interference qos

8/12/2019 Interference QOS

1/10

166 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 1, JANUARY 2009

Interference-Aware QoS Routing forMulti-Rate Multi-Radio Multi-ChannelIEEE 802.11 Wireless Mesh Networks

Tehuang Liu, Student Member, IEEE, and Wanjiun Liao, Senior Member, IEEE

AbstractQoS routing in multi-channel wireless mesh net-works (WMNs) with contention-based MAC protocols is avery challenging problem. In this paper, we propose an on-demand bandwidth-constrained routing protocol for multi-radiomulti-rate multi-channel WMNs with the IEEE 802.11 DCFMAC protocol. The routing protocol is based on a distributedthreshold-triggered bandwidth estimation scheme, implementedat each node for estimating the free-to-use bandwidth on eachassociated channel. According to the free-to-use bandwidth ateach node, the call admission control, which is integrated intothe routing protocol, predicts the residual bandwidth of a pathwith the consideration of inter-flow and intra-flow interference.To select the most efficient path among all feasible ones, wepropose a routing metric which strikes a balance between thecost and the bandwidth of the path. The simulation results showthat our routing protocol can successfully discover paths thatmeet the end-to-end bandwidth requirements of flows, protectexisting flows from QoS violations, exploit the capacity gain dueto multiple channels, and incurs low message overhead.

Index TermsQoS routing, multi-channel, multi-rate, multi-radio, wireless mesh networks.

I. INTRODUCTION

W IRELESS mesh networks (WMNs) have receivedmuch attention in recent years thanks to such desirablefeatures as low up-front cost, ease of maintenance, robustness,

and reliable service coverage [1], [2]. In such networks, each

node plays both roles of a host and a router, and is typically

stationary and not power-constrained [3]. Some of the nodes

in the network may have directed connections to the wirednetworks, serving as gateways for other nodes to access the

Internet. Packets are forwarded in a multi-hop fashion toand from the gateway nodes. One crucial issue in WMNs

is the capacity degradation problem [3] due to interference

between wireless links. Previous work [4], [5] shows thatemploying multiple non-overlapping channels is an effective

approach to improving the network capacity. However, to

effectively exploit the capacity gain available with multiple

Manuscript received April 6, 2007; revised March 22, 2008; acceptedJuly 27, 2008. The associate editor coordinating the review of this paperand approving it for publication was Q. Zhang.

This work was supported in part by the Excellent Research Projects ofNational Taiwan University, under Grant Number 97R0062-06, and in partby National Science Council (NSC), Taiwan, under Grant Number NSC96-2628-E-002-003-MY3.

The authors are with the Department of Electrical Engineering and theGraduate Institute of Communication Engineering, National Taiwan Univer-sity, Taipei, Taiwan (e-mail: [email protected]).

Digital Object Identifier 10.1109/T-WC.2009.070369

channels, existing protocols and algorithms for single-channel

environments need to be redesigned.

In this paper, we focus on the QoS routing problem in

multi-channel WMNs based on IEEE 802.11 DCF. Even insingle-channel WMNs, with IEEE 802.11 DCF, a contention-

based MAC protocol, QoS routing is very challenging. The

root of the problem is how to precisely estimate the residual

bandwidth of a routing path for QoS commitments. If the

residual bandwidth of a path is overestimated, too manyflows

may be admitted into the system, depriving existing flows of

the reserved bandwidths. On the other hand, a conservative

estimation may provide better protection for existing flows,

at the expense of degradation of channel utilization and

system throughput. As shown in [6], the end-to-end bandwidth

calculation problem in single-channel TDMA-based wirelessnetworks is NP-hard. This implies that the QoS routing

problem in WMNs with IEEE 802.11 DCF and multiple

channels is even more complicated. When channel diversity

is present, the factors which determine whether or not two

nodes in a WMN can communicate with each other include

not only their locations but also the set of channels theyuse. In addition, the interference relationship between links

depends on the channels on which they operate. Therefore,

for a routing path, inter-flow and intra-flow interference [7]

must be calculated according to the channels used along the

path to estimate its capacity. Moreover, to increase the capacitygain due to multiple channels, the routing algorithm must be

able to evenly distribute traffic load across nodes as well aschannels while striking a good balance between maximizing

system throughput and meeting the QoS requirements offlows

[8].

The channel assignment problem is usually considered a

companion issue for routing in multi-channel WMNs [4], [9],[10]. The interplay between routing and channel assignment

in MR-MC WMNs can be found in our previous work in

[8], [11]. The channel assignment problem is to bind eachradio interface to a channel such that the network capacity

is maximized. Since two neighboring nodes can communicate

with each other only if they are assigned a common channel,

the channel assignment controls the network topology and

consequently restricts the possible routes between any pair

of nodes in the network. Therefore, a well-designed routing

algorithm for multi-channel WMNs may become useless if

an improper channel assignment algorithm is used. However,

when the traffic demand is dynamic and not predictable, it

1536-1276/09$25.00 c 2009 IEEE


2/10

LIU and LIAO: INTERFERENCE-AWARE QOS ROUTING FOR MULTI-RATE MULTI-RADIO MULTI-CHANNEL MESH NETWORKS 167

is meaningless to solve the routing problem along with the

channel assignment problem for future multicast sessions in

advance [10]. In this paper, we develop an on-demand QoSrouting protocol for multi-channel WMNs with a dynamic traf-

fic model (where QoSflows arrive at the network dynamically

without any prior knowledge of future arrivals), so we separate

the routing problem from the channel assignment problem and

assume that the channel assignment is given and static [12].

