IEEE 802.11s Mesh Backbone for Vehicular Communication: Fairness and Throughput

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 62, NO. 5, JUNE 2013 2193

IEEE 802.11s Mesh Backbone for VehicularCommunication: Fairness and Throughput

Sandip Chakraborty, Student Member, IEEE, and Sukumar Nandi, Senior Member, IEEE

Abstract—Providing ubiquitous connectivity inside a mobilevehicle is a challenging task that requires efficient vehicle-to-infrastructure communication. A wireless mesh network (WMN)provides a cost-effective and easily maintainable solution to man-age the backhaul of vehicular communication. The mesh points(MPs) act as access points for mobile vehicles and connect themto the Internet through multihop mesh communication. IEEE802.11s is the recent standard for a WMN, which provides meshcoordinated function controlled channel access (MCCA) for effi-cient and minimum-contention channel access at MPs. However,the standard MCCA-based communication provides equal-timefair channel access, which is not suitable for vehicular communi-cation. This paper proposes an efficient channel access mechanismbased on IEEE 802.11s MCCA to provide proportionally fairchannel access. A distributed scheme is proposed to calculatetraffic load at every MP, and based on the traffic load, the MCCAis tuned to meet the required proportionality constraint. Theefficiency of the proposed scheme is confirmed using theoreticalanalysis and simulation results.

Index Terms—Fairness, IEEE 802.11s, mesh coordinated chan-nel access, vehicular network, wireless mesh network (WMN).

I. INTRODUCTION

PROVIDING ubiquitous connectivity inside mobile vehi-cles is a recent demand that has attracted the research com-

munity to design new channel access and network managementprotocols for such dynamic network scenarios. IEEE 802.11p[1] is the current standard for vehicle-to-roadside access pointcommunication that aims to provide seamless connectivity andincreased throughput for vehicular communication. The road-side access points form a wired backbone or distribution systemand are connected to outside Internet through gateways. Thistype of network architecture requires a complex managementsystem due to the wired backbone, and in addition, the mainte-nance cost is increased.

A wireless mesh network (WMN) is a promising technol-ogy to replace the wired backbone for vehicular communica-tion. WMNs are a self-organized and self-configured networkthat provides cost-effective solution for replacing the wired

Manuscript received March 28, 2012; revised July 10, 2012; acceptedJanuary 5, 2013. Date of publication January 11, 2013; date of current versionJune 12, 2013. The work of S. Chakraborty was supported by TATA Consul-tancy Services (TCS), India, through the TCS Research Fellowship Program.The review of this paper was coordinated by Dr. F. Bai.

The authors are with the Department of Computer Science and Engi-neering, Indian Institute of Technology, Guwahati 781039, India (e-mail:[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2013.2239672

backbone with wireless mesh routers. IEEE 802.11s [2]–[4]is the IEEE standard for mesh networking built upon IEEE802.11 Physical layer specifications and medium access control(MAC) layer channel access protocols. IEEE 802.11s definestwo types of nodes: mesh stations (STA), which are clientnodes, and mesh points (MPs), which are basically mesh routersand gateways. MPs can be of two types: The routers arecalled mesh access points (MAPs), and the mesh gatewaysare called mesh portal points (MPPs). MPPs connect the meshnetwork with outside Internet. MAPs form the mesh backboneby forwarding traffic to or from STAs to MPPs, or vice versa.Other than forwarding traffic to or from STAs, MAPs also actas relay nodes that forward traffic from neighboring MAPs.In the context of vehicular communication, MAPs work asroadside access points and form the mesh backbone for trafficforwarding.

The IEEE 802.11s MAC layer uses mesh coordination func-tion (MCF) for contention-based and scheduled channel accessmethods. The MCF includes functionality provided by bothenhanced distributed channel access (EDCA) and mesh co-ordinated function controlled channel access (MCCA) as thechannel access protocol [3]. EDCA is built upon the well-used distributed coordination function (DCF)-based MAC layerprotocol with the binary exponential backoff algorithm forcontention resolution. In EDCA, devices do not cooperate witheach other, and under high load, EDCA becomes less efficient.In case of MCCA, MPs reserve future channels in terms ofMCCA opportunity (MCCAOP). MCCA performs better com-pared with EDCA in case of highly loaded and bursty trafficnetwork scenarios.

This paper considers IEEE 802.11s as the MAC layer pro-tocol with MCCA-based channel access for the wireless meshbackbone. IEEE 802.11p is used for vehicle-to-access-pointcommunication. Thus, every MP is equipped with two inter-faces: one IEEE 802.11p interface for vehicular communicationand one IEEE 802.11s interface for mesh communication. Mostof the current commodity MPs, such as Cisco Aironet 1500MAP [5], use two interfaces, i.e., one for mesh communicationand another for client communication. Hence, this two-interfacearchitecture is directly available for use for the proposed vehic-ular mesh backbone.

The traffic characteristic of vehicular communication showsthat most of the traffic for highway mobility in urban areasis bursty [6]. Hence, in the wireless mesh backbone, someMPs experience very high traffic load, whereas traffic load atother MPs is moderate to low. Although MCCA-based channelaccess is more flexible compared with EDCA-based channelaccess for highly loaded and bursty traffic [7], it experiences the

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2194 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 62, NO. 5, JUNE 2013

proportionality unfairness in case of a bursty traffic condition.Mesh coordinated channel access allows any MP to reserve fu-ture channel, which is called MCCAOP. The maximum amountof slots an MP can reserve in a delivery traffic indicationmessage (DTIM) interval is bounded by an MCCA accessfraction (MAF) limit. However, the MAF limit is set to a fixednetwork specific value, i.e., (1/255)th of the DTIM interval.This provides equal-time fairness to all MPs. However, dueto the bursty traffic nature and high load variation at differentparts of the network, proportional fairness is more suited for avehicular mesh backbone. The MP with more traffic load shouldget more channel share. This unfairness in proportionally fairchannel access impacts the flow throughput, and individual flowthroughput may degrade.

In this paper, we propose to use an adaptive MAF limit basedon the proportionality constraint on channel access and vehicu-lar traffic estimation at every MP. This enables efficient usage ofthe IEEE 802.11s mesh backbone for vehicular communication.The following points have been assumed while designing theestimation mechanism.

