- Research Article
- Open Access
A Novel Approach to Fair Routing in Wireless Mesh Networks
© J. Määttä and T. Bräysy. 2009
- Received: 2 June 2008
- Accepted: 8 December 2008
- Published: 24 December 2008
Multiradio wireless mesh network (WMN) is a feasible choice for several applications, as routers with multiple network interface cards have become cheaper. Routing in any network has a great impact on the overall network performance, thus a routing protocol or algorithm for WMN should be carefully designed taking into account the specific characteristics of the network. In addition, in wireless networks, serious unfairness can occur between users if the issue is not addressed in the network protocols or algorithms. In this paper, we are proposing a novel centralized routing algorithm, called Subscriber Aware Fair Routing in WMN (SAFARI), for multiradio WMN that assures fairness, leads to a feasible scheduling, and does not collapse the aggregate network throughput with a strict fairness criterion. We show that our protocol is feasible and practical, and exhaustive simulations show that the performance is improved compared to traditional routing algorithms.
- Medium Access Control
- User Rate
- Medium Access Control Protocol
- Channel Assignment
- Wireless Mesh Network
Wireless mesh network (WMN)  has recently appeared as a promising technology, which can increase coverage area and capacity of existing wireless networks. With the help of of-the-shelf wireless mesh routers, large, previously possibly unreachable, areas can have wireless access to, for example, the Internet. As these routers are becoming less expensive, the introduction of multiple radios to each router is becoming economically possible. multiradio concept with multiple noninterfering channels can significantly improve the overall network capacity, thus current WMN research has been concentrated to multiradio WMN.
In wireless networks, users or subscribers can experience unfairness depending on their location in the network. Users with multiple hops to destination are given less bandwidth than those with fewer hops. The unfairness stems from the shared wireless medium and unfair network protocols that are designed to maximize network capacity, that is, the aggregate throughput or do not take into account the fairness at all. Maximizing capacity and ensuring fairness are contradictory requirements and usually maximizing capacity has been preferred . Unfairness is also present in multihop multichannel WMN. Users with multiple hops can be completely starved, while capacity, in terms of throughput, is maximized. This is naturally not fair, especially if the users pay the same amount for the service.
Usually routing in WMN has been seen from the point of view of the mesh routers (e.g., in ). As they are, mesh routers do not generate traffic, they only forward traffic of users and other routers. Thus, routing should be seen from the point of view of the users, who are also the paying customers. In addition, subscribers can be unevenly distributed in the network; the number of subscribers registered to a mesh router can vary significantly. This is neglected in most of capacity and routing studies, where one user per router is assumed (e.g., in ). Therefore, as the number of subscribers per router increase, so should its share to the limited network capacity. As discussed above, there is a need for a new or improved routing protocol or algorithm, which takes into account the special characteristics and applications of WMN as well as the distinct needs of users.
The rest of this paper is organized as follows. In Section 2 some related studies are discussed briefly. Section 3 presents needed concepts and definitions. Section 4 presents the SAFARI algorithm and shows simulation results. Section 5 interprets the simulation results and draws conclusions.
Fairness in medium access control (MAC), scheduling and network layer has been studied to some extent (e.g., [5–7]). These papers observe fairness in the different layers of the protocol stack and propose their solutions. However, fairness is a cross-layer problem, and thus MAC-layer solutions are useless if higher layer protocols are unfair. This is not true vice versa; an ideal transport protocol can enforce fairness even if the underlying MAC protocol is unfair .
Several papers have appeared that have taken linear programming (LP) approach to routing and fairness. One of the benchmark paper in LP-based routing, with several linear constraints, is presented in . The paper addresses two interrelated questions: what is the maximum throughput capacity of an arbitrary (ad hoc) network with given source-destination pairs can this maximum throughput capacity be achieved by jointly routing packets and scheduling transmissions?
The authors devise an LP formulation that maximizes aggregate rates and incorporates any requirements that can be modeled as linear constraints. The paper provides a proof that using their LP formulation, all needed packet transmissions can be feasibly scheduled and that their solution to the maximum concurrent flow problem is a constant factor away from the optimal. The problem in their proposed scheme is that the authors use an infinitesimally divisible flow model for data transmission. This means that data packet can be divided into pieces and transmitted along all possible paths between source and destination, which lead to very complex receiver structures and possibly to a long delay between the arrival of the first and the last data segment. In addition, storing and updating of all possible routing paths leads to large routing tables and network overhead.