Previous research efforts on routing in WMNs focus mainlyon best-effort routing (e.g., [5], [9]) or QoS routing for single-

channel multi-hop wireless networks (e.g., [14], [15]). Yangand Kravet [14] show that the available bandwidth that a

node can use without causing QoS violations to existing

flows (which pass through nodes within its interference range)

is jointly determined by all nodes within its carrier-sensing

range, not solely by this node itself. They then proposean admission control framework, called Contention-aware

Admission Control Protocol (CACP), to support bandwidth-

constrained routing in single-channel ad hoc networks. Gupta

et al. [15] propose a QoS routing mechanism in single-channel

ad hoc networks, called Interference-aware QoS Routing(IQRouting), which relies on the concept of cliques in theconflict graph for admission control. Draves et al. [13] design

a routing metric, called Weighted Cumulative Expected Trans-

mission Time (WCETT), to address the impact of co-channel

interference on routing in multi-channel WMNs. The WCETT

metric is a weighted sum of end-to-end delay and intra-flowinterference. However, the calculation of WCETT does not

take into account the inter-flow contention. As a result, the

routes selected by this metric may go through congested

areas [16]. Considering the support of multiple channels,

Raniwala et al. [9] highlight the dependency between the

channel assignment problem and the routing problem in multi-channel multi-radio WMNs. They then propose a set of cen-

tralized channel assignment, routing, and resource allocationalgorithms to ensure that the resulting available bandwidth

on each radio is at least equal to its expected traffic load.

QoS routing in IEEE 802.11 multi-channel WMNs is even

more challenging since it needs to choose less congested paths

to combat the uncertainty of the bandwidth estimation for

QoS-constrainted flows. However, QoS routing alone cannot

ensure QoS guarantees for flows. Typically, it must incorporate

an admission control mechanism to protect existing flows

from QoS violations. Tang et al. [10] propose a centralized

QoS routing algorithm for multi-channel WMNs. However,they formulate this problem as a linear programming (LP)problem with some given global knowledge, such as the routes

of all existing flows, the network topology, the interference

relationship between any two nodes in the network, and the

bandwidth demands of flows. As a result, it is difficult to

implement that solution in real-world networks. Hu et al. [17]design a distributed link scheduling algorithm to perform call

admission control in multi-channel multi-radio WMNs. Since

their algorithm is based on a TDMA MAC layer, it is not

applicable to IEEE 802.11 DCF WMNs.

In this paper, we propose an on-demand QoS routing

protocol for multi-rate multi-radio multi-channel (MR2-MC)WMNs based on the IEEE 802.11 DCF MAC protocol. We fo-

cus on bandwidth-constrained flows. Each node in the network

is equipped with multiple radios tuned to different channels,

and may communicate with different neighbors at different

data rates to combat channel deterioration. Our routing pro-tocol is based on a threshold-triggered bandwidth estimation

scheme, with which each node can estimate the free-to-use

bandwidth on each associated channel (i.e., the channel to

which this node has a radio tuned). This bandwidth estimation

scheme uses two configurable parameters for tradeoff selection

between message overhead and estimation accuracy. Accord-ing to the free-to-use bandwidth estimated at each node,

the call admission control, which is a distributed mechanism

and can be integrated into the routing protocol, predicts

the residual bandwidth of a path with the consideration of

the inter-flow and intra-flow contentions. Since a bandwidth-constrained path may be costly in terms of radio resource, we

propose a routing metric, intending to strike a balance betweenthe cost and the width (i.e., bandwidth) of the path. The

performance of the proposed routing algorithm is evaluated

via ns-2 simulations [18]. The simulation results show that

our routing protocol can successfully find paths satisfying the

end-to-end bandwidth requirements of flows, protect existingflows from QoS violations, exploit the capacity gain due to

multiple channels, and incur low message overhead. To our

best knowledge, this is the very first paper presenting a fully

distributed, on-demand QoS routing protocol for MR2-MC

WMNs based on the IEEE 802.11 DCF MAC protocol.The rest of the paper is organized as follows. We introduce

the system model in Section II. Section III presents the

proposed routing protocol. Section IV shows the simulation

results. We conclude the paper in Section V.

I I . SYSTEM M ODEL

We consider an MR2

-MC WMN with the IEEE 802.11DCF MAC protocol. Nodes in the network are all stationary

and act as traffic aggregation access points (or called Transit

Access Points, TAPs [3]), providing network connectivity to

end-user mobile stations within their coverage areas. Packets

are forwarded via multi-hop relaying. Each node is equipped

with multiple radios and the number of radios for each node

may be different. For the sake of efficiency, all radios of a

node are tuned to different channels. We consider a dynamic

traffic model and adopt static channel assignment strategies

(as in [4], [9], [12]) which last permanently or for a long

period of time (for example, several hours or days). Two nodes

are said to be one-hop neighbors (or neighbors for short) onchannel k if they have a radio operating on channel k andfall within the transmission range of each other. The multi-

rate capability in the PHY layer is also considered in ourmodel, i.e., nodes in the network may communicate at different

data rates, depending on the distance between them, the radio

signal quality, and the set of modulation and coding schemes

available in the system.We assume that for each node in the network, the list of its

neighbors on each channel and the transmission profile (i.e.,

the data rate and the packet loss rate) on the link between

itself and each of its neighbors are available (e.g., from the

ranging algorithm and the rate adaptation algorithm in thephysical layer). We also assume that for each flow, the end-to-

end bandwidth requirement can be arbitrary, but not dividable


3/10


Fig. 1. A seven-node WMN.

due to difficulties in packet fragmentation and reassembly. A

routing path between the source node and the destination node

is specified with a sequence of links. Let (i, j)k denote thelink that operates on channel k and is incident on nodes i

and j. For example, in the network shown in Fig. 1 wherethe number on a link denotes the channel used on this link,

(A, B)1 and(A, B)2 are two links operating on channel 1 andchannel 2, respectively, between nodesA and B. Thus, we can

easily identify a path from node A to node D in Fig. 1, say,

{(A, B)2 (B, E)1 (E, D)4}. Note that the discussion ofthis paper is confined to single-path routing since multi-pathrouting may cause out-of-order arrivals of packets.

In a multi-channel multi-radio WMN, when a node needs

to broadcast a control message to its neighbors for cer-tain network management operations (e.g., routing [5], load-

balancing channel assignment [4], topology control [19], and

flow redirection [19]), it can simply duplicate the message

and broadcast it on each associated channel. However, this

approach is inefficient and may incur high control overhead.

