Routing with Load-Balancing in Multi-Radio ... - Semantic Scholar

IEICE TRANS. COMMUN., VOL.E92–B, NO.3 MARCH 2009

700

PAPER

Special Section on Ad Hoc and Mesh Networking for Next Generation Access Systems

Routing with Load-Balancing in Multi-Radio Wireless Mesh Networks Anh-Ngoc LE† , Student Member, Dong-Won KUM† , Nonmember, You-Ze CHO†a) , and Chai-Keong TOH†† , Members

SUMMARY This paper addresses the interference and load imbalance problems in multi-radio infrastructure mesh networks where each mesh node is equipped with multiple radio interfaces and a subset of nodes serve as Internet gateways. To provide backbone support, it is necessary to reduce interference and balance load in Wireless Mesh Networks (WMNs). In this paper, we propose a new Load-Aware Routing Metric, called LARM, which captures the differences in transmission rates, packet loss ratio, intra/inter-flow interference and traffic load in multi-radio mesh networks. This metric is incorporated into the proposed load-balancing routing, called LBM, to provide load balancing for multi-radio mesh network. Simulation results show that LARM provides better performance compared to WCETT and hop-count routing metrics in LBM routing protocol. key words: wireless mesh network, multi-channel, multi-radio, routing metric, load balancing

1.

Introduction

Wireless Mesh Networks (WMNs) are emerging as a new attractive communication paradigm owing to their low cost and rapid deployment. The application scenarios for WMNs include wireless broadband Internet access, intelligent transportation systems, transient networks in convention centers, and disaster recovery [1]. WMNs comprise of mesh routers and mesh clients, in which mesh routers form the mesh backbone of the infrastructure and can either be stationary or semi-mobile. Some mesh routers may have gateway functionality and provide connectivity to other networks, such as the Internet and other wireless networks. Mesh clients (e.g., PDAs, laptops) use mesh routers as relay nodes to access the Internet, as shown in Fig. 1. In a wireless mesh network, the capacity is reduced by interference from simultaneous wireless transmissions. Two types of interference affect the throughput of multi-hop wireless networks: intra-flow interference, which is induced between adjacent nodes on the same routing path, and interflow interference, which is caused by nodes from neighboring paths. The capacity of a wireless mesh network can be improved if the data rate of the wireless channel is increased, which can be achieved by better modulations, multi-antenna techniques, and better Medium Access Control (MAC) proManuscript received July 17, 2008. Manuscript revised October 23, 2008. † The authors are with the School of Electrical Engineering and Computer Science, Kyungpook National University, Korea. †† The author is with Department of Electrical and Electronic Engineering, University of Hong Kong, China. a) E-mail: [email protected] DOI: 10.1587/transcom.E92.B.700

Fig. 1

Multi-radio wireless mesh networks.

tocols. Furthermore, the capacity of a WMN can be improved if non-overlapping wireless channels are used simultaneously. One approach is that each mesh router uses a single radio interface that is dynamically switched to wireless channels with different frequency bands to communicate with different nodes. However, this approach incurs an overhead due to the channel switching delay [2]. Thus, a more practical approach is to use multiple radio interfaces dedicated to different radio channels, called a multi-radio mesh network. Yet, multi-radio mesh network requires efficient multi-channel assignment and routing schemes. An multi-channel assignment scheme will assign a channel chosen from among the non-overlapped channels to a specific radio interface at each node. This helps to reduce intra-flow and inter-flow interferences. While routing scheme requires efficient, high-capacity routes to be computed between a source-destination pair of nodes. Various multi-channel assignment methods have already been proposed [3]–[5]. Accordingly, in this paper we present a routing scheme for a multi-radio mesh network. The unique characteristics of multi-radio wireless mesh networks, such as shared nature of wireless channels, use of multiple radio interfaces, stationary mesh routers, and userto-gateway traffic pattern, make them different from wired networks and other wireless networks. In particular, the effects of an unbalanced load can cause rapid gateway overloading, center overloading, or channel overloading. Since most traffic in a WMN is destined towards gateways, traf-

c 2009 The Institute of Electronics, Information and Communication Engineers Copyright

