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

Interference-Aware QoS Routing for Multi-Rate Multi-Radio Multi-Channel IEEE 802.11 Wireless Mesh Networks Tehuang Liu, Student Member, IEEE, and Wanjiun Liao, Senior Member, IEEE

Abstract—QoS routing in multi-channel wireless mesh networks (WMNs) with contention-based MAC protocols is a very challenging problem. In this paper, we propose an ondemand bandwidth-constrained routing protocol for multi-radio multi-rate multi-channel WMNs with the IEEE 802.11 DCF MAC protocol. The routing protocol is based on a distributed threshold-triggered bandwidth estimation scheme, implemented at each node for estimating the free-to-use bandwidth on each associated channel. According to the free-to-use bandwidth at each node, the call admission control, which is integrated into the routing protocol, predicts the residual bandwidth of a path with the consideration of inter-flow and intra-flow interference. To select the most efficient path among all feasible ones, we propose a routing metric which strikes a balance between the cost and the bandwidth of the path. The simulation results show that our routing protocol can successfully discover paths that meet the end-to-end bandwidth requirements of flows, protect existing flows from QoS violations, exploit the capacity gain due to multiple channels, and incurs low message overhead. Index Terms—QoS routing, multi-channel, multi-rate, multiradio, wireless mesh networks.

I. I NTRODUCTION

W

IRELESS mesh networks (WMNs) have received much attention in recent years thanks to such desirable features 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 wired networks, serving as gateways for other nodes to access the Internet. Packets are forwarded in a multi-hop fashion to and 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 that employing 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; accepted July 27, 2008. The associate editor coordinating the review of this paper and approving it for publication was Q. Zhang. This work was supported in part by the Excellent Research Projects of National Taiwan University, under Grant Number 97R0062-06, and in part by National Science Council (NSC), Taiwan, under Grant Number NSC962628-E-002-003-MY3. The authors are with the Department of Electrical Engineering and the Graduate Institute of Communication Engineering, National Taiwan University, 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 in single-channel WMNs, with IEEE 802.11 DCF, a contentionbased 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 many flows 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 wireless networks 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 they use. 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 capacity gain due to multiple channels, the routing algorithm must be able to evenly distribute traffic load across nodes as well as channels while striking a good balance between maximizing system throughput and meeting the QoS requirements of flows [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 each radio 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

c 2009 IEEE 1536-1276/09$25.00 

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

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 QoS routing protocol for multi-channel WMNs with a dynamic traffic model (where QoS flows 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 mainly on best-effort routing (e.g., [5], [9]) or QoS routing for singlechannel multi-hop wireless networks (e.g., [14], [15]). Yang and 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 propose an admission control framework, called Contention-aware Admission Control Protocol (CACP), to support bandwidthconstrained 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 the conflict graph for admission control. Draves et al. [13] design a routing metric, called Weighted Cumulative Expected Transmission 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-flow interference. 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 multichannel multi-radio WMNs. They then propose a set of centralized channel assignment, routing, and resource allocation algorithms 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 focus on bandwidth-constrained flows. Each node in the network

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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 protocol 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. According 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 bandwidthconstrained path may be costly in terms of radio resource, we propose a routing metric, intending to strike a balance between the 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 existing flows 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. II. S YSTEM M ODEL We consider an MR2 -MC WMN with the IEEE 802.11 DCF 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) on channel k if they have a radio operating on channel k and fall within the transmission range of each other. The multirate capability in the PHY layer is also considered in our model, 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 the physical layer). We also assume that for each flow, the end-toend bandwidth requirement can be arbitrary, but not dividable

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Fig. 1.

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

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 the link that operates on channel k and is incident on nodes i and j. For example, in the network shown in Fig. 1 where the 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 and channel 2, respectively, between nodes A 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 of this paper is confined to single-path routing since multi-path routing 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 certain network management operations (e.g., routing [5], loadbalancing 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 rendezvous 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 wireless 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 a result, 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 missing neighbor 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. I NTERFERENCE -AWARE Q O S 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 propose 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 channel status 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 residual 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 a period of time T via 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 − α)·LRB TABLEki

+ α·LRB MEASUREki ,

where 0 ≤ α ≤ 1. The second table maintained by each node i in the network, denoted by LRB COLLECTIONi , is used to store the LRB values reported by its co-channel interfering neighbors. We let LRB COLLECTIONki denote the set of entries in LRB COLLECTIONi for channel k, which are reported by node i’s 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 node’s LRB measurement is flooded, and (ii) Reporting Threshold (RT), indicating the condition under which the flooding for a node’s LRB measurement is triggered. Specifically, each node floods its LRB measurement on a certain channel (via the control radio operating on the control channel) only when the fluctuation in the measured LRB, compared with the last advertised value, exceeds RT times

