2013 8th International Conference on Communications and Networking in China (CHINACOM)
A New Interference-aware Routing Metric for Wireless Mesh Networks Wei Feng, Suili Feng, Yuehua Ding
Yongzhong Zhang
School of Electronic and Information Engineering South China University of Technology Guangdong, Guangzhou, China Email:
[email protected]
No.7 Research Institute China Electronics Technology Group Corporation Guangdong, Guangzhou, China Email:
[email protected]
Abstract—This paper proposes an interference-aware routing metric function in wireless mesh networks (WMNs) in order to select a better path to route the packets. Different from the existing metrics, the new metric function has two advantages. Firstly, it applies cross-layer design to take inter-flow interference, intra-flow interference and load balancing into consideration to capture the characteristics of wireless networks. Secondly, it is proved isotonic by using virtual network decomposition, so that the optimal loop-free path can be found by efficient algorithms with polynomial complexity. The simulation results demonstrate that the proposed metric performs significantly better than the existing routing metrics. Keywords—routing metric; interference; load-balancing; isotonic; cross-layer design
I.
I NTRODUCTION
Routing in wireless mesh networks has been a long hot topic. In order to achieve as high throughput as possible, the existing methods pay more attention to design an efficient routing metric to find the minimum cost path to save resource such as bandwidth, power in a wireless mesh networks. In fact, Yaling Yang et al [1] have already summarized four requirements for a good metric. Firstly, in order to find the optimal path, the routing metric should be capable of correctly capturing the physical characteristics of the networks. Secondly, in order to ensure the stability of the network, the routing metric should not cause frequent route changes when the path is updated. Thirdly, the routing metric should make sure that low-complexity algorithms can be used to find the path with minimum cost. Finally, the routing metric should be designed to ensure that forwarding loops are avoided when used in the routing protocol. To satisfy the later three conditions, a concept of isotonicity has been introduced in [1] and the isotonicity property of a positive routing metric has also been proved sufficient to ensure the routing protocols to be optimal, consistent and loop-free. According to the above standards, a lot of researches have been done to design an efficient routing metric for wireless mesh networks. Given the advantages related to cross-layer approaches, more advanced metrics have been proposed in [2-7]. Expected Transmission Count (ET X) in [2] is defined as the number of transmission attempts (including retransmissions) for successfully delivering a packet through a wireless link. Expected Transmission Time (ET T ) in [3] is derived from ET X, and denotes the duration of time that is needed to transmit a packet 479
through the link. Both ET X and ET T characterize traffic load in the absence of interference. Interference is not as a metric for routing until Weighted Cumulative ETT (W CET T ) in [3] is developed. W CET T improves ET T metric by taking into consideration the intra-flow interference. W CET T introduces the number of nodes that operate on the same channel to capture the intra-flow interference. Unfortunately, W CET T is proved to be not isotonic. In addition, W CET T also does not take inter-flow interference into consideration. The Metric of Interference and Channel-switching (M IC) in [1] is then presented, which incorporates both intra-flow and inter-flow interference. It introduces Interference-aware Resource Usage (IRU ) for capturing inter-flow interference and Channel Switching Cost (CSC) for capturing intra-flow interference. However, it is designed for static networks, so it can’t capture the traffic load dynamically. Compared with the previous metrics, the Interference AWARE metric (iAW ARE) in [4] shows better performance due to the joint design of interference ratio (IR) and ET T , but its non-isotonicity makes low-complexity algorithms infeasible to find the shortest path. Interferer Neighbor Count (IN X) in [5] is defined as the product of ET X of the link and the number of the interferer links weighted by their respective bit rates. Since it uses ET X to estimate the traffic load, so it performs better only in low traffic load conditions. The Routing with interflow and Intraflow Interference Metric (RI 3 M ) in [6] is developed by considering the interflow interference and intra-flow interference. However, when there is no interflow interference along the path, this metric ignores the load balancing completely. The Cumulated Interference Metric (CIM ) in [7] is not isotonic so that it can’t be applied to find the shortest path by using low-complexity algorithms in the WMNs. In this paper, based on the aforementioned requirements, a new routing metric named Cross-Layer Weight Function CLW F is proposed for the multiple radios and multiple physical channels WMNs. Cross-layer routing metric function CLW F captures not only inter-flow\intra-flow interferences but also the traffic load in the multiple radios and multiple channels WMNs. In addition, it is further proved isotonic by using the virtual network decomposition [4], so that the low-complexity algorithms such as Dijkstra algorithm and Bellman-ford algorithm can be applied to find the shortest path in the WMNs. The paper is organized as follows. In Section II, the new routing metric function is presented. In Section III, the 978-1-4799-1406-7 © 2013 IEEE
simulation is made to illustrate the efficiency of the metric. Finally, we conclude our paper in section IV.