LE et al.: ROUTING WITH LOAD-BALANCING IN MULTI-RADIO WIRELESS MESH NETWORKS

701

fic concentration at gateway nodes creates a load imbalance at certain gateways, which in turn results in gateway overloading. The issue of center overloading refers to the nodes located near the geographical center of the network becoming overloaded in comparison to other nodes in the network. The main reasons behind center overloading are: (i) the nodes near the center of the network tend to lie on the shortest path more than other nodes in the network, (ii) the use of multi-hop relaying, and (iii) the relatively static nature of WMNs. Finally, channel overloading in a multi-radio wireless mesh network refers to certain channels becoming overloaded compared to other channels. Thus, for any static network, like a multi-radio mesh network, load balancing is necessary to avoid hot spots and to increase network utilization, as bad routes can exist for a long time in a static network and result in congestion and inefficient use of network resources. In this paper, we propose a new Load-Aware Routing Metric, called LARM, which captures the differences in transmission rates, packet loss ratio, intra/inter-flow interference and traffic load in multi-radio mesh networks. This metric is incorporated into the proposed load-balancing routing protocol, called LBM, to balance the load in multiradio wireless mesh networks. We demonstrate that LARM provides better performance compared to WCETT and hopcount routing metrics in LBM routing protocol. The remainder of this paper is organized as follows: Sect. 2 reviews some related work, then Sect. 3 describes the load metric LARM and proposed LBM routing protocol in detail. Section 4 presents some simulation results, and finally, Sect. 5 concludes this paper. 2.

Related Work

In this section, we briefly discuss recently proposed routing metrics and load balancing routing protocols for wireless mesh network. Routing metrics are very critical for determining the performance of the networks. A good routing metric should find paths with links that have high data rate, low loss ratio, low level of interference and less congestion. Recently many routing metrics are proposed for multi-hop wireless mesh networks. They are: (i) Hop-count [6]–[8], (ii) Expected Transmission Count (ETX) [9], (iii) Expected Transmission Time (ETT) [2], (iv) Weighted Cumulative Expected Transmission Time (WCETT) [2], (v) Metric of Interference and Channel switching (MIC) [10], and (vi) Interference Aware routing metric (IWARE) [11]. The hop-count metric is the most commonly used routing metric in routing protocols such as AODV [6], DSR [7], and DSDV [8]. Hop-count treats all links in the network to be alike and finds paths with minimum number of hops. It does not consider the difference of transmission rate and packet loss ratio or interference experienced by the links. Therefore, hop-count results in poor performance. The routing metric ETX [9] captures both the packet loss ratio and path length by counting number of MAC layer transmissions for successfully delivering a packet through a

wireless link. However, ETX does not consider the data rate at which packets are transmitted over each link. It also does not capture the intra-flow or inter-flow interference. The routing metric ETT [2] improves upon ETX by capturing the data rate used by each link. It considers the differences in link transmission rate for different links in wireless mesh networks. The ETT is defined as the expected MAC layer duration for a successful transmission of a packet on a wireless link. ETT metric captures the impact of link capacity on the performance of the path. The drawback of ETT is that it does not fully capture the intra-flow and inter-flow interference in the network. Both ETX and ETT do not consider the presence of multiple channels and therefore find path with less channel diversity. Weighted Cumulative Expected Transmission Time (WCETT) [2] is proposed for multi-radio mesh networks. WCETT enhances ETT by considering the bandwidth, error rate and channel diversity in the path. But the drawback of this metric is that it does not capture inter-flow interference and when there are multiple flows in the network, it will route the packet through congested areas resulting in poor throughput. Metric of Interference and Channel Switching (MIC) [10] is designed as the routing metric for Load and Interference-Balanced Routing Algorithm (LIBRA) which is a proactive routing protocol [12]. Routing metric MIC improves WCETT by considering inter-flow interference and intra-flow interference. MIC captures inter-flow interference by scaling up the ETT of the link by the number of interfering neighbors. However, the degree of interference caused by the each interfering node is not the same and it depends on the amount of traffic generated by the interfering node [11]. An interferer that is not involved in any transmission simultaneously but close to the sender or receiver will not cause any interference. As a result, MIC does not completely capture the inter-flow interference and traffic load. It may route traffic through congested areas. Also, MIC does not capture the link loss ratio, data rate of the link in the absence of interfering neighbors [11]. One major disadvantage of MIC metric is that it has high implementation complexity. For estimation of routing metric, it requires each node to maintain global information of the network, such as the total number of nodes and the smallest value of ETT in wireless mesh networks [10]. Therefore, this metric is suitable for proactive routing algorithms, such as link state routing, which allow each node maintain a global knowledge of the WMNs. Interference Aware routing metric (iAWARE) [11] is proposed for multi-radio mesh networks. This metric captures the effects of variation in link loss-ratio, differences in transmission rate as well as inter-flow and intra-flow interference. However, this metric captures the interference from physical layer which requires very high implementation complexity. Another drawback of this metric is that it gives more weightage to ETT compared to interference of the link. This will sometimes result in choosing the path with lower ETT but higher interference.