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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., (a)

IRB TABLEki ≈ min(x : x∈{LRB TABLEki } ∪ LRB COLLECTIONki ). Note that we let IRB TABLEi denote node i’s 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 show how 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 node’s cochannel interfering neighbors on the path. In the following, we introduce the expected 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, and EBT(i,j)k ,f , the EBT value for link (i, j)k with respect to flow f . 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 time for 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 with respect to flow f . Thus, we have  EBTx,f , (1) CEBT(i,j)k ,f,p = x∈{I(i,j)k ∩p}

(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)k ∩p} 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 interfering link of link (i, j)k if t = k and at least one of nodes m and n is the co-channel interfering neighbor of node i or j. To obtain {I(i,j)k ∩p}, we approximate the set of co-channel interfering neighbors of node i on channel k by LRB COLLECTIONki . In other words, if there is an entry in LRB COLLECTIONki which is 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 the transmission 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 link operates, 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 of p1 for example. Since it interferes with links (B, C)1 , (D, E)1 , and (E, F )1 of 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 only interferes 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,f denote the sustainable sending rate (bps) on path p for flow f . Obviously, SRp,f is dominated by the bottleneck

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link of path p, i.e., SRp,f =

IRB(i,j)k − σ · T Lf · min ( ), T (i,j)k ∈p CEBT(i,j)k ,f,p

(2)

where IRB(i,j)k ≡ min(IRB TABLEki , IRB TABLEkj ) represents the minimum amount of free-to-use air time for link (i, j)k and σ ∈ [0, 1] is a tunable system parameter representing the percentage of radio resource that cannot be utilized due to MAC overhead. Accordingly, a path p is considered feasible for flow f if SRp,f is larger than the requesting sending rate of flow f (denoted by RRf ), i.e., SRp,f ≥ RRf . The call admission control integrated into the routing protocol (shown in 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 Metric It 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-end bandwidth as the metric. Choosing a path with larger residual bandwidth can reduce the probability of QoS violations due to the 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 keep away 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 transmission activity at a node takes up the capacity among all cochannel 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 the transmission cost of link (i, j)k . This product reflects the bandwidth consumption from the perspective of the whole network, rather than from a single link as ETB. The cost of path p for flow f , denoted by Cp,f , is then defined as the sum of the costs over all links of p, i.e.,  Cp,f = EBT(i,j)k ,f · |NODE(LRB COLLECTIONki ) (i,j)k ∈p

∪ NODE(LRB

COLLECTIONkj )|,

(3)

where NODE(LRB COLLECTIONkx ) denotes the set of nodes that each have an entry in LRB COLLECTIONkx , i.e., NODE(LRB COLLECTIONkx ) is used to approximate the cochannel 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 flow f as follows: PEFp,f =

SRp,f . Cp,f

(4)

The PEF of a path can be interpreted as the benefit-tocost ratio of the path. The higher the value of PEF, the lower the cost required for obtaining the same amount of benefit. 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 of Bellman’s Principle of Optimality [15], it is hard for distributed algorithms to 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 divided into the following three phases. 1) Route Request (RREQ) Packet Flooding: To determine a route for flow f , the source node initiates the route discovery procedure by flooding a route request (RREQ) packet on the control 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 destination node for a new flow f , respectively, and that an RREQ packet has traveled along a path Px−1 = {(n1 , n2 )c1 → (n2 , n3 )c2 → . . . → (nx−2 , nx−1 )cx−2 }, where 2 < x < m+1, and is being broadcast by node nx−1 to its neighbors on the control channel. Here ci represents the channel used on the i-th link of the partial path. When nx−1 broadcasts this RREQ packet on the control channel, it appends the following information to the METRIC CALCULATION field in the RREQ packet: < SRPx−1 ,f , CPx−1 ,f , EBT LISTx−1 , CEBT LISTx−1 , IRB LISTx−1 , IRB TABLEnx−1 , IN TABLEnx−1 >, where EBT LISTx−1 = {EBTi,f : i = (nj , nj+1 )cj , max(1, x − RD − 2) ≤ j ≤ x − 2}, CEBT LISTx−1 = {CEBTi,f,Px−1 : i = (nj , nj+1 )cj , max(1, x − RD − 2) ≤ j ≤ x − 2}, IRB LISTx−1 = {IRBi : i = (nj , nj+1 )cj , max(1, x − RD − 2) ≤ j ≤ x − 2}, and IN TABLEnx−1 is a table that contains the number of co-channel interfering neighbors of node nx−1 on each of its associated channels (except the control channel). Let IN TABLEknx−1 denote the entry in IN TABLEnx−1 for channel

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k. Since NODE(LRB COLLECTIONki ) is used to approximate the set of co-channel interfering neighbors of node i on channel k, we have

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

IN TABLEknx−1 = |NODE(LRB COLLECTIONknx−1 )|.