• All MPs in the mesh backbone are MCCA supported.However, a non-MCCA node can exist in the vicinity ofthe MPs. Thus, there may be contention between MCCA-supported MPs and non-MCCA nodes, and hence, theowner of an MCCAOP may need to contend for thechannel during MCCAOP period using the IEEE 802.11eEDCA access mechanism. However, this paper does notdeal with the effect due to contention between MCCA-supported MPs and non-MCCA nodes. There are schemesthat exist in literature, such as [8], to deal with thisproblem.

• The proposed scheme assumes that interference can oc-cur up to two-hop neighborhood. Beyond two hops, in-terference is negligible. This is because MPs are staticnodes with high efficiency and processing power. Dueto a capture effect [9], interference beyond two hops isnominal, and interfered signals can be easily recovered.This assumption is also used in the design of the MCCA-based channel access mechanism.

• The MPs are equipped with two interfaces: one IEEE802.11s interface for mesh communication and one IEEE802.11p interface for vehicular communication.

• The standard IEEE 802.11p handover scheme is used atvehicular clients. This paper does not discuss about thehandover issues and its effect on the proposed scheme. Theeffect of load variance at neighboring access points due toclient mobility is considered, and the simulation is donebased on this assumption.

A preliminary version of this paper is presented in [10]. Theearlier paper assumes IEEE 802.11 DCF-based channel accessat the mesh backbone and provides some preliminary results forfair channel allocation based on traffic estimation. This paperextends the idea for IEEE 802.11s MCCA-based channel accessin the mesh backbone. The simulation results and performanceanalysis are provided to show the efficiency of the proposedmechanism. The proposed scheme is directly compatible withthe IEEE 802.11s standard and requires minimal changes in the

standard algorithm. In summary, this paper has the followingcontributions.

1) It provides an effective mechanism of using an IEEE802.11s mesh network as the backbone of vehicularcommunication. The need for providing proportional fair-ness based on traffic load at MPs has been analyzed.Based on traffic load, a load estimation strategy has beenproposed, which works at every MPs in a decentralizedmanner.

2) It provides an effective tuning mechanism of the MAFlimit for IEEE 802.11s MCCA to achieve proportionalfairness.

3) Theoretical analysis is provided to justify the effective-ness of the proposed scheme.

4) Finally, extensive simulation results are reported toanalyze the performance improvement of the pro-posed scheme compared with IEEE 802.11s EDCA andMCCA.

The rest of this paper is organized as follows: Section IIdescribes the state-of-the-art works related to this paper. Themain motivation behind this paper and the proposed schemeis described in Section IV. Section V provides the theoreticalvalidation of the proposed scheme. In Section VI, the effective-ness of the proposed scheme is shown using simulation results.Finally, Section VII concludes this paper.

II. RELATED WORKS

The standard IEEE 802.11s is a very recent standard pub-lished in September 2011 [3]. Hence, most of the earlier worksare based on its draft versions. There are very few worksthat exist in literature that deals with IEEE 802.11s meshnetworking. Beckman et al. [11] propose a “multipoint-to-multipoint” or mesh connectivity between vehicular clients forvehicle-to-vehicle communication. However, this work doesnot consider ubiquitous connectivity inside vehicular clientsby providing vehicle-to-infrastructure communication. In [12],integration of a vehicular ad hoc network and Internet using awireless mesh backhaul is proposed. The performance analysisshown from the practical testbed, as reported in [12], showsthe possibility of using a WMN as the backbone for vehicularcommunication. They have used MobiMesh technology [13]at the wireless mesh backbone and have not analyzed theperformance of IEEE 802.11s at the mesh backbone. How-ever, the channel access algorithm for MobiMesh is based onknowledge of the complete network topology, which is neitherscalable nor IEEE 802.11 compatible. Optimization in IEEE802.11s channel access for the vehicular mesh backbone isnecessary as it is the current standard for mesh networkingtechnology, and most of the commercial products, such asCisco Aironet 1500 MAP [5], are built upon the IEEE 802.11standard.

There are few works that exist in literature that try tosolve the inherent problem in IEEE 802.11s MCCA-basedchannel access. The performance of MCCA-based channelaccess is affected due to the presence of non-MCCA stationsin the vicinity of MCCA-capable stations [8]. As discussed in[8], the owner of MCCAOP needs to perform IEEE 802.11

CHAKRABORTY AND NANDI: IEEE 802.11s MESH BACKBONE FOR VEHICULAR COMMUNICATION 2195

EDCA-based channel access in the scheduled MCCAOP tocompete with non-MCCA legacy stations. The scheme usesa preemptive MCCA to deal with this problem and per-forms better in the presence of non-MCCA legacy stations.In [14], Cicconetti et al. describe the problem of schedulingMCCAOPs, which is left unspecified by the standard. Thescheme provides an algorithm that determines the locationof requested MCCAOPs inside the DTIM interval. They fur-ther provide a dynamic relocation algorithm to deal with in-terference occurred beyond two-hop neighborhood. However,as earlier discussed, interference is nominal beyond two-hopneighborhood in case of the static mesh backbone and can beeasily recovered due to the capture effect.

Lenzini et al. [15] proposed a dynamic delay-balancing slotallocation for IEEE 802.11s MCCA. In the paper, they havecalculated the delay experienced by per-flow using queuinganalysis and used that analysis to calculate the amount ofslots to be reserved inside MCCAOP to balance delay betweenindividual flows. However, only flow-based analysis does notprovide efficient usage of channels, unless the total traffic toserve for an MP and the traffic in the neighborhood is analyzed.In [16], Krasilov et al. show the possibility of existence ofhidden stations and interference caused by acknowledgmentpackets sent by hidden stations in MCCA-based channel ac-cess. In [17], Tomic and Neskovic presented a simulation-based comparison between EDCA and MCCA and showed theperformance improvement in the case of MCCA, as comparedwith EDCA-based channel access in a mesh networking sce-nario. Vanhatupa et al. [18] proposed a deployment design foran IEEE 802.11s mesh network to minimize interference andmaximize coverage. The design also minimizes the number ofaccess points required to cover a certain area and is effective fordeployment of static mesh networks.