An alternative solution [20] is to let nodes periodically ren-

dezvous on a common channel to exchange control messages,

but this approach requires synchronization between nodes.Shi et al. [21] propose a channel coordination protocol for

exchanging control messages between nodes in CSMA wire-

less networks without the reliance on synchronization. The

idea is to let nodes that have no data packets to send or

receive keep listening on a dedicated control channel. As aresult, it may suffer the missing neighbor problem (or called

the deafness problem) [21]. A simple method widely adopted

by existing papers [4], [5], [19] is to employ an extra radio

tuned to a dedicated control channel permanently such that a

node can broadcast control messages to its neighbors via this

radio. This approach generates lower message overhead, needs

no synchronization between nodes, and avoids the missingneighbor problem. In this paper, for simplicity, we adopt the

last approach. Note that the RTS, CTS, ACK control frames of

IEEE 802.11 DCF are still transmitted on the data channels.

III. INTERFERENCE-AWARE Q OS ROUTING

A. Residual Bandwidth Estimation

Different from best-effort routing algorithms, QoS routing

must cooperate with call admission control to protect existing

QoS flows in the network. Call admission control regards a

path as feasible if its end-to-end residual bandwidth meets

the required bandwidth of the flow. In this section, we pro-pose a threshold-triggered approach that allows each node to

estimate the residual bandwidth on each associated channel

and calculate the sustainable sending rate of a path (presented

in Section III-C) based on this estimation. Our bandwidth

estimation scheme is similar to a framework introduced in [14]but is applicable to multi-channel multi-rate environments and

provides parameters to balance the tradeoff between control

message overhead and estimation accuracy.

Due to the broadcast nature of wireless networks, the

residual bandwidth on a certain channel that a node can use

is not simply determined locally, but rather by the channelstatus perceived by the nodes located within its interference

range [6], [14] (or called its co-channel interfering neighbors

[10]). To capture this relationship among nodes for estimating

the residual bandwidth on a wireless channel, we introduce

two types of assessments on channel utilization: local resid-

ual bandwidth (LRB) and interference-neighborhood residual

bandwidth (IRB). The LRB on a channel is obtained via

passively monitoring the activities on this channel locally. The

IRB on a channel corresponds to the smallest one among the

LRBs on the channel perceived by all co-channel interfering

neighbors of this node. It is IRB that a node can use without

causing QoS violations [14].Each node maintains a table to store the LRB on each

associated channel. We let LRB TABLEi denote this table of

node i, and let LRB TABLEki denote the entry for channel

k in LRB TABLEi, i.e., the LRB on channel k. Each node i

periodically updates the value of LRB TABLEki to the amount

of air time that is observed as idle on channel k during aperiod of time Tvia passively monitoring the local network

activities on this channel. Note that the channel is said to be

busy for a node if (i) this node is transmitting or receiving

on the channel or (ii) the channel is perceived as busy

by physical carrier sensing or virtual carrier sensing. Let

LRB MEASUREki denote the latest measured LRB on channel

k for node i. Node i updates its LRB TABLEi using the

exponential weighted average as follows.

LRB TABLEki = (1 )LRB TABLEki

+ LRB MEASUREki ,

where 0 1.The second table maintained by each node i in the net-

work, denoted by LRB COLLECTIONi, is used to store the

LRB values reported by its co-channel interfering neigh-

bors. We let LRB COLLECTIONki denote the set of en-

tries in LRB COLLECTIONi for channel k, which are re-ported by node is co-channel interfering neighbors, and let

LRB COLLECTIONki(j) denote the LRB value on channel k ,which is reported by node j , a co-channel interfering neighbor

of node i. To notify the co-channel interfering neighbors of

the measured LRB, each node follows a threshold-triggered

approach, which has two configurable parameters, namely,

(i) Reporting Distance (RD), specifying how far (in terms

of hops) a nodes LRB measurement is flooded, and (ii)

Reporting Threshold (RT), indicating the condition under

which the flooding for a nodes LRB measurement is triggered.

Specifically, each node floods its LRB measurement on a

certain channel (via the control radio operating on the controlchannel) only when the fluctuation in the measured LRB,

compared with the last advertised value, exceeds RT times


4/10


T, and the flooding is restricted to its RD-hop neighborhood

on the control channel.

Based on LRB TABLEi and LRB COLLECTIONi, node i

estimates the IRB on channel k, denoted by IRB TABLEki,by determining the co-channel interfering neighbor which

perceives the busiest channel status, i.e.,

IRB TABLEki min(x: x{LRB TABLEki }

LRB COLLECTIONki).

Note that we let IRB TABLEi denote node is IRB table,

which stores the most up-to-date IRB estimated on each

channel. Our approach, a threshold-triggered one, provides

another option for the system administrator to manage the

tradeoff between message overhead and estimation accuracy

using parameters RD and RT. In Section IV, we will showhow these two parameters influence the system performance

in terms of QoS satisfaction and control message overhead via

ns-2 simulations.

B. Bandwidth Consumption Prediction

Due to the shared nature of the wireless media, nodes on

a multi-hop path may contend with each other for wireless

access. There are two types of bandwidth consumption for

a flow going through a node along a given path, including:

1) the amount of air time spent in transporting frames across

the link on which this node is incident, and 2) the amount

of air time occupied by the transmissions of this nodes co-

channel interfering neighbors on the path. In the following,we introduce theexpected amount of busy air time (EBT) and

the cumulative expected busy time (CEBT), which represent

the first type of bandwidth consumption and the sum of these

two types of bandwidth consumption, respectively. The EBT

for a link with respect to a flow is defined as the amount of

air time needed for successfully sending one frame of this

flow on this link. Let R(i,j)k and PLR(i,j)k denote the data

rate and the packet loss rate for link (i, j)k, respectively, andEBT(i,j)k,f, the EBT value for link(i, j)k with respect to flowf. We have

EBT(i,j)k,f(TRTS+ TCTS+ Lf

R(i,j)k+TACK)

(1 PLR(i,j)k )1,

where TRTS, TCTS, TACK are the amounts of air timefor transmitting the RTS, CTS, and ACK control frames,

respectively, and Lf is the packet size of flow f. The above

equation follows because the expected number of Bernoulli

trials to get the first success with parameter 1 PLR(i,j)k is(1 PLR(i,j)k)

1. Note that here we ignore the additional

channel busy time consumed by the losses of RTS and CTS

frames since such losses are relatively rare.