In , optimized routing in WMN is considered with fairness constraints. The paper points out that past work can be categorized into two different strategies: heuristic and optimization problem. Heuristic methods lack the theoretical foundation to analyze how well the method is working, while optimization problems can be far too complex in practise or make too much simplified assumptions. The paper inspects and analyzes optimal routing with uncertain traffic demand and fairness constraints, thus the authors end up with a stochastic maximum concurrent flow optimization problem. Unfortunately, their LP-formulation seeks to maximize scaling factor , which defines the fraction of traffic that can be transmitted for each flow, instead of guaranteeing fairness.
In , a topology control algorithm (TCA) and a new routing metric suitable for WMN, namely, collision domain (CD), are presented. The term topology control refers to any set of network operations that lead to a connected topology, for example, node placement, channel assignment, power control, and routing. It is shown that the proposed TCA performs better than conventionally used metrics, that is, hop count and interference, in the terms of minimum collision domain. On the other hand, the paper makes simplified assumptions such as one user per router, absolute fairness is said to be enforced and only one radio per router is assumed.
Our work is mainly based on the work by Malekesmaeili et al.  and Kumar et al. . From , the topology control concept and collision domain routing metric are taken as baseline for routing with modifications. From , linear programming-based approach to rout and rate maximization are adopted with modifications to constraints and routing path selection. The essence of this work is to develop a fair subscriber-aware routing algorithm for WMN, in which the positions of subscribers are taken into account in order to ensure fairness without crippling the network performance. The algorithm is called Subscriber Aware Fair Routing in WMN (SAFARI).
In this section, basic definitions and concepts are introduced and explained. We consider multiradio WMN modeled as a graph , where is the set of nodes and the set of wireless links (edges). Each link has a certain amount of data to send, , and each has a set of interfering links , which is based on the transmitter-receiver (Tx-Rx) model .
3.1. Network Model
In the context of wireless networks, fairness means that every user receives a fair share of the network resources (e.g., time and frequency), taking into account user's service requirements. Different services can have very different requirements, for example, voice calls have strict delay requirements and relatively low data rates, while file downloading has high bandwidth and low delay requirements. These different requirements should be taken into account, when designing a fair network protocol.
It is important to notice that assuring fairness is a cross-layer problem, since unfairness occurs in MAC (e.g., channel access and scheduling) and transport layers (e.g., congestion control). Current network protocols (e.g., IEEE 802.11) ensure user fairness only on one-hop communication or seek to maximize aggregate throughput of the network .
Three popular definitions of fairness are absolute, max-min, and proportional fairness. Absolute fairness is defined as equal rates among all users, max-min fairness is enforced if no user can increase its rate without decreasing some other users' smaller rate at the same time, and a set of allocated rates is proportionally fair if any other feasible rate allocation results in zero- or negative-aggregate change.
where is the set of user rates, , where is the number of users. When , some user's rate are allowed to starve and when , absolute fairness is enforced. Together with linear programming-based rate allocation, our fairness index enforces proportional fairness when and also satisfies quality of service (QoS) requirements if minimum allowable rate is set to QoS threshold.
3.3. Collision Domain
where (e) is the set of edges interfering with edge , for all .
where is the amount of data on link .
where is the set of CD loads, that is, , is the number of edges in the network.
The technology-dependent link capacity (theoretical maximum throughput (TMT)) is calculated in , and it was used in  to assess link capacity, which is also limiting the network capacity since the accumulated traffic of a link cannot exceed the link capacity. In Figure 2, the total load of the collision domain is 20 U since it is the accumulated traffic of links , , , , , and , where U is the amount of data that mesh user transmits and it is same to all users (Figure 2) for simplicity of notation. Thus, the throughput per node is bounded by . Note that this is not necessarily the BCD of the network. The above calculation needs to be done to every link in order to find the BCD.
where the required data rate for each user is .
3.4. Routing Metrics
A good routing metric for WMN is aware of network topology, takes into account network characteristics, and is isotonic . Isotonicity means that the order of path lengths of two paths is preserved if they are appended or prefixed by a common third path. An isotonic metric assures loop-free routing, simple implementation, and minimum weight paths using Dijkstra's algorithm.