However, none of the aforementioned works has consideredto use IEEE 802.11s mesh networking technology at the ve-hicular backbone. As earlier discussed, using a mesh networkat the vehicular backbone requires handling time-driven burstytraffic. Hence, the MPs should dynamically adjust channelaccess priority based on traffic conditions. Furthermore, theproposed scheme should not burden the implementation com-plexity. Keeping all these in mind, this paper proposes a trafficestimation strategy and provides a mechanism based on IEEE802.11 MCF to dynamically adjust channel access priority atevery MPs. The simulation results and analytical discussionjustify the effectiveness of the proposed mechanism.

III. BACKGROUND: IEEE 802.11s MESH

COORDINATION FUNCTION

IEEE 802.11s [3] provides both EDCA and MCCA asthe channel coordination function for mesh basic service set(MBSS). To provide completeness, the MCCA functionality isbriefly described here. MCCA is a reservation-based channelaccess method that aims to optimize the efficiency of frametransfer inside an MBSS. MCCA is an optional access methodthat allows MPs to access wireless media at selected timeswith lower contention. MCCA is considered for mesh back-bone channel access as it is more suitable for bursty vehicular

Fig. 1. MCCAOP reservation with periodicity 2.

traffic. The MPs reserve future channel through MCCAOP. TheMCCA setup is done through mesh beaconing. The MP thattransmits an MCCA setup request frame to initiate a reservationbecomes the MCCAOP owner of the MCCAOP reservation.The receivers of the MCCA setup request frames are theMCCAOP responder. The MCCAOP owner and the MCCAOPresponder advertise the MCCAOP reservation to their neigh-bors through MCCAOP advertisement. The neighbors, afterreceiving the MCCAOP advertisement, defer their transmissionfor the MCCAOP period. This reduces the contention betweenMCCA-supported MPs. However, contention is still possiblewith non-MCCA nodes. During the MCCAOP, the MCCAOPowner obtains the transmission opportunity through EDCA-based channel access, by contending with non-MCCA nodes.To use MCCA, MPs keep synchronized with their neighboringMPs. The MPs use a DTIM interval for beaconing and reserva-tion purpose.

The reservation of an MCCAOP is initiated by the MCCAOPowner. The MCCAOP owner builds a map of the neighbor-hood MCCAOP periods in the DTIM interval, after hearingthe advertisement from all its neighbors. Then, it determinesMCCAOP reservations. The MCCAOP reservation parametersare as follows:

• duration of the MCCAOP in slots;• offset of the MCCAOP in slots with respect to the begin-

ning of the DTIM interval;• periodicity to specify how many MCCAOPs are to be

allocated within the DTIM interval.

The duration, offset, and periodicity of MCCAOP in a DTIMinterval are shown in Fig. 1.

Based on the MCCAOP reservation request, the MCCAOPresponder either accepts or rejects the MCCAOP and sendsthis information through an MCCAOP reply message. Oncethe request is granted, both the MCCAOP owner and theMCCAOP responder broadcast this information throughMCCAOP advertisement messages. The advertisement mes-sage contains the MCCAOP TX–RX period, MCCAOP broad-cast period, and MCCAOP interference period report. Based onthis information, the neighboring MPs defer their transmissionperiod. The MCCAOP advertisement message also containsMAF, i.e., the ratio between the sum of TX–RX period, broad-cast period, and interference period, to the DTIM interval. Theamount of channel capacity reserved for MCCA transmission isupper bounded by the MAF limit. It should be noted that everyMP keeps an up-to-date list of its neighbors using the meshpeer link management protocol. Periodic beaconing after meshbeacon transmission time is used to synchronize between neigh-boring MPs.


IV. MESH COORDINATION FUNCTION CONTROLLED

CHANNEL ACCESS-BASED MESH BACKBONE FOR

VEHICULAR COMMUNICATION

A. Unfairness in Channel Reservation

The IEEE 802.11s standard [3] only employs a MAF limitthat upper bounds the amount of channel capacity reservedfor MCCA transmissions. The standard provides a fixed valuefor the MAF limit, which is rounded down (floor) to thenearest multiple of (1/255) of the DTIM interval length. Beforeattempting to set up an MCCAOP reservation with a neighbor,an MP should verify that the new MCCAOP reservation doesnot cause neither its MAF to exceed its MAF limit nor itsneighbors’ MAF to exceed their MAF limit. An MCCAOPsetup request is refused by the MCCAOP responder if the MAFlimit of one of its neighbors is exceeded due to a new setup.

A fixed value for the MAF limit provides time fairness inMCCA-based channel access. However, time fairness does notprovide efficient channel access for bursty vehicular traffic. Asearlier discussed, some MPs may experience very high loadcompared with others in its neighborhood. Hence, proportionalfairness is more appropriate for a vehicular mesh backbone.Thus, the MP with higher traffic load should get higher oppor-tunity to transmit packets. The MAF limit should be set to avalue that is proportional to the traffic load of an MP. This is themain motivation of this paper. Based on the traffic load analysisat MPs, an adaptive MAF limit is proposed, which providesproportional fairness at vehicular mesh backbone and performsbetter in the case of bursty traffic.

B. Network Model

A vehicular mesh backbone can be represented as a networkgraph G = (V,E), where V is the set of MPs, and E is the setof links. A link e ∈ E is a tuple (vi, vj); vi, vj ∈ V , where viand vj are in the range of each other. As earlier described, eachMP also acts as a relay node. Hence, there are mainly two typesof traffic: one is from self-clients (the clients that are directlyconnected to the MAP) and another is the relayed traffic. It hasbeen assumed that traffic is to or from MPPs. This assumption isvery general in the context of vehicular mesh backbone used forubiquitous connectivity inside mobile vehicles. The total trafficfor MP M can be represented as follows:

TM =∑∀c

λc (1)

where c is the client that the MP serves either as self-client oras relayed, and λc is its traffic generation rate. Now, the amountof self-traffic can be estimated from received signal strengthindicator (RSSI)-based threshold calculations, but it is hard toestimate relayed traffic because of the cumulative effect. This isdescribed in following sections.

