows the calculation of AFC of MR3 as calculated by theMA of cluster C1. According to the node profile sent by MR3 it hasreceived 141 data packets from MR1 and 80 of those packets haveoriginated from MR1. Considering the link quality it is inferred thatMR1 has relayed approximately 76 data packets. Similarly the MAcalculates relay information for other mesh routers to generate thecluster relay report. These reports are combined by the SAs to formmaster relay reports.

3.3. Reputation management

The previous section discussed the details of gathering the traf-fic information from the mesh routers of the network. Based onthat information the proposed scheme identifies and excludes self-ish mesh routers. To achieve this, the SA maintains a behaviouralhistory of all mesh routers in a data structure called reputation ta-ble. This subsection discusses the computation of reputation valuesand the response mechanism of the reputation system.

3.3.1. Reputation computationThe master relay report generated by the SA contains the relay

information for all mesh routers in the network. Based on theinformation contained in the report the SA calculates the selfish-ness indices (X) of all mesh router in the network. Selfishness In-dex is the measure of selfishness exhibited by a mesh router overa period of time. We define Selfishness Index (Eq. (6)) of a meshrouter as the fraction of packets dropped by it since the previousdetection cycle.

X ¼ EFC� AFCEFC

ð6Þ

where:

� AFC is the Actual Forward Count of the mesh router� EFC is the Expected Forward Count of the mesh router

The value of X has a lower bound of 0, which corresponds to awell behaved mesh router and an upper bound of 1, which corre-sponds to a completely selfish mesh router.

The Selfishness Index of a mesh router signifies its instanta-neous behaviour. To judge selfishness of a mesh router its behav-iour history is maintained in form of its reputation. The SAmaintains a data structure called reputation table for maintaining

N. Saxena et al. / Computer Communications 34 (2011) 548–555 553

reputation rating of all the mesh routers in its domain. To make thereputation more dependent on the recent behaviour of a mesh rou-ter its reputation rating (R) is calculated as the exponential movingaverage of its selfishness indices recorded over time (Eq. (7)).

Rt ¼ aXt þ ð1� aÞRt�1 ð7Þ

where:

� Rt is the reputation rating for the tth detection cycle� Xt is the Selfishness Index values for the tth detection cycle.� a is the smoothing constant, 0 < a < 1.

The weight given to current behaviour as compared to pastbehaviour is determined by the smoothening constant (a), whichhas value bounded within the range [0,1]. This ensures that thereputation rating of a mesh router corresponds to its recent behav-iour. Hence only those mesh routers which exhibit selfish behav-iour on a regular basis are punished.

3.3.2. Response mechanismSelfish mesh routers are temporarily excluded from the net-

work so that they can be forced to cooperate. When reputation rat-ing (R) of a mesh router exceeds the detection threshold (DTH), it isput into a probation state during which it is not allowed to trans-mit or receive any packets. Other mesh routers remove the selfishnode from their routing table and chose alternative paths to sendtheir packets. The SA broadcasts a probation message within its do-main, which contains the ID of the selfish mesh router and proba-tion expiry time which is equal to current time + probation period(PT). Until the end of probation period other mesh routers ignore alltraffic originating or terminating at the selfish mesh router. At theend of probation period the mesh router is given another chance touse the network services and participate in packet forwarding.However if it continues to behave selfishly it is put on the proba-tion state for a longer period (2PT). Therefore the probation periodof a selfish mesh router is doubled on every subsequent offence un-til it reaches a maximum value ðPT

MAXÞ after which it is permanentlyexcluded from the network.

A wireless mesh network usually offers multiple alternativepaths between a source and destination and removing a mesh rou-ter from a network does not significantly affect the performance ofrest of the network. However when multiple mesh routers are re-moved from the network, the presence of certain mesh routersmight become critical for ensuring the connectivity of other meshrouters. Removal of these critical nodes can cause network parti-tioning and leave certain mesh routers completely cut off fromthe mesh gateway. To deal with this issue, the SAs in our schemeuse connectivity graph from the network layer to identify the pres-ence of these critical nodes. To avoid network partitioning, thesecritical nodes are not excluded from the network even if they aredetected as selfish.