702

Although various load-balancing routing protocols have already been proposed for MANET, most of them are only applicable to single channel wireless networks. For example, Dynamic Load-Aware Routing (DLAR) [13] uses the number of packets buffered at an interface as the load metric. However, the interface queue length by itself does not reflect the correct status of the actual load in 802.11 MAC-based protocols. Load Aware Routing in Ad hoc (LARA) [14] also uses the queue length at an interface as the load metric, which is the sum of the node’s average queue length and the neighbor node’s average queue length, thereby this load metric is more accurate than the one used with DLAR. However, it is still only applicable to a single radio wireless network. Furthermore, many routing protocols have already been proposed for multi-radio mesh networks. In [2], Draves et al. proposed Multi-Radio Link-Quality Source Routing (MR-LQSR) protocol to find high-throughput paths based on WCETT routing metric. In [15], the Multi-Radio Ad-hoc On-Demand Distance Vector Routing (AODV-MR) protocol is proposed, which is a multi-homing extension to the Ad-hoc On-Demand Distance Vector Routing (AODV) protocol [6]. However, these protocols do not consider load balancing, and randomly select a channel for each flow during the route discovery phase. Hence, packets can be routed through hot spots, resulting in congestion and inefficient use of network resources. In order to balance load in WMNs, routing metric needs to captures load correctly. Furthermore, routing protocol needs to distribute traffic among mesh routers to avoid creating congested areas. Different from existing routing metrics, our routing metric LARM captures not only differences in data rate, link loss ratio, intra/inter-flow interference but also traffic load. Specifically, it captures traffic load which is the sum of the node’s average queue length and the neighbor node’s average queue length. LARM is incorporated into load balancing routing protocol LBM to provide load balancing in multi-radio wireless mesh network. 3.

Load-Balancing Routing for Multi-Radio Mesh Networks

This section presents the channel assignment assumptions, Load-Aware Routing Metric (LARM), and the operation of Load-Balancing routing protocol for Multi-radio mesh network (LBM). 3.1 Basic Assumptions in Channel Assignment We consider a wireless mesh network in which each mesh router is equipped with multiple radio interfaces. Each wireless interface can be statically assigned non-overlapping channels using channel assignment algorithms [3]–[5]. One of the interfaces at each node is tuned to the dedicated control channel. To fully utilize the resource in the system, we do not restrict the control channel to transmit control messages only, i.e., it can carry data packets as well. This strat-

Fig. 2

Multi-radio mesh network and logical topology.