C. Estimation of Vehicular Mobile Clients at MAPs

The estimation of vehicular clients under a MAP is based onthe RSSI. The RSSI-based measurement is previously used forlocalization of vehicular nodes [19]. However, the exact loca-tion of every vehicle is not required in the proposed scheme of

Fig. 2. Estimation of mobile vehicular clients.

this paper. An estimation of vehicular clients under every MAPis required to get the amount of traffic to or from self-clients.Hence, a simpler method is proposed here to get an estimationof vehicular clients under every MAP in a highway scenario.

Each MP maintains two threshold parameters based on theRSSI: communication RSSI (RC) and handoff RSSI (RH).The communication RSSI is the signal strength threshold abovewhich the MAP and the vehicular nodes can successfullycommunicate. The handoff RSSI is the signal strength thresholdbelow which the vehicular node should initiate the handoffprocedure. These two threshold values divide the coverage areaof a MAP in two zones, as indicated in Fig. 2. The handoff RSSIthreshold is kept higher than the communication threshold tomaintain seamless connectivity. This also helps to preinformthe handoff notifications to the next MAP so that the next MAPcan reserve channels for the forthcoming clients. Let Ri

H be thehandoff RSSI threshold value for MAPi. Each vehicular clientperiodically broadcasts beacon messages in IEEE 802.11p-based channel access. This periodic beaconing can be usedto estimate vehicular clients. The beacon message contains alist of tuples (MAPi, RSSIi), where MAPi is the ID of theMAP in its vicinity, and RSSIi is the RSSI value receivedfor MAPi. The MAPs maintain a list of the RSSI valuesreceived from successive beacon messages from a vehicularclient. In a highway scenario, a MAP can find out the directionof the vehicular clients from the signal strength of consecutivebeacon messages. If the signal strength of consecutive beaconmessages gradually increases, it indicates that the vehicularclient is moving toward the access points. Similarly, if the signalstrength of successive beacon messages gradually decreases, itindicates that the vehicular client is going far from the MAP.

Now, MAPi sets a vehicular client in its active client list ifthe following two conditions hold.

• The vehicular client sends an association request.• RSSIi ≥ Ri

H − δ and the direction is toward MAPi.(That is, the signal strength for successive beacons in-creases.) Here, δ is a tolerance value that is same for allMAPs.

Similarly, MAPi removes a vehicular client from its activeclient list if the following two conditions hold.

• The vehicular client sends a deassociation request.• RSSIi ≤ Ri

H + δ and the direction is away from MAPi.(That is, the signal strength for successive beacons de-creases.) Here, δ is a tolerance value that is the same aspreviously shown.


According to Fig. 2, MAP1 will accept c2 to its client listand remove c1 from the list. This estimation can be performedusing out-of-band signaling (if required).

D. Total Traffic Load Estimation at MPs

The total network traffic load at each MP in the meshbackbone consists of two types of traffic: traffic to or from theself-clients and the relayed traffic to or from other MPs in itsneighborhood. Again, based on the direction, the traffic can beof two types, namely, uplink or downlink. The cumulative up-link and downlink traffic can be represented with the followingrecursive equations:

• Cumulative Uplink Traffic (CUT) for MPn can be ex-pressed as

CUTn ← λun +

∑i∈Un

CUTi (2)

where λun is the total uplink traffic from self-clients, and

Un is the set of those MPs forwarding uplink trafficto MPn.

• Cumulative Downlink Traffic (CDT) for MPn can beexpressed as

CDTn ← λdn +

∑i∈Dn

CDTi (3)

where λdn is the total downlink traffic for self-clients, and

Dn is the set of MPs to which MPn forwards downlinktraffic.

• Cumulative Traffic Load (CTL) for MPn can be ex-pressed as

CTLn = CUTn + CDTn. (4)

The CUT and CDT values can be computed in a distributedfashion at each MP (described later in Section IV-G).

E. Estimation of Required Channel Share

The required channel share, i.e., RSi, for MPi can beformulated as

RSi =σi∑

j∈Ni

σj(5)

whereσi traffic load for MP i;Ni set of MPs in the two-hop neighborhood of MP i.To estimate the required channel share, as shown in (5), each

MP should know the set of MPs that are contending with it inits neighborhood. In IEEE 802.11s MCCA, MCCAOP adver-tisement messages provide the contention information to everyMP. This can be used to find out the set of contending MPs. TheCTL value is transferred to adjacent MPs through MCCAOPadvertisement messages. Equation (5) can be represented interms of CTL as

RSi =CTLi∑

j∈Ni

CTLj. (6)

Equation (6) represents the required channel share for an MPto ensure proportional fairness.

F. Adaptive Tuning of MAF Limit

According to the IEEE 802.11s standard, every MP calcu-lates MAF before reserving the next MCCAOP and comparesthe MAF value with the MAF limit whether it is eligible toreserve any more channel in the next DTIM interval. The MAFat a mesh node is the ratio of the time reserved for MCCAOPsin the DTIM interval of this mesh node to the duration of theDTIM interval. The standard proposes a fixed MAF limit forall MPs that provide equal channel access to every MP. Thispaper proposes to use an adaptive MAF limit that providesproportional fairness among the contending MPs. Rather thanusing a fixed MAF limit, every MP tunes its MAF limit beforetransmitting the MCCAOP reservation request, as follows:

MAF Limiti = RSi. (7)

As for every MP MPi,∑

i∈NjRSj = 1, every MP limits the

channel reservation according to its required channel share in aDTIM interval.

The standard defines that an MP does not reserve MCCAOPsif either its MAF value exceeds its MAF limit or the MAF valueof any of its neighbors exceeds their MAF limit. The MAFvalue of the neighbors is checked to handle the interferencewith the neighbors. It should be noted that while calculating theMAF value, the MPs also include the interference period.The proposed adaptive MAF limit tuning mechanism handlesthe interference calculation while calculating the required chan-nel share value. Considering the capture effect as earlier men-tioned, the interference is limited up to two-hop neighborhoodamong static mesh routers. Every MP calculates its requiredchannel share from the load information on all the MPs inits two-hop neighborhood, as shown in (6). In the proposedscheme, the following two modifications are done at the timeof MCCAOP reservations.