Proposed routing metrics for WMN are hop count, distance, weighted cumulative expected transmission time (WCETT), and CD. Hop count is used in AODV , but it fails to address WMN characteristics and network congestion. Distance-based metric is usually used with modified Dijkstra's algorithm and it suffers from same things as hop count-based metric. WCETT was proposed by Draves et al.  and it is a combination of loss rate with a priori-known packet loss probability, bandwidth, and interference of a link. Unfortunately, WCETT is not isotonic as shown in . CD was proposed as a routing metric by , which is an excellent choice since it models wireless interference, MAC layer collisions, and is isotonic. Based on the above discussion, CD is used in this work as a routing metric.
3.5. Linear Programming
where is the rate of user i and is the theoretical maximum throughput (i.e., physical data rate a link can transmit ), is the minimum required rate, and is the maximum feasible rate. However, (7)) is the capacity constraint, (8) is the fairness constraint, and (9) is constraining the rates. Solving this optimization problem leads to a rate allocation that can be feasibly scheduled, as shown later on.
3.6. Channel Assignment
The main purpose of any channel assignment (CA) algorithm is to minimize interference, maximize aggregate throughput, as well as capacity or fairness. The assignment of radios and channels to mesh nodes is far from trivial. In , it is proved that simply assigning first channel to the first node and second channel to second node, and so forth, is far from optimal.
where is the aggregate traffic that traverses through a certain node , is the minimum number of hops from node that needs to be done in order to reach a gateway, and is the number of radios in node . MeshTiC has been chosen here since it takes into account the traffic load on links, can be modified to incorporate interference, and has low complexity.
Next, the centralized SAFARI algorithm is explained in detail, pseudocode and simulation results are presented. The SAFARI algorithm uses CD as a cumulative routing metric, assigns channels to links using a modified version of the MeshTiC algorithm, and uses a linear programming framework to assign rates to users taking into account capacity, fairness, and rate constraints, see (6), (7), (8), (9).
MeshTiC algorithm is modified such that in (13), is estimated by using CD of link based on the initial geographical positions of users and is estimated as distance to the nearest gateway. This way CA is fixed until user positions change dramatically, and channels can be assigned before routing and rate allocation.
Abbreviations used in the pseudocode.
Set of edges
A graph representing
on each link for all
Set of mesh users (i.e., sources)
New routing order based on
to which router each user is
Set of all nodes in the network
Collect network information: and .
Compute initial estimate of .
Assign channels to , update accordingly.
Solve best known paths using and FW.
for to do
For user , choose the router from which the
to any gateway is shortest.
Connect to this router.
Sort users such that users in low CD regions are
Store the order in .
for to do
Calculate paths from to all gateway
Choose optimal gateway router and select the
Solve the LP-problem in order to find optimized
On line 2, link weight matrix is calculated based on the initial positions of users, CD and Tx-Rx models. The ultimately determines the routing path selection and it is modified several times in SAFARI so that it always reflects the current network condition. The first calculation of does not take into account CA, since the used MeshTiC CA algorithm needs an estimate of the traffic demand and it is estimated using CD based on the initial positions of users.
On line 3, channels are assigned using modified MeshTiC and is updated to match CA. Channels can be now assigned to , since we have an estimate of traffic in the network. needs now to be updated to match CA. In other words, the Tx-Rx model takes into account the CA, that is, links interfere only if links are within the interference range and use the same channel.
On line 4, the best known paths are solved using and FW's algorithm. In this context, the best-known paths are the "shortest" paths to gateways and they are used in the determination of the best router for each user to attach to (lines 6–8). Modified FW is used, since it can be made to incorporate CD metric and performs necessary routing with relatively short time, that is, running time of FW is . The determination of the router each user attaches to is decided now by simply selecting the router from which the path to any gateway is "shortest". With this kind of router selection, the randomness of user positions is diminished and overall network throughput is increased, since in most cases, the router selection procedure leads to smaller number of hops for user to reach destination.