Fig. 3

Interference traffic in multi-radio mesh network.

egy allows those nodes with only one interface to transmit data to other nodes. The channel assignments to the interfaces create a logical topology that can be represented by a multi-graph (Fig. 2(b)). For example, as shown in Fig. 2(a), nodes B and C have two interfaces tuned to common channels (channels 1 and 6), meaning there are two links between nodes B and C. Therefore, the routing protocol can decide which link to use according to the channel load and degree of interference. 3.2 Load-Aware Routing Metric (LARM) As mentioned above, a multi-radio network can be represented as the multi-graph G = (V, E), where V is the set of mesh routers and E is the multi-set of unordered pairs of distinct vertices, called edges (Fig. 2). Because of the shared nature of wireless medium, if node u wants to transmit the data, it has to sense for other transmissions in its range first, and then access channel only if no other node in its range is currently transmitting or receiving. As a result, packet delay is not only caused by traffic load at current node, but also by traffic load at neighboring nodes called interference traffic load (Fig. 3). Let li be a link between nodes u and v (u and v are neighbors) using channel i. The interference traffic load of link li depends on the load between nodes u and v, where the neighbor nodes compete to access to the channel i to route a traffic: Q(li ) = Qik (1) k∈N i (u)∪N i (v)

where N i (u), N i (v) are the set of interfering neighbors of nodes u and v on channel i respectively, and Qik is the average number of packets buffered at the interface assigned with channel i at node k. Link load of link li is defined as follows: LL(li ) = ET T (li ) × Q(li )

(2)


703

where ET T (li ) is the expected transmission time of a packet over a link li . Link metric (LL) captures not only the difference in transmission rate and loss ratio of links, but also inter-flow interference and traffic load at end-points of link. Suppose p = {li11 , li22 , ..., linn } be a route from source to destination, where likk is the kth link using channel ik on route p. We define channel load (CL) of channel j on the routing path p as the cumulative load of links using channel j: CL( j) =

n

LL(lkj )

(3)

k=1

In order to exploit the channel diversity and to find the path with less intra-flow interference, Load-Aware Routing Metric (LARM) is defined by taking weighted average of the accumulated channel load and bottlenecked channel load on the routing path: m LARM = (1−α)× CL( j)+α× max {CL( j)} j=1

1≤ j≤m

(4)

where m is the number of available channels on the routing path, α is a tunable parameter: 0 ≤ α ≤ 1. To estimate interference traffic load, HELLO messages are broadcasted to all interfaces periodically (every 1 second in our implementation). When a node sends a HELLO message on the channel i, it updates the average number of packets buffered at its interface. The neighbors that receive this message then update the corresponding node’s load information into the neighbor table. The interference traffic load is then computed by using load information in the neighbor table. This HELLO messages are also used to calculate the expected transmission count (ETX) and expected transmission time (ETT) [2]. 3.3 Load-Balancing Routing Protocol for Multi-Radio Mesh Networks (LBM) We design Load-Balancing routing protocol for Multi-radio mesh networks (LBM) derived from AODV-MR protocol [6]. LBM selects the route with less congestion, low packet loss, high data rate and low level of interference (see Algorithm 1). When a source node has a packet to transmit to a destination node for which there is no entry in the routing table, it sends a Route Request (RREQ) broadcast packet to the control channel to discover route. Each RREQ packet has a unique identifier that is a combination of the MAC address for the interface to which it is sent and a sequence number that is incremented for each RREQ packet generated. Before a source node sends a RREQ packet, it sets the load of available channels (CL) in the route request, used to calculate the path load (LARM), to zero. When an intermediate node (X) receives a RREQ packet from a neighbor node (Y), it needs to know which channel to use as the reverse route. A channel selection mechanism is applied to achieve channel load balancing. First, node X determines

Algorithm 1 Load-balancing route discovery. CL( j): channel load of channel j RC: recommended channel m: the number of available channels The source node:sending RREQ 1: CL(i) ← 0, ∀i ∈ {1, .., m} 2: Broadcast RREQ to the control channel Intermediate node(X):receiving RREQ from node Y 1: compute a recommended channel: RC 2: update the channel load of recommended channel: CL(RC) 3: compute path load from source to current node m CL( j) + α × max {CL( j)} LARM ← (1 − α) × j=1