1) The interference period is not included in the MAFcalculation. Hence, in the proposed scheme, the MAF isdefined as the ratio between the sum of the TX–RX periodand broadcast period to the DTIM interval.

2) While sending the MCCAOP reservation request, theMCCA owner checks its MAF value with its MAF limit.It does not check neighbor MAF values. Similarly, theMCCAOP responder checks its MAF value with its MAFlimit.

Once the MAF limit is adjusted in an adaptive way based onthe required channel share, every MP can reserve future channelmaximum up to its need, which is proportional to its traffic load.While calculating the required channel share, every node alsoencounters the need for their neighbors. This way, consistencyin channel access among the MPs in a common neighborhoodis assured.

G. Distributed Algorithm to Calculate CUT and CDT

A piggybacked value is used in DATA and ACK packets topropagate traffic load estimation throughout the network, andeach MP calculates its CUT and CDT from the piggybacked


TABLE ISTATE VARIABLES AT EACH MPi

values. The piggybacked value with the DATA frame is usedto propagate the CUT value, whereas the piggybacked valuewith the ACK frame is used to propagate the CDT value. Thedetail algorithm is reported next. Algorithm 1 and Algorithm 2are executed at MPi upon receiving DATA and ACK packets.Each MP maintains lists and variables, as shown in Table I.For downlink traffic, the MPP forwards DATA frames with apiggybacked value of −1.

Algorithm 1 On receiving DATA frame from MPj

1. if PL �= −1 then2. /∗DATA for uplink traffic∗/

3. if MP iu = NULL then

4. CUTi ← λiu /∗Initialize∗/

5. else6. LCUT(j) ← PL7. CUTi ← λi

u +∑

k∈MP iuLCUT(k) /∗Update∗/

8. end if9. Forward DATA to next hop MPk ∈ Ri

u with CUTi

10. Send ACK to MPj with −111. else12. /∗DATA for downlink traffic∗/

13. Send ACK to MPj with CDTi

14. Forward DATA to next hop MPk ∈ Rid with −1

15. end if

Algorithm 2 On receiving ACK frame from MPj

1. if PL �= −1 then2. /∗ACK corresponding to DATA for downlink traffic∗/

3. LCDT(j) ← PL4. end if5. if MP i

d = NULL then6. CDTi ← λi

d /∗Initialize∗/

7. else8. CDTi ← λi

d +∑

k∈MP idLCDT(k) /∗update∗/

9. end if

The piggybacked value in a DATA or ACK packet can beeither a CUT value or a CDT value. These can be differentiatedas follows.

• If the DATA packet contains a positive piggybacked value,then it is for uplink traffic, and the piggybacked value is the

CUT value. The ACK packets for uplink traffic contains“−1” as the piggybacked value.

• If the ACK packet contains a positive piggybacked value,then it is for downlink traffic, and the piggybacked valueis the CDT value. The DATA packets for downlink trafficcontains “−1” as the piggybacked value.

Algorithm 1 is executed when an MP receives a DATApacket. If the piggybacked value is a positive value, then itis the CUT value of the source MP. Upon receiving such aDATA packet, the CUT value at the receiver MP is updated.If the piggybacked value is “−1,” it indicates that the DATApacket is for downlink traffic. The CDT value of the MPis piggybacked with the ACK packet for that DATA packet.Algorithm 2 is executed when an MP receives an ACK packet.If the piggybacked value with the ACK packet is a positivevalue, then it is the CDT value of the source MP. The CDTvalue for the corresponding MP is updated.

From Algorithm 1 and Algorithm 2, it can be calculated thatthe CUT value propagates in O(1) messages, whereas the CDTvalue propagates in O(h) messages. Here, h is the depth of thedownlink tree from the MPP to vehicular clients.

V. THEORETICAL ANALYSIS

In [20], Bisnik and Abouzeid modeled delay and throughputfor a WMN using G/G/1 queuing analysis. For performancemodeling, they considered a random access WMN, which usesDCF with binary exponential backoff for MAC layer channelaccess. The G/G/1 queuing analysis is adopted here for IEEE802.11s MCCA.

A WMN can be modeled as a queuing network, as shown inFig. 3. The stations of the queuing network represent MAPs.pij denotes the probability that a packet is transmitted from thequeue at MP i to the queue at MP j. Let us assume that thenetwork consists of n vehicular clients distributed uniformlyand independently over a torus of unit area. The wireless meshbackbone, as proposed in this paper, uses regular and uniformplacement of MPs beside the roads. Let us further assume thatthe torus is uniformly divided into nonoverlapping zones of areaa(n), where an MP is placed in every zone and covers the com-plete zone. Packets are generated at vehicular clients accordingto an independent identically distributed Poisson process withrate λ. Whenever a packet is generated at a vehicular client, itis forwarded to the MP of its zone. The MP forwards the packetover the backbone until it reaches the MPP or the zone where


Fig. 3. Queuing network model of a WMN.

Fig. 4. Placement of MCCAOP in the DTIM interval.

the destination is located. Let p(n) denote the probability that apacket received by an MP is destined to a vehicular client withinits zone. The probability that a packet received by an MP isforwarded to a neighboring MP is (1 − p(n)). The visit ratio ofa queuing network is defined as the average number of times apacket is forwarded by the station. The following lemmas from[20] is reported here for completeness of the analysis. Theselemmas are also applicable in the present context.

Lemma 1: The visit ratio of MP i, which is denoted by ei, isequal to a(n)/p(n).

Lemma 2: The effective packet arrival rate at MP i, which isdenoted by λi, is equal to na(n)(λ/p(n)).

Lemma 3: The number of hops traversed by a packet in aWMN, which is denoted by s̄, is equal to 1/p(n).

Now, let X̄i denote the average waiting time of MP i ina DTIM interval. Then, the maximum achievable throughput(λmax) is the maximum value of packet arrival rate λ atvehicular clients for which λiX̄i < 1.