The routing order is decided on line 10. Low CD areas, that is, links and corresponding nodes with low are routed first since these areas are usually at the border of a network, thus their routing is essential. This comes from the fact that when the users are far away from gateways, the number of hops increases. Now, if far away users are routed last, their number of hops increases even more. Keep in mind that as the number of hops increase, the capacity constraints become stricter and the throughput decreases while delay increases. Thus, the aggregate number of hops in the network should be minimized, and routing far-away-users first is one way to do it.
On lines 13–15, every user is routed in the decided order to the best gateway and is updated to reflect current network condition. Each user is routed individually using FW's algorithm and the best gateway is selected according to cumulative CD metric. The main reason for using FW is that even a large number of gateways does not increase running time of the algorithm. This is the final routing path selection. After every user's routing, is updated according to and along the chosen path.
Line 17 executes LP-problem, which allocates the highest possible rates subject to capacity and fairness constraints. Solving the LP-problem (6), (7), (8), (9), optimal rate allocation with chosen paths is performed.
- (1)Positions of users are taken into account in
CA by traffic load estimation with the help of CD,
determination of which router each user attaches to.
Determination of routing order.
The positions of users are taken into account in CA so that in the rank calculation (13), the traffic load is estimated with CD. In addition, the positions of users help to determine the router each user attaches to. This is determined by finding the best-known paths to the best gateways using FW's algorithm with CD estimated by user positions. With our router selection scheme, the number of users attached to each router is not random, as in cases where simply the closest router is chosen, but determined by considering transmit powers, available gateways, and other users' positions.
4.1. Feasibility of the Algorithm
When comparing the SAFARI algorithm to any wireless network routing algorithm, several similarities and differences arise. Every routing algorithm needs to collect network information, at least and , in order to be able to route data from source to destination. Also, every routing algorithm should have at least an estimate of link weights, that is, hop count, distance, interference, bandwidth, or CD, in order to compute . Finally, every routing algorithm needs a path selection algorithm (e.g., Dijkstra or FW). These properties are also implemented in SAFARI, and thus there is no extra complexity in that regard.
There are a few factors that increase SAFARI's complexity compared to, for example, a simple distance-based routing algorithm. The calculation of best-known paths and the following router selection for each user increases complexity compared to algorithms where simply the closest or the farthest router is selected. Sorting users so that low CD regions are routed first increases complexity only slightly since all the necessary information is already calculated and stored in . The biggest factor increasing complexity is the recursive path selection with FW and updating . This recursion is done because it allows the routing algorithm to adapt to changing traffic conditions. In addition, rate allocation by LP-problem solving increases the complexity and running time especially with a large number of users. Based on the above discussion, it can be stated that the performance gain of SAFARI, as shown later, comes with the cost of increased complexity. Nevertheless, this increase in complexity is not too great to make SAFARI infeasible for practical implementation since FW's algorithm is the most complex with a running time proportional to . Thus, SAFARI can be solved in polynomial time.
Since SAFARI's rate allocation is based on the LP-formulation by Kumar et al. , it can be shown that this rate allocation leads to feasible scheduling. The feasible scheduling of SAFARI is formalized in Theorem 1.
The LP formulation (6), (7), (8), (9) (i.e., rate allocation) used in the SAFARI algorithm results in a stable schedule, that is, flows are given enough transmission opportunities in a finite period of time. In addition, the rate allocation is a constant factor away from the optimal solution to the corresponding flow problem.
The proof of feasible scheduling in a TDMA-based system is based on [8, Lemma 1]. The intuition behind the proof is that link flows can only be scheduled in finite time if there are enough transmission opportunities for each flow, that is, there is enough bandwidth on the link. The detailed proof is available in .
where , is a subset of edges in which are greater than or equal to in length.
Link capacity (TMT)
Number of radios
1, 3 and 12
Number of users
Step size users
In the simulations, it is assumed that each user has the same QoS requirement and the corresponding data rate is tried to achieve with limitations from the LP constraints. The number of routers is kept relatively low since when there is too many routers leads to very long simulation times. On the other hand, using only a few routers is not practical, since then the routing algorithm is tied to only a couple of possible paths. In the simulations, defined numbers of users are dropped uniformly into an area covered by a certain predefined topology (e.g., Figures 3(a) and 3(b)), are allowed to exceed this area by 100 m, and routed to destinations using the algorithm in question. This is done several hundred times since the distribution of users has a significant effect on performance.