IV. P ERFORMANCE E VALUATION

2) Path Selection Upon Receiving an RREQ Packet: When node nx receives an RREQ broadcast by nx−1 , 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 packet to prevent a loop. Otherwise, it determines the channels (excluding the control channel) which are common to itself and its upstream node (i.e., nx−1 ). 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 → . . . → (nx−2 , nx−1 )cx−2 → (nx−1 , nx )k }, based on the information carried in the RREQ packet. Note that here we approximate the cost of a path as follows such that node nx−1 does not need to transmit the whole NODE(LRB COLLECTIONknx−1 ) to node nx .  EBT(i,j)k ,f · IN TABLEki . Cp,f = (i,j)k ∈p

If each common channel leads to an infeasible path (i.e., SRPxk ,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 maxk∈C PEFPxk ,f , where C = {i : SRPxi ,f ≥ RRf }. If there is a tie, the shortest one wins. If there is still a tie, the node just randomly selects one channel from C. After channel t is determined, if node nx is not the destination, it updates the partial path to Px = {(n1 , n2 )c1 → (n2 , n3 )c2 → . . . → (nx−2 , nx−1 )cx−2 → (nx−1 , nx )cx−1 }, where cx−1 = t, and updates the METRIC CALCULATION field 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 , PEFPxt ,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, the destination selects the path with the highest PEF among all discovered paths, and then unicasts a ROUTE REPLY (RREP) packet back to the source. Ties are broken at random. The RREP packet carries the information about the selected path, i.e., Pm = {(n1 , n2 )c1 → (n2 , n3 )c2 → . . . → (nm−1 , nm−2 )cm−1 }. Each intermediate node ni on the selected path receiving an RREP packet knows about which channels to use (i.e., ci−1 and ci ) to communicate with the previous hop node (i.e., ni−1 ) 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 the source node receives more than one RREP packet, which may be replied by different gateways in the network, it will update

In this section, we conduct ns-2 [18] simulations to evaluate the performance of the proposed routing protocol and study how parameters σ, RD, and RT affect 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 1500m×1500m 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 node are 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. In IEEE 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 450m for all channel rates for simplicity. A radio of a node is said to be an orphan radio if this node cannot communicate with any other node via this radio even at the lowest data rate. An orphan radio occurs when either of the following two conditions holds: (i) the radio is assigned a channel which is 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 to a 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 σ, 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 α = 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  1, if Ta · T−1 r > 1 BSI = −1 Ta · Tr , otherwise

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A LGORITHM 1 The routing algorithm performed at node nx 1: if nx appears on Px−1 then 2: discard this RREQ packet; 3: exit; 4: else 5: Common Channel Set ← {k : k ∈ Freq(nx−1 ) ∩ Freq(nx )}, where Freq(i) is the set of channels used by node i, excluding the control channel; 6: if Common Channel Set is empty then 7: discard this RREQ packet; 8: exit; 9: else 10: C ← {}; 11: for each channel k ∈ Common Channel Set do 12: Pxk ← {(n1 , n2 )c1 → (n2 , n3 )c2 → . . . → (nx−2 , nx−1 )cx−2 → (nx−1 , nx )k }; 13: CEBT(n EBT(nj ,nj+1 )c ,f ; k ← EBT(nx−1 ,nx )k ,f + max(1,x−RD−2)≤j≤x−2,cj =k x−1 ,nx )cx−1 ,f,Px j 14: for i = 0, 1, . . . , RD do 15: if cx−i−2 = k then 16: CEBT(n k ← EBT(nx−1 ,nx )k ,f + CEBT(nx−i−2 ,nx−i−1 )c ,f,Px−1 ; x−i−2 ,nx−i−1 )cx−i−2 ,f,Px x−i−2 17: end if; 18: end for; −σ·T IRB(nx−i−1 ,nx−i )c L x−i−1 19: SRP k ,f ← min(SRPx−1 ,f , Tf · mini=0,1,...,RD+1 ( CEBT )); x (n ,n ) ,f,P k 20:

CP k ,f ← CPx−1 ,f + IN TABLEknx−1 · EBT(nx−1 ,nx )k ,f ;

21:

PEFP k ,f ←

x

x

SR k Px ,f C k Px ,f

x−i−1

x−i cx−i−1

x

;

if SRP k ,f ≥ RRf then x C ← C ∪ {k}; end if; end for; end if; end if; if C is empty then discard this RREQ packet; exit; else t ← arg maxk∈C PEFP k ,f , where if there is a tie, the shortest one wins.; x end if; if nx = nm then store< Pxt , PEFP t ,f >; x wait for an appropriate amount of time to receive RREQ packets to learn more feasible routes; else cx−1 ← t; update the partial path in the RREQ packet to Px ← {(n1 , n2 )c1 → (n2 , n3 )c2 → . . . → (nx−2 , nx−1 )cx−2 → (nx−1 , nx )cx−1 }; SRPx ,f ← SRP t ,f ; x CPx ,f ← CP t ,f ; x if x − RD − 2 > 0 then EBT LISTx ← EBT LISTx−1 − {EBT(nx−RD−2 ,nx−RD−1 )c ,f }; x−RD−2 CEBT LISTx ← CEBT LISTx−1 − {CEBT(nx−RD−2 ,nx−RD−1 )c ,f,Px−1 }; x−RD−2 }; IRB LISTx ← IRB LISTx−1 − {IRB(nx−RD−2 ,nx−RD−1 )c x−RD−2 end if; EBT LISTx ← EBT LISTx + {EBT(nx−1 ,nx )t ,f }; CEBT LISTx ← CEBT LISTx + {CEBT(nx−1 ,nx )t ,f,P t }; x IRB LISTx ← IRB LISTx + {IRB(nx−1 ,nx )t }; IN TABLEknx ← |NODE(LRB COLECTIONknx )| for each k ∈ Freq(nx ); replace the METRIC CALCULATION field of the RREQ packet by < SRPx ,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;

22: 23: 24: 25: 26: 27: 28: 29: 30: 31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44: 45: 46: 47: 48: 49: 50: 51:

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 BSI as the average BSI of flows in the steady state. System saturated throughput: We define the system throughput as the aggregate of flows’ 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 successively received by the destination node. We define the saturated endto-end delay as the average end-to-end delay in the steady state. Normalized saturated message overhead: The normal-

Fig. 3.

28

system saturated throughput (Mbps)

system saturated throughput (Mbps)

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

26 24 22 20 18 16 14 12 best-effort best-effort WCETT WCETT BLC

BLC

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average saturatedBSI

1.0 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Fig. 5.

25%

3 0%

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System saturated throughput for Scenario 2.

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PEF

Average saturated BSI for Scenario 1.

average saturated end-to-end delay (ms)

Fig. 4.

BLC

15%

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Fig. 6.

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30 28 26 24 22 20 18 16 14 12

PEF

System saturated throughput for Scenario 1.

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BLC

PEF

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 the steady state. C. Simulation Results 1) Scenario 1: In the first scenario, we let σ = 25%, RD = 3, and RT = 10%, but vary the routing metrics with 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 test so that no flow is blocked). Fig. 3 depicts the system saturated throughputs against different routing metrics for different

Average saturated BSI for Scenario 2.

approaches. When call admission control is activated, the PEF metric 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-channel systems. We also find that de-activating call admission control increases 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 σ. We vary σ from 10% to 30%, but fix RD and RT at 3 and 10%, respectively. The routing metric is PEF. The simulation results are shown in Figs. 6 to 8. When σ 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

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1.00 0.98 0.96 0.94 0.92 0.90 0.88 0.86 0.84 0.82 0.80

average saturated end-to-end delay (ms)

Fig. 9.

Fig. 10.

Fig. 11.

RD=1 RD=2 RD=3

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Average saturated BSI for Scenario 3.

RD=1 RD=2 RD=3

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0.8% 0.6% 0.4% 0.2% RT=20%

RT=30%

RT=40%

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 even when an average BSI of about 97.4% is achieved with a small RT, say 10%, and a large RD, say 3, the normalized message overhead of our protocol remains low (no larger than 1.62%). V. C ONCLUSIONS

40 38 36 34 32 30 28 26 24 22 20 18 16 RT=10%

1.2% 1.0%

RT=10%

Average saturated end-to-end delay for Scenario 2.

RT=10%

1.4%

0.0%

parameter σ Fig. 8.