There are two sources of packet arrival at an MP: packetsgenerated by self-clients in its zone and packets that are for-warded to the MP from neighboring MPs. The effective packetarrival rate λi at MP i can be expressed as

λi = λ.ei

where ei is the visit ratio at MP i.The average waiting time of an MP in the DTIM interval is

the average time from the en-queue of a packet in the queueuntil the packet gets serviced from the queue. Let a commonMAF limit be used for every MP. Assume that L is the MAFlimit and that DI is the DTIM interval. Hence, the maximumMCCAOP length in a DTIM interval can be L. As shown inFig. 4, the MCCAOP can be placed anywhere uniformly inthe DTIM interval. Packet transmission starts at the beginningof MCCAOP. From the definition, the average waiting time ofan MP in the DTIM interval is uniformly distributed between

[0, DI − L]. The expected waiting time of MP i, i.e., X̄i, in aDTIM interval can be expressed as

X̄i =DI − L

2. (8)

The following inequality can be derived as

λiX̄i < 1 ⇒ na(n)λ(DI − L)

2.p(n)< 1

⇒λ <2

s̄na(n)(DI − L)

λ =Θ

(1

s̄na(n)(DI − L)

). (9)

The maximum achievable throughput λmax = Θ((1/s̄na(n)(DI−L))). Now, from [20], when n→∞, a(n)=(log(n)/n) and p(n) =

√log(n)/n. Then, for large number of vehicu-

lar clients λmax = Θ((1/√

nlog(n)(DI − L))).Hence, the maximum achievable throughput is inversely pro-

portional to DI − L. Therefore, tuning MAF limit can improvethe performance of a WMN.

The utilization factor of MP i, i.e., ρi, can be written as

ρi =λi

μi(10)

where μi is the mean service time of MP i.Let every MP use a separate MAF limit that is adaptively

tuned based on the traffic load, as discussed in Section IV-F.Let Lb

i be the MAF limit for MP i in DTIM interval b. Asthe average waiting time in a DTIM interval is uniformlydistributed in [0, DI − Lb

i ], hence

μi =DI − Lb

i

2.

The utilization factor of MP i can be written as

ρi =2na(n)λ

p(n)(DI − Lb

i

) . (11)

The network is stochastically stable under proportional fair-ness if and only if [21] ∑

∀i∈Nρi < CN (12)

where CN is the total normalized traffic load of the interferingMPs in the neighborhood N.

From (7), (11), and (12), the following theorem can bederived.

Theorem 1: The network is stochastically stable under pro-portional fairness, that is,

∑∀i∈N ρi < CN, if ∀ MP i, Lb

i =RSi ×DI .

Proof:

∑∀i∈N

ρi =∑∀i∈N

2na(n)λ

p(n)(DI − Lb

i

)

=2na(n)λCN

p(n).DI

∑∀i∈N

1(CN − CTLi

).


Now

∑∀i∈N

1(CN − CTLi

) < 1

2na(n)λp(n)

= λe

where λe is the average effective arrival rate of all MPs inthe neighborhood N. The size of the DTIM interval is kepteffectively large to accommodate high packet arrival rate (i.e.,(λe/DI) < 1). Hence ∑

∀i∈Nρi < CN.

�

VI. SIMULATION RESULTS

The proposed scheme is simulated using an NS-3 networksimulator [22]. The vehicular mobility and highway scenariois generated using Highway Mobility extension for NS-3 [23].100 MPs have been uniformly deployed in a highway scenario,which is generated using Traffic Software Integrated System[24]. The capture effect is enabled at every MP to reduceinterference beyond two hops. Every MP is equipped with twointerfaces: one IEEE 802.11s interface for mesh communica-tion and one IEEE 802.11p for vehicular communication. Bothinterfaces use IEEE 802.11g physical layer technology, with54 Mb/s for mesh communication and 6 Mb/s for vehicularcommunication. The 5.9-GHz physical channel band is usedfor vehicular communication, and the 2.4-GHz physical chan-nel band is used for mesh communication. Mobile traffic isgenerated using Weibull distribution with variable data rate(indicated as the traffic load in simulation graphs). As shown in[25], Weibull random variable captures the short- and medium-term characteristics of traffic flows in vehicular networks. Theduration of each flow is selected according to lognormal ran-dom variables [25], with mean 20 s and standard deviation 2.CBR traffic is used to generate traffics with packet size =1000 bytes and interarrival time as 32 μs. The MCCA slotduration is taken as 32 μs, and the DTIM interval is takenas 32 ms. Out of the 100 MPs, five MPs act as the gatewayto the Internet. The results from the simulation are shown inFigs. 5–13. In all the figures, the term “Adaptive MAF” limitis used to denote the proposed scheme. EDCA and MCCA arealso simulated in the same scenario for comparison with theproposed scheme.

Fig. 5 shows the network throughput with respect to offeredload, which is generated by varying the mean of Weibulldistribution and keeping the variance fixed at 1.5 Mb/s. As theoffered load increases, the proposed mechanism performs bettercompared with IEEE 802.11s EDCA-based and IEEE 802.11sMCCA-based channel access. The network throughput in thecase of IEEE 802.11s EDCA is at a minimum. As the trafficload increases, IEEE 802.11s EDCA performs poorly. The pro-posed scheme increases the network throughput by providingproportionally fair channel access to the MPs. The networkthroughput with respect to load variance is shown in Fig. 6.

Fig. 5. Traffic load versus throughput.

Fig. 6. Load variance versus throughput.

The load variance is generated by keeping the mean of Weibulldistribution fixed at 10 Mb/s and varying the standard deviation.It is shown in the figure that as the load variance increases, theperformance of IEEE 802.11s EDCA severely decreases. Theproposed scheme performs better compared with both IEEE802.11s EDCA-based and IEEE 802.11 MCCA-based mediumaccess.

Outage ratio is defined as the number of flows whose packetloss is greater than a threshold, divided by the total number oftraffic flows. The threshold is taken to be equal to 5% of thetraffic in simulation. Fig. 7 shows the outage ratio with respectto the offered load. IEEE 802.11s EDCA has the maximumoutage ratio, and the outage ratio is at a minimum for theproposed scheme. The outage ratio is expected to be lowerif the buffer overflow is less at the intermediate MPs. Theproposed scheme experiences less buffer overflow at individualMPs due to proportionally fair channel access. The MP withhigh traffic load gets more channels to transmit packets, whichmakes buffer overflow at a minimum. A similar result is shown


Fig. 7. Traffic load versus outage ratio.