Since intra-flow contention stems from the transmissions

on the set of links operating on the same channel, let

CEBT(i,j)k,f,p denote the CEBT for link (i, j)k of path p withrespect to flowf . Thus, we have

CEBT(i,j)k ,f,p=

x{I(i,j)kp}

EBTx,f, (1)

(a)

(b)

Fig. 2. Examples to demonstrate the calculation of CEBT.

where I(i,j)k is the set of co-channel interfering links of

link (i, j)k, and {I(i,j

)kp} represents the set of co-channel

interfering links of link (i, j)k that are included in path p.Note that link (m, n)t is said to be a co-channel interferinglink of link(i, j)k ift = k and at least one of nodes m and nis the co-channel interfering neighbor of node i or j . To obtain{I(i,j)kp}, we approximate the set of co-channel interferingneighbors of nodei on channelk by LRB COLLECTIONki. In

other words, if there is an entry inLRB COLLECTIONki whichis reported by node j, then j is regarded as the co-channel

interfering neighbor of node i. Therefore, the interference

neighborhood of node i on channel k is approximated to

its RD-hop neighborhood. Note that (1) indicates the worst

case of the bandwidth consumption because spatial reuse may

allow overlaps between the time periods occupied by thetransmission activities on co-channel interfering links. As an

example, we demonstrate the calculation of CEBT for the

paths shown in Fig. 2. There are six nodes comprising a

routing path in this example, namely node A to node F. The

number on each link indicates the channel on which this linkoperates, and each dashed line connecting two links indicates

that the two links interfere with each other. Suppose that

EBT(A,B)2,f = 0.5, EBT(B,C)1,f = 0.2, EBT(B,C)2,f = 0.3,EBT(C,D)1,f = 0.1, EBT(D,E)1,f = 0.3, EBT(E,F)1,f = 0.1,and EBT(E,F)3,f = 0.4. Consider path p1 = {(A, B)2 (B, C)1 (C, D)1 (D, E)1 (E, F)1}, shown in

bold lines in Fig. 2(a). Take link (C, D)1 ofp1 for example.Since it interferes with links (B, C)1, (D, E)1, and (E, F)1of p1, CEBT(C,D)1,f,p1 = EBT(B,C)1,f + EBT(C,D)1,f +EBT(D,E)1,f+EBT(E,F)1,f= 0.7. Consider another path p2={(A, B)2 (B, C)2 (C, D)1 (D, E)1 (E, F)3},as shown in bold lines in Fig. 2(b). Now link (C, D)1 onlyinterferes with link (D, E)1 of p2, so CEBT(C,D)1,f,p2 =EBT(C,D)1,f+EBT(D,E)1,f= 0.4.

C. Path Feasibility Test

Let SRp,fdenote the sustainable sending rate (bps) on path

pfor flowf. Obviously,SRp,f is dominated by the bottleneck


5/10


link of path p, i.e.,

SRp,f= Lf

T min

(i,j)kp(IRB(i,j)k T

CEBT(i,j)k,f,p), (2)

where IRB(i,j)k min(IRB TABLEki ,IRB TABLE

kj ) repre-

sents the minimum amount of free-to-use air time for link

(i, j)k and [0, 1] is a tunable system parameter represent-

ing the percentage of radio resource that cannot be utilized dueto MAC overhead. Accordingly, a pathp is considered feasible

for flow f if SRp,f is larger than the requesting sending rateof flow f (denoted by RRf), i.e., SRp,f RRf. The calladmission control integrated into the routing protocol (shownin Section III-E) blocks a flow if no feasible paths are found

for this flow. Equation (2) also ensures that a path is feasible

if and only if all its sub-paths are feasible. In other words,

the routing protocol will discard a candidate partial path if

the path fails in the feasibility test (or it would lead to an

infeasible path ultimately otherwise).

D. Routing MetricIt is shown in [5], [15], [16] that routing metrics for wireless

networks substantially affect the system performance in terms

of achievable throughput, delay, and QoS satisfaction. While

the feasibility of a discovered path is determined by (2), a

routing metric is still needed to further judge the goodness of a

feasible path. An intuitive way is to use the residual end-to-endbandwidth as the metric. Choosing a path with larger residual

bandwidth can reduce the probability of QoS violations due tothe uncertainty of the predicted bandwidth, but this may be at

the expense of consuming more radio resource. For example,

the routing algorithm may select a wider path (i.e., path with

a larger bandwidth) but with a larger hop count so as to keepaway from congested areas in the network. Jia et al. [22]

show that taking the widest (but maybe more costly) path may

not benefit the long-run system performance in terms of the

admission ratio. This motivates us to develop a routing metric

striking a balance between the width and the cost of the path.

To reflect the width of a path, we make use of the minimum

sustainable sending rate of the path given by (2). The cost

of a path can be reflected as follows. Since each transmis-

sion activity at a node takes up the capacity among all co-channel interfering neighbors of this node, we use the ETB

of link(i, j)k times the number of the co-channel interfering

neighbors of nodes i and j on channel k to represent thetransmission cost of link (i, j)k. This product reflects thebandwidth consumption from the perspective of the whole

network, rather than from a single link as ETB. The cost of

path p for flowf, denoted by Cp,f, is then defined as the sum

of the costs over all links ofp, i.e.,

Cp,f=

(i,j)kp

EBT(i,j)k,f |NODE(LRB COLLECTIONki)

NODE(LRB COLLECTIONkj )|, (3)

whereNODE(LRB COLLECTIONkx)denotes the set of nodesthat each have an entry in LRB COLLECTIONkx, i.e.,

NODE(LRB COLLECTIONkx) is used to approximate the co-channel interfering neighbors of node x on channel k.

Based on the width and the cost of the path described above,

we further define the Path Efficiency Factor (PEF) of path p

with respect to flowf as follows:

PEFp,f= SRp,f

Cp,f. (4)

The PEF of a path can be interpreted as the benefit-to-

cost ratio of the path. The higher the value of PEF, the

lower the cost required for obtaining the same amount ofbenefit. In this paper, we use PEF as the routing metric. The

proposed routing algorithm (presented in the next subsection)

is designed to select the feasible path with the largest PEF

among all discovered paths.