Table 2Simulation parameters.

Traffic type Constant bit rate (CBR)Network simulator NS3Environment dimensions 1500 m � 1500 mSimulation time 100 sPacket size 1024 BytesNumber of flows 2Smoothening constant (a) 0.3Reporting interval (TInterval) 10 sDetection threshold (DTH) 0.1

4. Performance evaluation

In this section we analyse the performance of our proposedscheme. The performance of the scheme is evaluated under differ-ent scenarios such as varying percentage of selfish mesh routers,percentage of lossy wireless links and under different networksizes. The performance metrics used in this study are packet deliv-ery ratio, false positive rate and hop counts of the reportingscheme. The proposed scheme has been compared with existingsolution [11].

4.1. The simulation environment

We have used the NS-3 network simulator [16] for carrying outsimulations. Simulation results have been averaged over 10 runswith random seeds. The some parameters for simulation are listedin Table 2. The network nodes are arranged in a grid topology, andthe first node (gateway) receives packets from the source nodes inthe network. The network contains 20% managed mesh routers.Certain simulation parameters such as network size, and percent-age of selfish mesh routers were varied during experiments tomeasure their effect on the following performance metrics.

(1) Packet delivery ratio (PDR): It is the percentage of total num-ber of packets that have been delivered to the destinationsout of the total number of packets originated by the sourcenodes. This is our primary metric which shows how effec-tively the network transmits packets from source todestination.

(2) False positive rate: It is defined as the percentage of numberof cooperating mesh routers wrongly detected as selfish outof the total number of cooperating mesh routers in thenetwork.

(3) Hop Length of the reporting packets: The average number ofhops covered by the reporting packets during every detec-tion cycle. For D-SAFNC this value corresponds to the sumof hops from every network node to the gateway. For ourscheme this value is sum of hops from network nodes totheir respective cluster heads and hops of the cluster-headsto the gateway node.

4.2. Discussion of simulation results

(1) The effect of percent of selfish mesh routers: To investigate theeffectiveness of the detection scheme in detecting selfishmesh routers we measure the PDR under various percent-ages of selfish mesh routers. For every simulation run certainmesh routers were programmed to behave selfishly by drop-ping 10–20% of their relay packets. The selfish mesh routersare not chosen as source nodes and therefore do not origi-nate any packets of their own.Fig. 4 shows the averagePDR of proposed scheme compared with the unprotectednetwork under varying percentage of selfish mesh routers.As shown in the graph, the performance of networkdecreases as the number of selfish mesh routers increases.When there are no selfish mesh routers in the network thePDR of proposed scheme is slightly lower than the unpro-tected network due to the detection overhead. In other casesthe detection scheme attempts to improve the network per-formance. However, as the percentage of selfish mesh rou-ters increases beyond 10% the performance of unprotectednetwork suffers even more, and performance improvementdue to the detection scheme becomes more significant. Itshould be noted that as the percentage of selfish mesh rou-

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Fig. 4. PDR comparison of proposed detection scheme with an unprotectednetwork.

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Fig. 6. Comparison of the proposed scheme and D-SAFNC as a function of linkquality.

554 N. Saxena et al. / Computer Communications 34 (2011) 548–555

ters increases beyond 35%, the performance improvementshown by the detection scheme starts to decline. Finally,the difference between the two schemes disappears as morethan 44% selfish mesh routers in the network. The reasonbehind this is that when the ratio of selfish mesh routers ishigh there are fewer alternatives available for choosing for-warding paths, and selfish mesh routers may be included inthe forwarding paths.In Fig. 5 we compare the performance of our scheme with D-SAFNC by plotting the average PDR as a function of percent-age of selfish mesh routers in the network. The proposedscheme shows higher PDR in all the cases which is attributedto higher congestions in D-SAFNC due to higher reportingoverhead. The simulation results show that the performanceimprovement becomes higher with higher percentage ofselfish mesh routers. It is interesting to note that the perfor-mance of D-SAFNC falls even below the unprotected net-work when the percentage of selfish mesh routers exceeds37. This occurs because exclusion of large number of nodescauses network partitioning and some legitimate mesh rou-ters are left unreachable from the mesh gateway.