Fig. 8. Load variance versus outage ratio.

in Fig. 8 for different load variance. At low load variance, theoutage ratio is almost similar for all the schemes. As the loadvariance increases, the proposed scheme performs better thanboth the IEEE 802.11s EDCA-based and the IEEE 802.11sMCCA-based channel access mechanism.

Finally, we analyze the performance of the proposed schemein terms of Jain Fairness Index [26]. Fig. 9 shows the fairnessindex with respect to the offered load. As the offered loadincreases, the proposed scheme shows more fairness comparedwith the standard schemes. Similar result can be observed fordifferent load variance, as shown in Fig. 10. As the load vari-ance increases, the standard scheme shows greater unfairness;however, the proposed scheme shows better fairness comparedwith EDCA and MCCA access mechanisms. With the pro-posed adaptive MAF limit, there is almost 60% improvementin Fairness index, 29% decrements in outage ratio, and 55%improvement in network throughput, with load variance 5 Mb/s.Thus, the proposed scheme shows substantial improvement

Fig. 9. Traffic load versus fairness.

Fig. 10. Load variance versus fairness.

in performance compared with IEEE 802.11s MCCA-basedchannel access.

Fig. 11 shows the performance of the three schemes asvehicle density increases. For this experiment, the variancein vehicle density among neighboring MPs is randomly var-ied between 2 and 4. As vehicle density under a singleMP increases, the performance of both IEEE 802.11s EDCAand IEEE 802.11s MCCA drastically degrades. IEEE 802.11sEDCA is a contention-based protocol, and the contention at theMPs increases with the increase in vehicle density. However,EDCA cannot handle the large contention between traffic fromself-clients and relayed traffic, and the traffic near the MPPsgets prioritized. This reduces the throughput for the trafficthat is far from the MPP. A similar case happens for IEEE802.11s MCCA. The MPs near the MPPs get overloaded. Inthe proposed protocol, the channel is proportionally distributedamong all the MPs contending for channel access. Thus, allvehicular clients get an equal chance at traffic forwarding,


Fig. 11. Effect of vehicle density.

Fig. 12. Variance in vehicle density.

irrespective of their distances from MPPs. This improves av-erage per-vehicle throughput in the proposed scheme. Fig. 12shows the effect of variance in vehicle density among neigh-boring MPs. Both IEEE 802.11s EDCA and IEEE 802.11sMCCA provide equal-time channel access to all neighboringMPs. Hence, when the variation in density of vehicular clients ishuge among neighboring MPs, the MP with low load gets over-access to the channel compared with the MP with high load.Thus, per-vehicle throughput for both IEEE 802.11s EDCAand IEEE 802.11s MCCA drops as the variance in vehicularclients increases. Fig. 13 shows the effect of vehicle speed. Asvehicle speed increases, the variance in the density of vehicularclients increases in the network, and the performance for IEEE802.11s EDCA and IEEE 802.11s MCCA degrades. As perFigs. 12 and 13, the proposed scheme distributes the channelproportionally among all the contending MPs according to theirtraffic load, and thus, the performance for the proposed schemeis considerably better compared with IEEE 802.11s EDCA andIEEE 802.11s MCCA.

Fig. 13. Effect of vehicle speed.

VII. CONCLUSION

In this paper, a wireless mesh backbone structure is con-sidered for providing ubiquitous connectivity inside mobilevehicles. For efficient delivery of bursty traffic in vehicularcommunication, a fair channel access mechanism is designedover the IEEE 802.11s MCCA-based medium access protocol.A load estimation strategy at each roadside MP is used tocalculate the required channel share. The MAF limit, whichis the maximum channel access threshold in a DTIM interval,is adaptively tuned based on the required channel share. Thisprovides per-node proportional fairness, where the maximumreserved-channel amount in a DTIM interval is proportionalto the traffic load. Thus, the MP with more traffic gets morechannel share to fulfill its need. This type of channel reservationis necessary for the backbone of vehicular network, becauseof high traffic load with bursty nature. The effectiveness ofthe proposed scheme is analyzed using theoretical analysis andsimulation results.

REFERENCES

[1] IEEE Standard for Information Technology-Telecommunications andInformation Exchange Between Systems-Local and Metropolitan AreaNetworks-Specific Requirements Part 11: Wireless LAN Medium AccessControl (MAC) and Physical Layer (PHY) Specifications Amendment 6:Wireless Access in Vehicular Environments, IEEE Std. 802.11, Jul. 2010.

[2] IEEE 802.11: Wireless LAN Medium Access Control (MAC) and PhysicalLayer (PHY) Specifications, IEEE Std. 802.11, Jun. 2007.

[3] 802.11s-2011 IEEE Standard for Information Technology-Telecommunications and Information Exchange Between Systems-Localand Metropolitan Area Networks-Specific Requirements Part 11: WirelessLAN Medium Access Control (MAC) and Physical Layer (PHY) Spec-ifications Amendment 10: Mesh Networking, IEEE Std. 802.11s-2011,Sep. 2011.

[4] X. Wang and A. O. Lim, “IEEE 802.11s wireless mesh networks: Frame-work and challenges,” J. AdHoc Netw., vol. 6, no. 6, pp. 970–984,Aug. 2008.

[5] Cisco Aironet 1500 Series Wireless Mesh AP Version 5.0 DesignGuide. [Online]. Available: http://www.cisco.com/en/US/docs/wireless/technology/mesh/design/guide/MeshAP.html

[6] P. Cohen, H.-H. Hoang, and D. Haccoun, “Traffic characterization andclassification of users of land mobile communications channels,” IEEETrans. Veh. Technol., vol. VT-33, no. 4, pp. 276–284, Nov. 1984.


[7] B.-G. Choi, M. Kim, M. Y. Chung, and J. Y. Lee, “Adaptive hybridmedium access control scheme for traffic concentration problem in themesh portal of IEEE 802.11s-based wireless mesh networks,” in Proc. 5thInt. Conf. Ubiquitous Inf. Manage. Commun., 2011, pp. 63:1–63:8.