IRij = IRl = min(IRl (i), IRl (j))
II. PROPOSED CROSS-LAYER INTERFERENCE-AWARE ROUTING METRIC This section introduces the new cross-layer routing metric CLW F that takes into consideration inter-flow interference, intra-flow interference and traffic load. The CLW F captures the physical characteristics of the networks but unfortunately it is proved to be not isotonic when used directly. However, we show how to apply virtual network decomposition to address this limitation in the late part of this section. A. Problem Formulation The proposed routing metric consists of two parts: the load balancing part and the interference awareness part. The load balancing part is comprised of traffic load weighted by interflow interference on a given node. The interference awareness part contains the intra-flow interference experienced by a given node. Inter-flow interference generally leads to bandwidth starvation of some nodes in the area where the flow goes through, since nodes contend for the bandwidth in the neighbor area. The way to prevent the destructive competition is to balance the traffic load and reduce the inter-flow interference. Intra-flow interference is caused by the use of the same channel along the transmission path. We now define our CLW Fp as: ∑ ∑ CLW F p = (1 − β) W LB ij + β CSC i (1) ∀i∈p
∀(i,j)∈p
where W LBij denotes the load balancing part, the CSCi denotes the intra-flow interference part, β is a tunable factor subject to 0 ≤ β ≤ 1 and can be tuned to balance a tradeoff between the channel diversity performance and the load-balancing performance. 1) Weighted Load Balancing Component: The Weighted Load Balancing Component (W LBij ) denotes interflow interference characteristic and traffic load characteristic of the network simultaneously. It uses the node interference rate (IR) [6] based on physical interference model to depict interflow interference. Firstly, we get the formulation of SIN Rl (j) as: SIN Rij =
N+
∑
Pk (j)
≥β
Channel utilization rate (CU R) represents the ratio of the channel busy time and the testing period T . We obtain CU R by sensing the whole busy time of the channel in physical layer during a just past period T . It is clearly that CU R is proportional to the traffic load on the link. The higher the CU R, the less the traffic can be added to the link. It is defined as: CU Rij = Tbusy /Ttotal (5) where Tbusy is the channel occupied time, Ttotal is the testing period. Queue occupancy rate (QOR) denotes the ratio of the occupied buffer size and the whole buffer size. The QOR shows the data handling capability and the congestion condition at the node simultaneously. If the QOR is high, the network should reduce the traffic load sent to this node. It is defined as: QORij = Qin /Qtotal
(6)
where Qin is the average buffer size of backlogged fluid in a testing period T , and Qtotal is the whole buffer size of link layer. After collected the IR and the CU R in physical layer, QOR in link layer at the end of each period T , nodes formulate the W LBij component as: (7)
(2)
where SIN Rij denotes the signal-to-interference-plus-noise ′ ratio of link (i, j), E denotes the set of interference sources, Pi (j) is the received power from transmitter i, Pk (j) is the received power from interference source k, and N is the received noise power, β is the given SIN R threshold. Then, the node interference rate IRl (j) [6] can be written as: IRl (j) = SIN Rl (j)/SN Rl (j)
(4)
As for traffic load, there are a lot of metrics designed to evaluate it. The traditional one is to compute byte rate per unit time through the corresponding node, but it is obviously imperfect due to the time-varying property of wireless channel. The available bandwidth is varying. For example, 2 Mbps may cause congestion for a link with 2 Mbps available bandwidth, but it is still acceptable for a link with 4 Mbps bandwidth. So, it is unreasonable to estimate the congestion condition by the byte rate through the corresponding node. In this paper, the traffic load is estimated according to channel utilization rate and queue occupancy ratio.
W LBij = (CU Rij + QORij )/IRij
Pi (j)
k̸=i,k∈E ′
the minimum value of node interference rates, then, it can be formulated as:
(3)
where l represents the link (i, j), SIN Rl (j) is defined by (2), SN Rl (j) is equal to Pl (j)/N . We can draw a conclusion from (3) that the node interference rate is inversely proportional to the interference. Since that a packet can be successfully received needs the information interaction between the transmitter and receiver, so we define the link interference rate as 480
Clearly, W LBij denotes the traffic load weighted by the inter-flow interference. If no interference occurs, IRij is equal to 1. The W LBij only represents the traffic load. If 0 < IRij < 1, the traffic load component is weighted by a value greater than 1, so the W LBij component becomes larger, which means that the congestion on the link (i, j) increases. Equation (7) reveals the relationship during the cost, traffic load and inter-flow interference. 2) Channel Switching Cost Component: In order to estimate the intra-flow interference, we introduce the CSC component [4]. CSC accounts for the channel switching cost in MAC layer. It takes into consideration the intra-flow interference by comparing the current link and the previous link along a path. If the current link uses the same channel with the previous link, a higher value of CSC will be allocated to the current link. In fact, the channel diversity gain is improved
CH 1 ( A, B,1) CH 1 (B,C,1)
B
A
We use A1 , A2 , B1 , B2 , C1 and the connections between them to denote the induced virtual nodes and the decomposed weights. The weights of links (A1 , B1 ), (A2 , B2 ), (B, C)capture the W LBij component. While the weights of induced links (A, A1 ), (A, A2 ), (B1 , B), (B2 , B), (C, C1 ) denote the CSCi component. Because node A has no previous link and node C has no subsequent link, CSCA and CSCB are equal to 0. The weight of virtual link (B1 , B) is equal to ω2 since the consecutive links (A, B, 1) and (B, C, 1) are allocated the same channel 1. The weight of virtual link (B2 , B) is equal to ω1 since the consecutive links (A, B, 2) and (B, C, 1) are allocated different channels.