1≤ j≤m

4: if (first RREQ is received) or (duplicate RREQ with smaller LARM is received) then 5: create reverse route to source using interface with RC 6: if (node X is destination) then 7: unicast RREP message to source node 8: else 9: rebroadcast RREQ message to the control channel 10: end if 11: else 12: discard RREQ message 13: end if

the set of common channels (C) between nodes X and Y, then it specifies the channel with the lowest load among the common channels as the recommended channel (RC) for the link between nodes X and Y. After that, node X updates the load of recommended channel to RREQ, and computes the path load from source node to current node as described in Eq. (4). When node X first receives a RREQ packet, it creates a reverse route entry to the source node using the interface with the RC channel. However, in the case, node X has already seen the RREQ packet, yet receives a new RREQ with a better path (smaller path metric LARM), it updates the reverse path accordingly. It then forwards the RREQ on the control channel. To maintain up-to-date route load information, an intermediate node is not allowed to reply to a RREQ even if it has a route to the destination node. Once the first RREQ message reaches the destination, a Route Reply (RREP) packet is generated and unicasts toward the source node along the reverse route built during the RREQ flooding. As the RREP is propagated, intermediate nodes build a forward route to the destination. The RREP also carries the load of available channels which is used to calculate the path load (LARM). If a duplicate RREQ arrives at the destination node, the path load LARM value is compared with the former one. If a smaller value is found, a new RREP packet is sent back to the source, which then changes the route accordingly. Once the source node receives the RREP packet, the data packets are forwarded from source to destination. As data flow from the source to destination, each node along the route updates the timer associated with the route to maintain the route in routing table. If a route is not used in a period of time, a node can not be sure whether the route is still valid.


704

Therefore, the node removes the route from it’s routing table. When a link on an active route is broken, the node on the active route that detects the break either sends a Route Error (RRER) packet to the source node or performs a local repair by finding the alternative least load route to the destination. 4.

Performance Evaluation

4.1 Simulation Environment We have implemented various routing metrics for LBM, including LARM, hop-count and WCETT in ns-2 simulator. The performance of our proposed routing metric LARM is compared with hop-count and WCETT routing metrics. A wireless mesh network with an area of 1000 m×1000 m was established using randomly distributed static mesh routers. One mesh router, which is located at the center of network, is selected as single gateway node. In our simulation, we assume common channel assignment approach for simplifying channel assignment. In this approach, each mesh router was equipped with multiple IEEE 802.11a wireless interfaces that were tuned to nonoverlapping channels. The number of interfaces was the same for all the mesh routers, which also used the same channel allocation scheme (similar to [2]). For example, if two interfaces are used at each node, then the two interfaces are assigned to the same two channels at every node. The benefit of this approach is that the connectivity of the network is the same as that of a single channel approach. In our simulation, we use Constant Bit-Rate (CBR) traffic flows with UDP as transport protocol. Since most of the traffic in mesh network will be directed towards the gateway, we assume that all traffic flows from mesh routers are destined to the Internet through a gateway. The sources of the flows are randomly located in the mesh network. The α parameter is used to calculate LARM and WCETT metrics. This parameter is set to 0.1 while comparing the performance between LARM and WCETT. Note that this value gives better performance for WCETT metric [2]. We considered five simulation scenarios to evaluate the performance of LARM. The common parameters for all the simulations are listed in Table 1.

Table 1

Simulation parameter value.

Simulation time Simulation area Transmission range Traffic type Packet size Packet rate Number of nodes Number of radios Buffer size

250 seconds 1000 m × 1000 m 250 m CBR (UDP) 512 bytes 4, 8, 12, 16, 20 packets/second 100 1, 2, 3, 4, 5 50 packets