(2) The effect of wireless link quality: To investigate the effect ofpoor quality links on performance, we simulated certainwireless links in the network to perform poorly which couldbe caused in real life due to network characteristics. Thepoor quality links were simulated to have a forwarding ratioranging from 0.4 to 0.6. We varied the number of lossy wire-less links and examined the number of false positives givenby our detection scheme and D-SAFNC. As shown in Fig. 6, D-SAFNC shows increasing false positive rate with poor quality

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Fig. 5. PDR comparison of proposed detection scheme with D-SAFNC.

links, since the packets loss due to link quality is falselydetected as selfish behaviour.The proposed scheme does show a few false positives forlower percentage of lossy links. These false positives couldbe explained by the packet loss caused by congestions. Con-gestion is caused by the creation of heavy traffic near themesh gateway. As the percentage of lossy links increases,fewer packets are transmitted towards the gateway, thusrelatively easing up the bottleneck.Fig. 7 compares the two detection schemes and unprotectednetwork in terms of packet delivery ratio. The results showthat D-SAFNC falsely detects certain nodes as selfish duetheir poor wireless links. With the increase in percentageof lossy links, an increasing number of legitimate mesh rou-ters are excluded from the network resulting in rapidlydecreasing PDR for D-SAFNC scheme.

(3) The effect of network size: In this study, we varied the numberof mesh routers to investigate its effect on the reportingoverhead. Fig. 8 shows the average hop counts covered bythe traffic reports in each detection cycle. As shown in thefigure, the number of hop count covered by the reportingpackets is fewer for our scheme as compared to the D-SAFNCscheme. In D-SAFNC the mesh gateway collects reports fromall the mesh routers, and hence the average hop count cov-ered by its reports increases steeply with network size. Onthe other hand, in our proposed scheme the mesh routersneed to transmit their reports to the MAs. The MA processesthem and finally sends a single relay report to the meshgateway. Due to this hierarchical reporting mechanism, theincrease in hop lengths of reports is only linear with increasein network size.

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Fig. 7. PDR as a function of link quality.

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Fig. 8. Comparison of the overhead of the three schemes as a function of networksize.

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Fig. 9. Comparison of the delivery ratio of the three schemes as a function ofnetwork size.

N. Saxena et al. / Computer Communications 34 (2011) 548–555 555

Fig. 9 shows the PDR of the three schemes under varyingnetwork sizes. The packet delivery ratio decreases for allthe schemes with the increase in network size. However,rate of decrease in D-SAFNC scheme is much higher thanthe proposed scheme. This is due to the congestion causedfrom the reporting packets generated in the network.

5. Conclusions and future work

In this paper we have proposed an architecture and protocol toenforce cooperation in community wireless mesh networks. Thescheme detects presence of selfish mesh routers in the networkand forces them to cooperate by taking necessary actions. Thebehaviour of mesh routers is monitored by collecting traffic reportsperiodically. The SAs (hosted at the mesh gateways) process thesereports to detect selfish behaviour. The architecture aims at reduc-ing the load of the mesh gateways by partially delegating thedetection process to a set of mesh routers. This set of mesh routersis managed by the ISPs and therefore can be trusted. To make thedetection scheme more accurate the quality of wireless links is alsotaken into account.

We have shown through simulation experiments that ourscheme offers performance improvement over an existing schemeconsidered and a network without a detection scheme. Due to theuse of link quality metric the scheme gives accurate detection evenin presence of poor wireless links. We have also shown that ourscheme is much more scalable due the reduced involvement ofthe gateway. As part of future work, we plan to extend our schemeto support autonomic detection of malicious nodes. Moreover, wewould be looking to verify our scheme’s performance using meshrouters in a real world scenario.