C
CH 2 ( A, B,2) (a) Real network.
0
A 1
1
B 1
2
A
B
0
A 2
2
B 2
1
C
0
C 1
1
(b) Virtual network. Fig. 1.
Example of virtual network decomposition.
by the use of distinct channels on a path. As a result, the intraflow interference is also reduced. The definition of CSC can be written as: { ω1 , if CH(prev(i)) ̸= CH(i) CSCi = (8) ω2 , if CH(prev(i)) = CH(i) where CH(prev(i)) represents the channel used by a node prev(i) to transmit data to its next hop i, CH(i) denotes the channel used by node i to transmit data to its next hop, and 0 ≤ ω1 < ω2 must hold to ensure that a higher value ω2 will be allocated to the current link when two successive links are assigned the same channel. B. Isotonicity As mentioned earlier, isotonicity reflects the ability of a routing metric to compute minimum weight and loop-free paths. The definition of isotonicity in [4] is as follows: Definition 1: A weight function W (x) is isotonic if W (a) ≤ W (b) implies both W (a ⊕ c) ≤ W (b ⊕ c) and ′ ′ ′ W (c ⊕ a) ≤ W (c ⊕ b), for all paths, a, b, c, c . From the definition of CLW F , we can see that the first part of CLW F , W LB, is an independent positive value, so it won’t induce non-isotonicity. However, the second part of CLW F , CSC, which depends on the channel allocated to the consecutive links, introduces the non-isotonicity property. It has been shown in [4] that CSC is not isotonic since it is not independent, and causes the different increments of path weights due to the addition of a link on a path. The new metric CLW F is not isotonic when used directly in real network, however, it has been proved that virtual network decomposition can solve the non-isotonicity problem caused by CSC. Fig. 1 shows the virtual network decomposition of CLW F . (i, j, k) and CH in it denote a link with weight k and channel x respectively. As shown in Fig.1 (a), ω1 and ω2 can’t be depicted directly in the real network since it is caused by the relationship of the channel assignment of neighbor links. Hence, to break up the logical relationship of connecting links we introduce several virtual nodes and virtual links to represent ω1 and ω2 as done in Fig.1 (b). 481
Based on the decomposition, the link weights in virtual network are all non-negative and independent, so the link weight in virtual network must be isotonic. According to [4], by aggregating all of the weights of the links on the corresponding path in the virtual network, we can reconstruct the metric of a real path in a real network. So that the CLW F can be used in efficient algorithms to compute the shortest path from the virtual network.
III.
PERFORMANCE EVALUATION
The system performance with the proposed CLW F is compared with the aforementioned metrics namely ET T , IN X and RI 3 M using ns-2 with the OLSR extension [8]. The performance is evaluated in terms of the system throughput, packet delivery ratio and average end-to-end delay. In the set of simulations, we simulate a mesh network with 100 nodes including 68 stationary nodes, 28 mobile nodes and 4 TAPs randomly deployed in an area of 3,000×3,000 m2 . Each node has three radios and each radio can be configured to one of three orthogonal channels. We have both internal flows and external flows with a rate of 100Kbps in the network. The packet size is set to 1,024 bytes. β, ω1 and ω2 are set to 0.5, 3 and 5 respectively as in [9]. The speed of the mobile node is 5 m/s. Fig. 2, 3 and 4 show the average end-to-end delay, the packet loss ratio and total network throughput with the increasing traffic load respectively. It is obvious that CLW F has the best performance in terms of average end-to-end packet delay, packet loss ratio and total network throughput. ET T has the worst performance since it only characterizes the transmission time in the absence of interference and might direct all the packets of the network towards a single path (i.e., the best path), leading to increased congestion and contention. CLW F , IN X and RI 3 M both capture the interference in some degree. However, IN X and RI 3 M are both flawed. IN X only captures the inter-flow interference by collecting the number of links that interfere with another link, and RI 3 M can’t capture the traffic load and the processing capacity of nodes well, so that they both can not find the best path to forward the data packet, and they show worse performance when the traffic load is heavy.
IV.
C ONCLUSION
In this paper, we proposed an interference-aware routing metric in wireless mesh networks. The proposed metric takes CU R and IR in physical layer, QOR in link layer and CSC in MAC layer into consideration to capture the interference characteristic and traffic load characteristic. And the isotonicity is proved by using virtual network decomposition so that efficient algorithm can be used to find the minimum cost path. The extensive simulations show that the proposed metric performs significantly better than the existing routing metrics. ACKNOWLEDGMENT
Fig. 2.
The research work was supported by National Natural Science Foundation of China under Grant No. 2012AA050801 and the Open Research Program of Key Laboratory of Computer Network of Guangdong Province, China under Grants CCNL201101 and CCNL201102.
Comparison of average end-to-end delay.
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[7] Fig. 3.
Comparison of packet loss ratio. [8] [9]
Fig. 4.
Comparison of network throughput.
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