E. QoS Routing Protocol

Since the sustainable sending rate of a path can be obtained

by (2) only when the set of links comprising this path is

completely specified, we next discuss how to determine a path

by the routing protocol. Due to the failure ofBellmans Prin-

ciple of Optimality [15], it is hard for distributed algorithmsto find optimal paths in wireless networks. In this paper, we

propose an on-demand routing protocol which implements a

greedy routing algorithm based on the PEF routing metric.

This routing algorithm is fully distributed and can be dividedinto the following three phases.

1) Route Request (RREQ) Packet Flooding:To determine a

route for flowf, the source node initiates the route discovery

procedure by flooding a route request (RREQ) packet on thecontrol channel. The RREQ packet carries the information

about the partial path which has been discovered by far, along

with the profile of this flow. The partial path stored in the

RREQ packet is updated at each hop as the packet propagates

from the source to the destination. Without loss of generality,

we assume that n1 and nm are the source node and the desti-

nation node for a new flowf, respectively, and that an RREQ

packet has traveled along a path Px1 = {(n1, n2)c1 (n2, n3)c2 . . . (nx2, nx1)cx2}, where 2 < x , where

EBT LISTx1= {EBTi,f :i = (nj , nj+1)cj ,

max(1, x RD 2) j x 2},

CEBT LISTx1= {CEBTi,f,Px1 :i = (nj , nj+1)cj ,

max(1, x RD 2) j x 2},

IRB LISTx1 = {IRBi :i = (nj, nj+1)cj ,

max(1, x RD 2) j x 2},

and IN TABLEnx1 is a table that contains the number of

co-channel interfering neighbors of node nx1 on each of

its associated channels (except the control channel). LetIN TABLEknx1 denote the entry in IN TABLEnx1 for channel


6/10


k. SinceNODE(LRB COLLECTIONki) is used to approximate

the set of co-channel interfering neighbors of node i on

channel k, we have

IN TABLEknx1 =|NODE(LRB COLLECTIONknx1

)|.

2) Path Selection Upon Receiving an RREQ Packet: When

nodenx

receives an RREQ broadcast bynx1

, it performs the

path selection algorithm, which is formally stated in Algorithm

1. First, it checks if its ID appears in the partial path indicated

in this RREQ packet. If this is the case, it discards this packetto prevent a loop. Otherwise, it determines the channels (ex-

cluding the control channel) which are common to itself and its

upstream node (i.e., nx1). If there are no such channels, the

node discards this RREQ packet. Otherwise, for each common

channel k, node nx calculates the PEF for each new partial

path, namely Pkx = {(n1, n2)c1 (n2, n3)c2 . . . (nx2, nx1)cx2 (nx1, nx)k}, based on the informationcarried in the RREQ packet. Note that here we approximatethe cost of a path as follows such that node nx1 does not

need to transmit the whole NODE(LRB COLLECTIONknx1)to node nx.

Cp,f=

(i,j)kp

EBT(i,j)k,f IN TABLEki .

If each common channel leads to an infeasible path (i.e.,

SRPkx ,f < RRf), this RREQ is discarded. Otherwise,

node nx determines the channel t which leads to the

highest PEF among all feasible partial paths, i.e., t =arg maxkCPEFPkx ,f, where C = {i : SRPix,f RRf}. Ifthere is a tie, the shortest one wins. If there is still a tie, the

node just randomly selects one channel fromC. After channel

t is determined, if node nx is not the destination, it updatesthe partial path to

Px = {(n1, n2)c1 (n2, n3)c2 . . .

(nx2, nx1)cx2 (nx1, nx)cx1},

where cx1=t, and updates the METRIC CALCULATIONfield of the RREQ packet based on this new partial path. Node

nx then rebroadcasts this RREQ packet to its neighbors on

the control channel. If node nx is the destination, it stores

< Ptx, PEFPtx,f > and waits for a pre-defined period of time

to learn more feasible routes by receiving more RREQ packets.

3) Route Reply (RREP) Packet Reply: After timeout, thedestination selects the path with the highest PEF amongall discovered paths, and then unicasts a ROUTE REPLY

(RREP) packet back to the source. Ties are broken at ran-

dom. The RREP packet carries the information about the

selected path, i.e., Pm = {(n1, n2)c1 (n2, n3)c2 . . . (nm1, nm2)cm1}. Each intermediate node ni onthe selected path receiving an RREP packet knows about

which channels to use (i.e., ci1 and ci) to communicate

with the previous hop node (i.e., ni1) and the next hop

node (i.e., ni+1) on the path. The forward and reverse paths

are then established accordingly. The source node can start

transmission as soon as it receives an RREP packet. If thesource node receives more than one RREP packet, which may

be replied by different gateways in the network, it will update

the routing table and switch to a better path (with a larger

PEF).

IV. PERFORMANCEE VALUATION

In this section, we conduct ns-2 [18] simulations to evaluatethe performance of the proposed routing protocol and study

how parameters,RD, andRTaffect the system performance.

A. Simulation Settings

The network considered in the simulation consists of 80

nodes (two of which are selected as gateways to the Internet).

Nodes are randomly placed in a 1500m1500m square area.There are 12 non-overlapping channels available in the system,

including 11 data channels and one control channel. Each

node is equipped with five IEEE 802.11a radios, one control

radio and four data radios. To decouple the effect of the

channel assignment algorithm, the data radios of each nodeare randomly assigned four different data channels. The data

rate used by any two neighboring nodes for communication is

determined by the distance between them. In the simulation,the channel rates of 54, 36, 18, and 6 Mbps are considered,

and the corresponding transmission ranges are set to 89, 119,178, 238 m [23], respectively. The packet error rate on the

link between any two adjacent nodes is randomly selected

from{0.1%, 0.5%, 1%, 5%, 10%} with equal probability. InIEEE 802.11 systems, the interference ranges and the optimum

carrier sensing ranges for different channel rates are very close

[24]. Therefore, we use a single interference range of 450mfor all channel rates for simplicity. A radio of a node is said

to be an orphan radio if this node cannot communicate withany other node via this radio even at the lowest data rate.