[8] M. S. Islam, M. M. Alam, C. S. Hong, and J.-S. Sung, “Enhanced channelaccess mechanism for IEEE 802.11s mesh deterministic access,” in Proc.IEEE Wireless Commun. Netw. Conf., Apr. 2010, pp. 1–6.

[9] K. Whitehouse, A. Woo, F. Jiang, J. Polastre, and D. Culler, “Exploitingthe capture effect for collision detection and recovery,” in Proc. 2nd IEEEWorkshop Embedded Netw. Sens., 2005, pp. 45–52.

[10] S. Chakraborty and S. Nandi, “Achieving MAC layer fairness in wirelessmesh backbone based vehicular network,” in Proc. 2nd Int. Conf. Comput.Commun. Technol., Sep. 2011, pp. 612–617.

[11] P. Beckman, S. Verma, and R. Rao, “Use of mobile mesh networksfor inter-vehicular communication,” in Proc. IEEE VTC-Fall, Oct. 2003,vol. 4, pp. 2712–2715.

[12] M. Cesana, L. Fratta, M. Gerla, E. Giordano, and G. Pau, “C-VeT theUCLA campus vehicular testbed: Integration of VANET and mesh net-works,” in Proc. Eur. Wireless Conf., Apr. 2010, pp. 689–695.

[13] A. Capone, M. Cesana, S. Napoli, and A. Pollastro, “MobiMESH: Acomplete solution for wireless mesh networking,” in Proc. IEEE Int. Conf.Mob. Adhoc Sensor Syst., Oct. 2007, pp. 1–3.

[14] C. Cicconetti, L. Lenzini, and E. Mingozzi, “Scheduling and dynamicrelocation for IEEE 802.11s mesh deterministic access,” in Proc. 5th IEEEAnnu. Commun. Soc. Conf. Sensor, Mesh Ad Hoc Commun. Netw., 2008,pp. 19–27.

[15] L. Lenzini, E. Mingozzi, and C. Vallati, “A distributed delay-balancingslot allocation algorithm for 802.11s mesh coordinated channel accessunder dynamic traffic conditions,” in Proc. IEEE 7th Int. Conf. Mob.Adhoc Sensor Syst., 2010, pp. 432–441.

[16] A. Krasilov, A. Lyakhov, and A. Safonov, “Interference, even with MCCAchannel access method in IEEE 802.11s mesh networks,” in Proc. IEEE8th Int. Conf. Mob. Adhoc Sensor Syst., 2011, pp. 752–757.

[17] J. Tomic and N. Neskovic, “An analysis of medium access controlin IEEE 802.11s networks,” in Proc. 19th Telecommun. Forum, 2011,pp. 513–516.

[18] T. Vanhatupa, M. Hännikäinen, and T. D. Hämäläinen, “Performancemodel for IEEE 802.11s wireless mesh network deployment design,” J.Parallel Distrib. Comput., vol. 68, no. 3, pp. 291–305, Mar. 2008.

[19] R. Parker and S. Valaee, “Vehicular node localization using received-signal-strength indicator,” IEEE Trans. Veh. Technol., vol. 56, no. 6,pp. 3371–3380, Nov. 2007.

[20] N. Bisnik and A. Abouzeid, “Delay and throughput in random accesswireless mesh networks,” in Proc. IEEE Int. Conf. Commun., Jun. 2006,pp. 403–408.

[21] F. Kelly, A. Maulloo, and D. Tan, “Rate control for communication net-works: Shadow prices, proportional fairness and stability,” J. Oper. Res.Soc., vol. 49, no. 3, pp. 237–252, Mar. 1998.

[22] NS-3.9 network simulator, ver. 3.9. [Online]. Available: http://www.nsnam.org/

[23] H. Arbabi and M. C. Weigle, “Highway mobility and vehicularAd-Hoc networks in NS-3,” in Proc. Winter Simul. Conf., Dec. 2010,pp. 2991–3003.

[24] Traffic software integrated system—Corridor simulation. [Online]. Avail-able: http://mctrans.ce.ufl.edu/featured/TSIS/

[25] G. Meng, S. H. Y. Wong, Y. Yuan, and S. Lu, “Characterizing flowsin large wireless data networks,” in Proc. 10th Annu. Int. Conf. Mob.Comput. Netw., 2004, pp. 174–186.

[26] R. Jain, D. M. Chiu, and W. Hawe, “A Quantitative Measure of Fairnessand Discrimination for Resource Allocation in Shared Computer Sys-tems,” Digital Equipment Corp., Maynard, MA, Tech. Rep. DEC-TR-301,Sep. 1984.

Sandip Chakraborty (S’11) received the B.Eng.degree from Jadavpur University, Kolkata, India, andthe M.Tech. degree from the Indian Institute of Tech-nology Guwahati, Guwahati, India. He is currentlyworking toward the Ph.D. degree with Indian Insti-tute of Technology Guwahati.

He has received a research fellowship from TATAConsultancy Services, India. His research interestsinclude wireless ad hoc and mesh networks, wire-less sensor networks, distributed algorithms, perfor-mance modeling of communication systems, etc.

Mr. Chakraborty is a Student Member of the IEEE Communications Societyand the Association for Computing Machinery.

Sukumar Nandi (SM’01) received the Ph.D. degreein computer science and engineering from Indian In-stitute of Technology Kharagpur, Kharagpur, India.

He is currently a Professor of computer scienceand engineering with Indian Institute of TechnologyGuwahati, Guwahati, India. He served as the GeneralVice Chair and General Cochair of the EighthInternational Conference on Distributed Computingand Networking in 2006, the International Confer-ence on Advanced Computing and Communicationsin 2007, and the Eighth International Conference

on Information Systems Security in 2012. He coauthored a book entitledTheory and Application of Cellular Automata (IEEE Computer Society). Hehas published around 200 journals/conference papers. His research interestsinclude traffic engineering, wireless networks, network security, and distributedcomputing.

Dr. Nandi is a Senior Member of the Association for Computing Machinery,a Fellow of The Institution of Engineers (India), and a Fellow of The Institutionof Electronics and Telecommunication Engineers (India).

IEEE 802.11s Mesh Backbone for Vehicular Communication: Fairness and Throughput

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