1.6%

RT=40%

Average saturated end-to-end delay for Scenario 3.

lower saturated BSI (see Fig. 7) and larger delay (see Fig. 8) with a small σ. This concludes that σ can be used to adjust the tradeoff between system throughput and bandwidth satisfaction (or between system throughput and end-to-end delay). 3) Scenario 3: In this scenario, we investigate the influences of RT and RD on the system performance. The routing metric is PEF. We let σ = 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, leading to conservative estimations of channel residual bandwidth. The same reasons explain why delay (shown in Fig. 10)

In this paper, we propose an on-demand bandwidthconstrained routing protocol for MR2 -MC WMNs based on the IEEE 802.11 DCF MAC protocol. A threshold-triggered bandwidth 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 distributed call admission control mechanism predicts the residual bandwidth 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 meet the end-to-end bandwidth requirements of flows, protect existing flows from QoS violations, exploit the capacity gain due to 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 paper presenting an on-demand QoS routing protocol for MR2 -MC WMNs, which is fully distributed and applicable to the IEEE 802.11 DCF MAC protocol. R EFERENCES [1] I. F. Akyildiz, X. Wand, and W. Wang, “Wireless mesh networks: a survey,” Computer Networks, Mar. 2005. [2] R. Bruno, M. Conti, and E. Gregori, “Mesh networks: commodity multihop ad hoc networks,” IEEE Commun., Mar. 2005. [3] V. Gambiroza, B. Sadeghi, and E. W. Knightly, “End-to-end performance 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.

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[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 intraflow 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. 13–18, Jan./Feb. 2008. [9] A. Raniwala, K. Gopalan, and T. Chiueh, “Centralized channel assignment and routing algorithms for multi-channel WMNs,” ACM MC2R, 2004. [10] J. Tang, G. Xue, and W. Zhang, “Interference-aware topology control and QoS routing in multi-channel wireless mesh networks,” ACM MOBIHOC, 2005. [11] T. Liu and W. Liao, “Interplay of network topology and channel assignment 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, “Optimization models for fixed channel assignment in WMNs with multiple radios,” in Proc. IEEE SECON, 2005. [13] R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multi-hop wireless mesh networks,” ACM MOBICOM, 2004. [14] Y. Yang and R. Kravets, “Contention-aware admission control for ad hoc networks,” IEEE Trans. Mobile Computing, pp. 363–377, July/Aug. 2005. [15] R. Gupta, Z. Jia, T. Tung, and J. Walrand, “Interference-aware QoS routing (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 protocols for wireless mesh networks,” IEEE Network, 2008. [17] Y. Hu, X.-Y. Li, H.-M. Chen, and X.-H. Jia, “Distributed call admission protocol for multi-channel multi-radio wireless networks,” in Proc. IEEE Globecom, 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. Buddhikot, “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 through multi-channel coordination in CSMA based wireless networks,” ACM MobiHoc, 2006.

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Tehuang Liu received his BS and Ph.D. degrees in Electrical Engineering from National Taiwan University, Taiwan in 2004 and 2008, respectively. His research interests include performance analysis of multi-channel wireless mesh networks, routing protocols for multi-channel wireless mesh networks, and radio resource management in WiMAX networks. He is currently an engineer in MediaTek, Inc, Taiwan. Wanjiun Liao (M’97-SM’05) received her Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, CA, USA, in 1997. She joined the Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, as an Assistant Professor in 1997, where she is now a full professor. Her research interests include wireless networks, multimedia networks, and broadband access networks. Dr. Liao is currently an Associate Editor of IEEE T RANSACTIONS ON W IRELESS C OMMUNI CATIONS , and was on the editorial board of IEEE T RANSACTIONS ON M ULTIMEDIA. She served as the Technical Program Committee (TPC) chairs/co-chairs of many international conferences, including the Tutorial CoChair of IEEE INFOCOM 2004, the Technical Program Vice Chair of IEEE Globecom 2005 Symposium on Autonomous Networks, a TPC Co-Chair of IEEE Globecom 2007 General Symposium, and a TPC Co-Chair of IEEE ICC 2010 Next Generation Networks and Internet Symposium. Dr. Liao has received many research awards. Papers she co-authored with her students received the Best Student Paper Award for IEEE ICME 2000, and the Best Paper Award for ICCCAS 2002. Dr. Liao was the recipient of K. T. Li Young Researcher Award honored by ACM in 2003, and the recipient of Distinguished Research Award from National Science Council in Taiwan in 2006. She is a Senior member of IEEE.