An orphan radio occurs when either of the following twoconditions holds: (i) the radio is assigned a channel whichis not used by any other nodes in its transmission range (at

the lowest data rate), and (ii) there are no other nodes placed

in the transmission range of this radio (at the lowest data

rate). If there is a node whose four data radios are all orphan

radios, the network is regarded as disconnected. In case the

randomly generated topology and channel assignment lead toa disconnected network, we regenerate the topology and the

channel assignment until a connected network is obtained.In the simulation, a new QoS flow (with a bandwidth

requirement of 1.5 Mbps) destined to the Internet is started

every two seconds at a randomly selected non-gateway node.

The frame size of each flow is set to 1000 bytes. The total

simulation time is 70 seconds. Each result is obtained by

averaging over 20 runs. Note that the settings of parameters

, RD, and RT are varied in each scenario to study their

influences on the system performance. The other system

parameters remain fixed, including T= 100 ms and = 0.5.

B. Performance Metrics

Bandwidth Satisfaction Index (BSI): This index is used

to indicate how well the bandwidth requirement of a flow is

satisfied. BSI is calculated on a per-flow basis and defined as

BSI=

1, ifTa T1r >1Ta T

1r , otherwise


7/10


ALGORITHM1The routing algorithm performed at node nx

1: ifnx appears on Px1 then2: discard this RREQ packet;3: exit;4: else5: Common Channel Set {k: k Freq(nx1) Freq(nx)}, where Freq(i) is the set of channels used by node i, excluding the control channel;6: if Common Channel Setis empty then7: discard this RREQ packet;

8: exit;9: else10: C {};11: foreach channel k Common Channel Set do12: Pkx {(n1, n2)c1 (n2, n3)c2 . . . (nx2, nx1)cx2 (nx1, nx)k};13: CEBT(nx1,nx)cx1 ,f,P

kx

EBT(nx1,nx)k,f+

max(1,xRD2)jx2,cj=kEBT(nj ,nj+1)cj ,f

;

14: fori = 0, 1, . . . , R D do15: ifcxi2 = k then16: CEBT(nxi2,nxi1)cxi2 ,f,P

kx

EBT(nx1,nx)k,f+ CEBT(nxi2,nxi1)cxi2 ,f,Px1;

17: end if;18: end for;

19: SRPkx ,f min(SRPx1,f,

LfT

mini=0,1,...,RD+1(IRB(nxi1,nxi)cxi1

T

CEBT(nxi1,nxi)cxi1

,f,Pkx

));

20: CPkx ,f CPx1,f+ IN TABLE

knx1

EBT(nx1,nx)k,f;

21: PEFPkx ,f

SR

Pkx ,f

CPkx ,f;

22: ifS RPkx ,f

RRf then

23: C C {k};24: end if;25: end for;26: end if;27: end if;28: ifCis empty then29: discard this RREQ packet;30: exit;31: else32: t argmaxkCPEFPkx ,f

, where if there is a tie, the shortest one wins.;

33: end if;34: ifnx= nm then35: store< Ptx, PEFPtx,f

>;36: wait for an appropriate amount of time to receive RREQ packets to learn more feasible routes;

37: else38: cx1 t;39: update the partial path in the RREQ packet toPx {(n1, n2)c1 (n2, n3)c2 . . . (nx2, nx1)cx2 (nx1, nx)cx1};40: SRPx,f S RPtx,f;41: CPx,f CPtx ,f;42: ifx RD 2> 0 then43: EBT LISTx EBT LISTx1 {EBT(nxRD2,nxRD1)cxRD2 ,f

};

44: CEBT LISTx CEBT LISTx1 {CEBT(nxRD2,nxRD1)cxRD2 ,f,Px1};

45: IRB LISTx IRB LISTx1 {IRB(nxRD2,nxRD1)cxRD2};

46: end if;47: EBT LISTx EBT LISTx+ {EBT(nx1,nx)t,f};48: CEBT LISTx CEBT LISTx+ {CEBT(nx1,nx)t,f,Ptx};

49: IRB LISTx IRB LISTx+ {IRB(nx1,nx)t};

50: IN TABLEknx |NODE(LRB COLECTIONknx

)| for each k Freq(nx);51: replace the METRIC CALCULATION field of the RREQ packet by

< S RPx,f, CPx,f, EBT LISTx, CEBT LISTx,IRB LISTx,IRB TABLEnx ,IN TABLEnx >;52: rebroadcast this new RREQ packet to its neighbors on the control channel;53: end if;54: exit;

where Ta is the achieved end-to-end throughput and Tr is the

required end-to-end throughput of the flow. BSI is a value

between zero and one, and goes up when the bandwidth

requirement of the flow is more satisfied. We define the

saturated BSIas the average BSI offlows in the steady state.

System saturated throughput: We define the systemthroughput as the aggregate offlows average throughputs. The

system saturated throughput is defined as the average system

throughput in the steady state.

Saturated end-to-end delay: The end-to-end delay of a

packet is defined as the time between when the packet is

sent by the source node and when the packet is successivelyreceived by the destination node. We define the saturated end-

to-end delay as the average end-to-end delay in the steady

state.

Normalized saturated message overhead: The normal-


8/10


21

41

61

81

02

22

42

62

82

troffe-tseb

TTECW

troffe-tseb

CLB

FEPCLBTTECWsystem

saturatedthroughput(Mbps)

Fig. 3. System saturated throughput for Scenario 1.

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0.1

troffe-tseb

TTECW

troffe-tseb

CLB

FEPCLBTTECW

averagesaturatedBSI

Fig. 4. Average saturated BSI for Scenario 1.

0

01

02

03

04

05

06

07

08

09

001

troffe-tseb

TTECW

troffe-tseb

CLB

FEPCLBTTECW

averagesaturated

end-to-enddelay(ms)

Fig. 5. Average saturated end-to-end delay for Scenario 1.

ized message overhead is defined as the amount of control

messages (including messages for bandwidth estimation and

routing) sent by nodes per second divided by the system

throughput. The normalized saturated message overhead is

defined as the average normalized message overhead in thesteady state.