4.2 Performance Metrics The following metrics were used in various scenarios to evaluate the performance of LBM and our proposed routing metric: • Percentage of packet delivery: The ratio between the number of data packets successfully received by the destination node and the total number of data packets sent by the source node. This metric reflects the degree of reliability of the routing protocol. • End-to-end delay of data packets: This is defined as the delay between the time at which the data packet originated at the source and the time it reaches the destination, and includes all possible delays caused by queuing for transmission at the node, buffering the packet for a detour, retransmission delay at the MAC layer, propagation delay and transmission delay. This metric represents the quality of routing the protocol. • Total throughput: This is defined as the amount of data that is transmitted through the network per unit time, (i.e. data bytes delivered to their destinations per second). 4.3 Simulation Results 4.3.1 Scenario 1: Varying Number of Radios on Mesh Routers The first scenario varied the number of radios on each mesh router, where a traffic load that included 50 CBR flows with a 4 packet/second packet rate was injected from the mesh routers. As shown in Fig. 4, the performance of the routing metrics rapidly improved when the number of radios on a mesh router was increased. With a small number of radio interfaces on mesh routers, the simulation results showed that LARM significantly outperformed hop-count and WCETT in terms of the packet delivery percentage, average end-toend delay, and total throughput. LARM produced a shorter delay than other routing metric as it favored the routes with a small load, thereby reducing the queuing delay, and collision and packet loss due to a buffer overflow. In contrast, hop-count and WCETT do not capture the traffic load and inter-flow interference, resulting in highly congested regions in which the data packets suffered a long buffering time. As a result, LARM achieved a shorter end-to-end delay compared to hop-count (about 46% with 1 radio and 90% with 3 radios) and WCETT (about 30% with 1 radio and 60% with 3 radios). In addition, LARM improved the packet delivery percentage and throughput (about 50% with 1 radio and 30% with 3 radios) compared to hop-count. When the mesh routers were equipped with more radios, the load-aware channel selection helped balance the traffic across the multiple non-overlapping channels. Consequently, LARM helped to reduce the degree of interference


705

Fig. 4

Simulation results for the scenario 1 (varying number of radio interfaces).

Fig. 5

Simulation results for the scenario 2 (varying the traffic load).

and contention. However, increasing the number of radios interface to more than four does not show any significant further improvement. This is due to the fact that four orthogonal channels are sufficient to provide the required capacity and channel diversity for the network and traffic pattern considered in our simulation. However, the ideal number of radio interfaces will vary for different types of networks with different size, density and traffic load. 4.3.2 Scenario 2: Varying the Packet Rate The second scenario varied the packet rate from 4 to 20 packets/second, while maintaining the number of simultaneous flows at 50 in the wireless mesh network. Figure 5(a) shows the performance of LARM in a single radio environment. When increasing the packet rate, gateway nodes became congested due to the concentration of traffic. Nonetheless, LARM outperformed hop-count and WCETT in terms of multiple metrics due to efficient distri-

bution of the traffic in the network. In a multiple radio environment, increasing the number of radios helped to reduce the high load in the network. However, LARM helped balance the load between the channels. Furthermore, LARM avoided the creation of congested regions, as with hop-count and WCETT, by selecting a route based on the traffic load of interfering neighbors, less interference, and a high transmission rate. As a result, LARM reduced the interference and collision in the wireless network. As shown in Fig. 5(b), under a high load (20 packets/second packet rate), when compared with hopcount, LARM improved the end-to-end delay by 15% and packet delivery percentage and throughput by 45% with 3radio mesh routers. 4.3.3 Scenario 3: Varying the Number of Flows In this scenario, we study the behavior of LARM by varying the number of flows from 10 to 80 with a 4 packets/second


706

Fig. 7

Simulation result for the scenario 4 (varying interfering traffic).

at 80 flows) and WCETT (about 58% at 80 flows). Figure 6(b) shows the percentage of packet delivery of the network. The results indicate that LARM is better than other metrics. For single radio, LARM performs higher percentage of packet delivery compared with hop-count (30% at 70 flows) and WCETT (about 20% at 70 flows). When mesh routers are equipped with multiple radios, it helps to reduce the packet loss ratio as discussed above. For three radios case, LARM improved by 21% compared with hopcount and 5% compared with WCETT at 80 flows. 4.3.4 Scenario 4: Varying the Interfering Traffic Fig. 6 Simulation results for the scenario 3 (varying the number of flows).