References

[1] F.I.F. Akyildiz, Xudong Wang, A survey on wireless mesh networks, IEEECommunications Magazine 43 (2005) S23–S30.

[2] L. Santhanam, Bin Xie, D. Agrawal, Selfishness in mesh networks: wiredmultihop MANETs, IEEE Wireless Communications 15 (2008) 16–23.

[3] S. Marti, T.J. Giuli, K. Lai, M. Baker, Mitigating routing misbehavior in mobile adhoc networks, in: Proceedings of the Sixth Annual International Conference onMobile Computing and Networking, 2000, pp. 255–265.

[4] P. Michiardi, R. Molva, Core: a collaborative reputation mechanism to enforcenode cooperation in mobile ad hoc networks, in: Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and MultimediaSecurity: Advanced Communications and Multimedia Security, 2002, pp. 107–121.

[5] S. Buchegger, J. Boudec, Performance analysis of the confidant protocol:cooperation of nodes-fairness in dynamic ad hoc networks, in: Proceedings ofthe Third ACM International Symposium on Mobile Ad Hoc Networking &Computing, 2002, pp. 226–236.

[6] B. Awerbuch, D. Holmer, C. Nita-Rotaru, H. Rubens, An on-demand securerouting protocol resilient to byzantine failures, in: Proceedings of the FirstACM Workshop on Wireless Security, 2002, pp. 21–30.

[7] F. Kargl, A. Klenk, S. Schlott, M. Weber, Advanced detection of selfish ormalicious nodes in ad hoc networks, in: Proceedings of the First EuropeanWorkshop on Security in Ad-Hoc and Sensor Networks, 2005, pp. 152–165.

[8] D.M. Shila, T. Anjali, Defending selective forwarding attacks in WMNs, in:Proceedings of the IEEE International Conference on Electro/InformationTechnology, EIT 2008, 2008, pp. 96–101.

[9] L. Buttyan, J.P-. Hubaux, Enforcing service availability in mobile ad-hoc WANs,in: Proceedings of the First ACM International Symposium on Mobile Ad HocNetworking & Computing, 2000, pp. 87–96.

[10] S. Zhong, J. Chen, Y.R. Yang, Sprite: a simple, cheat-proof, credit-based systemfor mobile ad-hoc networks, in: Proceedings of the 22nd Annual JointConference of the IEEE Computer and Communications, IEEE Societies,INFOCOM 2003, vol. 3, 2003, pp. 1987–1997.

[11] L. Santhanam, N. Nandiraju, Y. Younghwan, D.P. Agrawal, Distributed self-policing architecture for fostering node cooperation in wireless meshnetworks, in: Proceedings of the 11th International Conference on PersonalWireless Communications, IFIP TC6, PWC 2006, 2006, pp. 147–158.

[12] K.N. Ramachandran, E.M. Belding-Royer, K.C. AImeroth, DAMON: a distributedarchitecture for monitoring multi-hop mobile networks, in: Proceedings of the2002 ACM Workshop on Wireless Security, 2004, pp. 601–609.

[13] K. Kim, K.G. Shin, On accurate measurement of link quality in multi-hopwireless mesh networks, in: Proceedings of the 12th Annual InternationalConference on Mobile Computing and Networking, MOBICOM 2006, 2006, pp.38–49.

[14] D.S.J. De Couto, D. Aguayo, J. Bicket, R. Morris, A high-throughput path metricfor multi-hop wireless routing, in: Proceedings of the Ninth AnnualInternational Conference on Mobile Computing and Networking, 2003, pp.134–146.

[15] R. Draves, J. Padhye, B. Zill, Routing in multi-radio, multi-hop wireless meshnetworks, in: Proceedings of the 10th Annual International Conference onMobile Computing and Networking, 2004, pp. 114–128.

[16] The Network Simulator - ns-3. Available online at: <http://www.nsnam.org>.[17] C.R. Lin, M. Gerla, Adaptive clustering for mobile wireless networks, IEEE

Journal on Selected Areas in Communications 15 (1997) 1265–1275.