C. Simulation Results

1) Scenario 1: In the first scenario, we let = 25%,RD = 3, and RT = 10%, but vary the routing metricswith PEF, BLC [5], and WCETT [13] for the QoS routing

protocol presented in Section III-E. In other words, we let

nodes choose the best partial path (among all feasible partial

paths) to forward data according to PEF, BLC or WCETT. We

also simulate the cases of best-effort routing by turning off the

call admission control (i.e., skipping the path feasibility testso that no flow is blocked). Fig. 3 depicts the system saturated

throughputs against different routing metrics for different

21

41

61

81

02

22

42

62

82

03

%03%52%02%51%01

retemarap

systemsaturatedthroughput

(Mbps)

Fig. 6. System saturated throughput for Scenario 2.

46.086.0

27.0

67.0

08.0

48.0

88.0

29.0

69.0

00.1

%03%52%02%51%01

retemarap

averagesaturatedBSI


approaches. When call admission control is activated, the PEFmetric achieves the highest throughput. This is because the

WCETT metric will not lead to load-balancing due to its

unawareness of inter-flow contention while the BLC metric

does not properly account for the path cost in multi-channelsystems. We also find that de-activating call admission controlincreases the system throughput. This is because best-effort

routing tends to consume all channel capacity, leading to better

channel utilization, while with call admission control, only

when sufficient bandwidth is available will flows be admitted.

Fig. 4 shows the saturated BSI against different routing

metrics. Clearly, when call admission control is activated,the saturated BSI is very close to one for each approach

(about 0.948, 0.965, and 0.974 for WCETT, BLC, and PEF,

respectively). This means that the proposed routing algorithm

and call admission control can effectively find paths satisfying

the bandwidth requirements of flows and protect existing

flows from QoS violations. Fig. 5 plots the average saturated

end-to-end delay for each approach. We observe that PEF

results in the lowest delay. In addition, when call admission

control is turned off, congestion occurs, and consequently,

delay increases substantially.

2) Scenario 2: In the second scenario, we study the system

performance with different settings of . We vary from

10% to 30%, but fix RD and RTat 3 and 10%, respectively.

The routing metric is PEF. The simulation results are shown

in Figs. 6 to 8. When is small, more flows are admitted

into the system, resulting in better channel utilization and

thus higher system throughput (see Fig. 6). However, as more

flows contend for the network resource, interference between

paths becomes severe. As a result, the system exhibits a


9/10


0

5

01

51

02

52

03

53

04

54

%03%52%02%51%01

retemarap

averagesaturatedend-to-end

delay(ms)


08.0

28.0

48.0

68.0

88.0

09.0

29.0

49.0

69.0

89.0

00.1

%04=TR%03=TR%02=TR%01=TR

averagesaturatedBSI

1=DR

2=DR

3=DR


61

81

02

22

42

62

82

03

23

43

63

83

04

%04=TR%03=TR%02=TR%01=TRaveragesaturated

end-to-enddelay(ms)

1=DR

2=DR

3=DR


lower saturated BSI (see Fig. 7) and larger delay (see Fig.

8) with a small . This concludes that can be used to

adjust the tradeoff between system throughput and bandwidth

satisfaction (or between system throughput and end-to-enddelay).

3) Scenario 3: In this scenario, we investigate the influ-

ences of RT and RD on the system performance. The routing

metric is PEF. We let = 25%, but vary RT and RD. Fig.9 shows the saturated BSIs with different settings of RT and

RD. We observe that the bandwidth satisfaction is enhanced as

RT decreases or as RD increases. The reasons are as follows.

First, when RT is small, the estimation of IRB is sensitive

to the fluctuations of channel residual bandwidth, and thus

the prediction of the residual bandwidth of a path becomes

accurate. Second, if RD is large, nodes tend to overestimate

the range of its co-channel interfering neighborhood, leadingto conservative estimations of channel residual bandwidth.

The same reasons explain why delay (shown in Fig. 10)

%0.0%2.0

%4.0

%6.0

%8.0

%0.1

%2.1

%4.1

%6.1

%8.1

%04=TR%03=TR%02=TR%01=TR

n

ormalizedmessageoverhead

1=DR

2=DR

3=DR

Fig. 11. Normalized message overhead for Scenario 3.

decreases with a smaller RT or a larger RD. Fig. 11 depicts

the normalized message overheads for different settings of RT

and RD. Clearly, the normalized message overhead increases

when RT becomes small or RT becomes large. This means

that there are tradeoffs between bandwidth satisfaction and

message overhead, and between delay and message overhead.

More importantly, from Fig. 11, we also observe that evenwhen an average BSI of about 97.4% is achieved with a small

RT, say 10%, and a large RD, say 3, the normalized messageoverhead of our protocol remains low (no larger than 1.62%).

V. CONCLUSIONS

In this paper, we propose an on-demand bandwidth-

constrained routing protocol for MR2-MC WMNs based on

the IEEE 802.11 DCF MAC protocol. A threshold-triggeredbandwidth estimation scheme is proposed for each node to

estimate the free-to-use bandwidth on each associated channel.

According to the free-to-use bandwidth at each node, a dis-

tributed call admission control mechanism predicts the residualbandwidth of a path with the consideration of the inter-flow

and intra-flow contentions. To select the most efficient feasible

path, we further propose a routing metric, intending to strike

a balance between the cost and the bandwidth of the path. We

conduct ns-2 simulations to evaluate the performance of the

proposed routing protocol. The simulation results show that

our routing protocol can successfully discover paths that meetthe end-to-end bandwidth requirements offlows, protect exist-

ing flows from QoS violations, exploit the capacity gain dueto multiple channels, and incurs low message overhead. We

also discuss the tradeoffs between different system parameters

in this problem. To our best knowledge, this is the first paperpresenting an on-demand QoS routing protocol for MR2-MC

WMNs, which is fully distributed and applicable to the IEEE

802.11 DCF MAC protocol.

REFERENCES

[1] I. F. Akyildiz, X. Wand, and W. Wang, Wireless mesh networks: asurvey, Computer Networks, Mar. 2005.