packet rate. Figure 6 shows that the end-to-end delay and percentage of packet delivery for a small number of flows is similar for all routing metrics. This is because the amount of traffic introduced into the network is lower than the available bandwidth and capacity of each node. Increasing the number of flows leads to greater contention and traffic concentration at gateway. This therefore decreases the percentage of packet delivery and increases the end-to-end delay. However, LARM provide better performance compared with hop-count and WCETT. As shown in Fig. 6(a), for the single radio case, LARM improved the end-to-end delay by 63% compared with hopcount and 20% compared with WCETT at 80 flows. This is because hop-count and WCETT metrics do not balance traffic flows over the network nodes, they create highly congested areas which the data packet suffered a long delay. Increasing the number of radio leads to eliminate high load, and reduces the delay for buffering packet. LARM performs better than other metrics since it selects the route with less interference, high transmission rate for routing data packets. Figure 6(a) shows that, for three radios case, LARM achieved shorter delay compared to hop-count (about 67%

In the fourth scenario, each node has only one radio and all of them are configured to the same channel. We investigate the behavior of LARM in the presence of interfering traffic by observing the packet delivery percentage of one flow. This flow starts at 137 second from a mesh router to the gateway node. Meanwhile, interfering traffic are 60 flows which are randomly started between 10 and 130 seconds. All interfering traffic are directed towards the gateway node by varying the packet rate from 4 to 20 packets/second. Figure 7 compares the percentage of packet delivery between LARM and the other routing metrics in the presence of interfering traffic. We can see that when the interfering traffic increases, the packet loss ratio of LARM, WCETT, and hop-count also increase. However, LARM outperforms the other routing metrics in presence of interfering traffic. At high level of interfering traffic (5 Mbps), LARM improved the packet delivery ratio by 53% compared with hop-count and 22% compared with WCETT. This occurs because the WCETT and hop-count does not capture the inter-flow interference when there are multiple flows in the network. The result shows that LARM performs well in the presence of interfering traffic by distributing the traffic among the network nodes and avoiding the creation of congested area.


707

routing protocol which is used to find paths that are better in terms of balancing load and reducing the inter-flow and intra-flow interference in multi-radio networks. We showed that LARM provides better performance compared to WCETT and hop-count routing metrics in LBM routing protocol. Acknowledgments This work was partly supported by the ITRC (IT Research Center) program [IITA-2008-(C1090-0801-0036)] of the MKE/IITA and the Korea Science and Engineering Foundation (KOSEF) (No. R01-2006-000-10753-0). Fig. 8

The number of congestion events.

4.3.5 Scenario 5: The Number of Congestion Events Over Time The fifth scenario is to investigate the number of congestion events during simulation. We define a congestion event as when a buffer overflow occurs at a mesh router. In this simulation, each node has only one radio and all of them are configured to the same channel. A traffic load that included 50 CBR flows with a 8 packets/second packet rate was injected from the mesh routers to the gateway node. Flows start randomly between 10 and 150 seconds. Figure 8 shows the cumulative congestion events which is defined as the cumulative occurrence over time of congestion events. The network is not congested when the amount of traffic introduced into the network is small. The simulation result shows that the number of congestion events rise quickly over time because of increasing the traffic load. For hop-count metric, the network congestion starts at 60 second since it selects the shortest path with high congestion. As increasing the traffic load, the network becomes congested due to the concentration of traffic at gateway. Furthermore, WCETT and hop-count does not capture the load of interfering neighbors, it therefore routes data packets through congested regions. In contrast, LARM selects a route based on the traffic load of interfering neighbors, less interference, and a high transmission rate. As shown in Fig. 8, LARM get lower congestion level than WCETT (about 57% at 240 second) and hop-count (about 78% at 240 second). These results are due to the efficient balancing of the traffic in the network by LARM. 5.

Conclusion

In this paper, we investigated the load imbalance problem in multi-radio infrastructure mesh networks. Also, we proposed a load-aware routing metric (LARM) which captures the difference in transmission rates, packet loss ratio, intra/inter-flow interference and traffic load in multi-radio WMNs. This metric is incorporated into a load-balancing