[2] R. Bruno, M. Conti, and E. Gregori, Mesh networks: commodity multi-hop ad hoc networks, IEEE Commun., Mar. 2005.

[3] V. Gambiroza, B. Sadeghi, and E. W. Knightly, End-to-end perfor-mance and fairness in multi-hop wireless backhaul networks, ACM

MOBICOM, 2004.[4] A. Raniwala and T.-C. Chiueh, Architecture and algorithms for an IEEE

802.11-based multi-channel WMN, in Proc. IEEE INFOCOM, 2005.[5] T. Liu and W. Liao, Capacity-aware routing in multi-channel multi-rate

WMNs, in Proc. IEEE ICC, 2006.


10/10


[6] C. Zhu and M. S. Corson, Qos routing for mobile ad hoc networks,in Proc. IEEE INFOCOM, Mar. 2002.

[7] K. Sanzgiri, I. Chakeres, and E. Belding-Royer, Determining intra-flow contention along multi-hop paths in wireless networks, in Proc.

BROADNETS, 2004.[8] T. Liu and W. Liao, On routing in multichannel wireless mesh

networks: Challenges and solutions, IEEE Network, no. 1, pp. 1318,Jan./Feb. 2008.

[9] A. Raniwala, K. Gopalan, and T. Chiueh, Centralized channel assign-ment and routing algorithms for multi-channel WMNs, ACM MC2R,

2004.[10] J. Tang, G. Xue, and W. Zhang, Interference-aware topology controland QoS routing in multi-channel wireless mesh networks, ACM

MOBIHOC, 2005.[11] T. Liu and W. Liao, Interplay of network topology and channel assign-

ment in multi-radio multi-rate multi-channel wireless mesh networks,in Proc. IEEE GLOBECOM, 2008.

[12] A. K. Das, H. M. K. Alazemi, R. Vijayakumar, and S. Roy, Opti-mization models for fixed channel assignment in WMNs with multipleradios, in Proc. IEEE SECON, 2005.

[13] R. Draves, J. Padhye, and B. Zill, Routing in multi-radio, multi-hopwireless mesh networks, ACM MOBICOM, 2004.

[14] Y. Yang and R. Kravets, Contention-aware admission control for adhoc networks, IEEE Trans. Mobile Computing, pp. 363377, July/Aug.2005.

[15] R. Gupta, Z. Jia, T. Tung, and J. Walrand, Interference-aware QoSrouting (IQRouting) for ad-hoc networks, in Proc. IEEE Globecom,2005.

[16] M. E. M. Campista, P. M. Esposito, I. M. Moraes, L. H. M. K.Costa, O. C. M. B. Duarte, D. G. Passos, C. V. N. D. Albuquerque,D. C. M. Saade, and M. G. Rubinstein, Routing metrics and protocolsfor wireless mesh networks, IEEE Network, 2008.

[17] Y. Hu, X.-Y. Li, H.-M. Chen, and X.-H. Jia, Distributed call admissionprotocol for multi-channel multi-radio wireless networks, in Proc. IEEEGlobecom, 2007.

[18] The network simulator - ns2. [Online]. Available: http://www.isi.edu/nsnam/ns/

[19] K. N. Ramachandran, E. M. Belding, K. C. Almeroth, and M. M. Bud-dhikot, Interference-aware channel assignment in multi-radio WMNs,in Proc. IEEE INFOCOM, 2006.

[20] J. So and N. Vaidya, Multi-channel MAC for ad hoc networks:handling multi-channel hidden terminals using a single transceiver,

ACM MOBIHOC, 2004.

[21] J. Shi, T. Salonidis, and E. Knightly, Starvation mitigation throughmulti-channel coordination in CSMA based wireless networks, ACM

MobiHoc, 2006.

[22] Z. Jia, R. Gupta, J. Walrand, and P. Varaiya, Bandwidth guaranteedrouting for ad-hoc networks with interference consideration, in Proc.

IEEE ISCC, 2005.[23] H. Zhai and Y. Fang, Impact of routing metrics on path capacity in

multi-rate and multi-hop wireless ad hoc networks, in Proc. IEEEICNP, 2006.

[24] H. Zhai and Y. Fang, Physical carrier sensing and spatial reuse in multi-rate and multi-hop wireless ad hoc networks, inProc. IEEE INFOCOM,2006.

Tehuang Liu received his BS and Ph.D. degreesin Electrical Engineering from National TaiwanUniversity, Taiwan in 2004 and 2008, respectively.His research interests include performance analysisof multi-channel wireless mesh networks, routingprotocols for multi-channel wireless mesh networks,and radio resource management in WiMAX net-works. He is currently an engineer in MediaTek, Inc,Taiwan.

Wanjiun Liao (M97-SM05) received her Ph.D.degree in Electrical Engineering from the Universityof Southern California, Los Angeles, CA, USA, in1997. She joined the Department of Electrical Engi-neering, National Taiwan University, Taipei, Taiwan,as an Assistant Professor in 1997, where she is nowa full professor. Her research interests include wire-less networks, multimedia networks, and broadbandaccess networks.

Dr. Liao is currently an Associate Editor ofIEEE TRANSACTIONS ON WIRELESS C OMMUNI-

CATIONS, and was on the editorial board of IEEE T RANSACTIONS ONMULTIMEDIA. She served as the Technical Program Committee (TPC)chairs/co-chairs of many international conferences, including the Tutorial Co-Chair of IEEE INFOCOM 2004, the Technical Program Vice Chair of IEEEGlobecom 2005 Symposium on Autonomous Networks, a TPC Co-Chair ofIEEE Globecom 2007 General Symposium, and a TPC Co-Chair of IEEEICC 2010 Next Generation Networks and Internet Symposium. Dr. Liao hasreceived many research awards. Papers she co-authored with her studentsreceived the Best Student Paper Award for IEEE ICME 2000, and the BestPaper Award for ICCCAS 2002. Dr. Liao was the recipient of K. T. Li

Young Researcher Award honored by ACM in 2003, and the recipient ofDistinguished Research Award from National Science Council in Taiwan in2006. She is a Senior member of IEEE.

interference qos

Documents