References [1] I.F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Computer Networks Journal, vol.47, pp.445–487, March 2005. [2] R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multi-hop wireless mesh networks,” Proc. ACM MobiCom, Oct. 2004. [3] K. Ramachandran, E. Belding-Royer, K. Almeroth, and M. Buddhikot, “Interference aware channel assignment in multi-radio wireless mesh networks,” Proc. IEEE INFOCOM, April 2006. [4] R. Raniwala and T. Chiueh, “Architechture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network,” Proc. IEEE INFOCOM, March 2005. [5] A.P. Subramanian, H. Gupta, and S.R. Das, “Minimum-interference channel assignment in multi-radio wireless mesh networks,” Technical Report, Department of Computer Science, Stony Brook University, March 2006. [6] C. Perkins, E.M. Royer, and S. Das, “Ad hoc on-demand distance vector (AODV) routing,” IETF RFC 3561, 2003. [7] D.B. Johnson and D.A. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing, vol.353, Kluwer Academic Publishers, 1996. [8] C. Perkins and P. Bhagwat, “Highly dynamic destination-sequence distance vector routing (DSDV) for mobile computers,” Proc. ACM SIGCOMM, Oct. 1994. [9] D.S.J. De Couto, D. Aguayo, J. Bicket, and R. Morris, “A highthroughput path metric for multi-hop wireless routing,” Proc. ACM MobiCom, 2003. [10] Y. Yang, J. Wang, and R. Kravets, “Designing routing metrics for mesh networks,” Proc. WiMesh, 2005. [11] A.P. Subramanian, M.M. Buddhikot, and S.C. Miller, “Interference aware routing in multi-radio wireless mesh network,” Proc. WiMesh, 2006. [12] Y. Yang, J. Wang, and R. Kravets, “Interference-aware load balancing for multihopwireless networks,” Technical Report UIUCDCS-R2005-2526, Department of Computer Science, University of Illinois at Urbana-Champaign, 2005. [13] S.-J. Lee and M. Gerla, “Dynamic load-aware routing in ad hoc networks,” Proc. IEEE ICC, June 2001. [14] V. Saigal, A.K. Nayak, S.K. Pradhan, and R. Mall, “Load balanced routing in mobile ad hoc networks,” Comput. Commun., vol.27, no.3, pp.295–305, Feb. 2004. [15] A.A. Pirzada, M. Portmann, and J. Indulska, “Evaluation of multiradio extensions to AODV for wireless mesh networks,” Proc. ACM MobiWAC, Oct. 2006.


708

Anh-Ngoc Le received the B.S. degree from Vinh University in 1996; M.S. degree from Hanoi University of Technology, Vietnam, in 2001. He is currently a Ph.D. student in School of Electrical Engineering and Computer Science, Kyungpook National University since 2006. He current research interests include routing and MAC protocols for wireless mesh networks.

Dong-Won Kum received his B.S. degree from Woosong University in 2003; M.S degree in School of Electirical Engineering and Computer Science, Kyungpook National University, Daegu, Korea. He is currently a Ph.D student in School of Electirical Engineering and Computer Science, Kyungpook National University, Daegu, Korea. His current research interests include wireless mesh networks and mobility management.

You-Ze Cho received the B.S. degree in electronics engineering from Seoul National University, Korea, in 1982, and the M.S. and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology, in 1985 and 1988, respectively. Since 1989, he has been with the Kyungpook National University, Korea, where he is currently a Professor of School of Electrical Engineering and Computer Science. From August 1992 to January 1994, he has been at the University of Toronto, Canada, as a visiting researcher. From March 2002 to February 2003, he has been at the National Institute of Standards and Technology, USA, as a guest researcher. His research interests include mobility management and traffic engineering for wireless and mobile networks, broadband convergence network, and future Internet.

Chai-Keong Toh received his Ph.D. degree in Computer Science from Cambridge University, England. He is the recipient of the 2005 IEEE Institution Kiyo Tomiyasu Medal for pioneering contributions to communications protocols for ad hoc mobile wireless networks. He is a fellow of IEEE, IET, British Computer Society, New Zealand Computer Society and Hong Kong Institution of Engineers. He was formerly Director of Research at TRW Tactical Systems Corporation based in California, USA, and Chair Professor in Communication Networks at the University of London. He was a Visiting Professor at YALE (2007). He is a member of IEEE Technical Field Awards Council and on the Board of IEEE COMSOC Sister and Related Societies.