There are three algorithms that are used throughout the following simulations. The first one implements SAFARI algorithm and is referred to as SAFARI in the following. The second one implements the TCA proposed in  and is referred to as CD metric in what follows. The third one is a simple distance-based algorithm that uses Dijkstra's algorithm, and is referred to as distance metric in what follows.
The following simulation results are obtained in a grid topology with four gateways and 45 routers as shown in Figure 3(a) (see ), and a grid topology with one gateway and 48 routers as shown in Figure 3(b), where red circles are gateways and green diamonds are routers.
Figure 5 shows the corresponding routing paths with the same user positions. Now, users are connected to the nearest router and are then routed using CD as a metric. It is obvious that using CD metric leads to lower power consumption while the number of hops is increased. Similar to SAFARI, CD metric also guides users to noninterfering paths. The main difference between these two schemes is that with CD metric, the number of hops is greater, thus finding noninterfering paths is harder. This is shown so that a fewer number of noninterfering paths are selected.
Figure 6 illustrates the path selection with distance metric scheme. It can be seen that also this scheme selects the nearest router for each user to attach to. Then, the paths are selected blindly without considering CA and link congestion. This leads to shorter paths than using CD metric scheme but some links are heavily congested, and thus limiting the network capacity. The distance metric scheme is the simplest scheme while worst on the performance, as seen later on.
Table 3 shows the achieved throughput and BCD using SAFARI, CD metric, and distance metric schemes with the shown user positions in Figures 4–6. SAFARI achieves almost twice as much throughput than the two others and has significantly lower-average BCD. CD metric performs slightly better than distance metric. As mentioned before, this performance gain comes with the cost of increased transmit power and algorithmic complexity.
In Figure 8, the same traffic profile is presented with CD metric. This scheme performs load balancing, which is shown especially in gateways, indices 1–4, as the number of users per gateway is equal in the long run. With CD metric, routers are not starved in any location and the traffic is divided smoothly among routers. This is another advantage of CD as a routing metric, it inherently performs load balancing. The difference to SAFARI, which also uses CD as a metric, is the router-selection procedure and routing order, which disables full-load balancing among routers.
In Figure 9, the traffic profile using distance metric model is shown. It is obvious that this model fails to achieve load balancing, which is shown in uneven gateway utilization and heavy congestion in some routers. This illustrates the effect of using blind distance-based routing and not taking into account network condition.
These results show that SAFARI is superior to the two other schemes with this topology, number of users, , and number of channels. Next, the performance of SAFARI is shown in scenarios where several parameters are changed.
4.2.2. Number of Hops and Starved Flows
Average number of hops using the three simulation models.
Percentage of starved users.
Average distance in meters to gateway of starved users.
4.2.3. Effect of Number of Channels
Next, the number of channels is limited to one in order to see how the three considered models perform in a single-channel environment. Naturally, there is no need for a CA in this case and all links that are within each other's interference range interfere with each other.
Users are positioned so that it can be exploited, for example, near gateways or far away from each other, thus using SAFARI leads to high throughput.
Users are poorly positioned, for example, forming clusters, and taking into account their positions, does not lead to a significant performance gain.
It can bee seen by comparing Figures 15-16 to Figures 10, 12, that using 12 channels instead of one results in 500% throughput increase when . This shows the benefit of multiradio concept (i.e., with increasing cost comes increased performance). One should notice that even though the number of orthogonal channels is increased from one to 12, the throughput is not increased with the same ratio. This stems from the fact that 12 channels does not result in empty , that is, some links still interfere with each other which leads to strict capacity constraints and lower throughput enhancement.
Figures 17 and 18 point out that the higher throughputs of SAFARI and CD metric, in Figures 15 and 16, compared to distance metric come with the cost of increased . One might notice that the standard deviations of SAFARI and CD metric fluctuate somewhat, while distance metric results in smooth curves. The fluctuation illustrates the significant effect on user positions within the network, even averaging over 750 random drop of users (Table 2) it cannot completely average the achieved throughput.
Figure 19 shows that SAFARI has the lowest average BCD, and CD metric increases gap to distance metric. This result reassures the benefit of CD as a routing metric compared to simple distance-based metric. Comparing Figures 14 and 19, it is clear that increasing the number of channels from one to 12 decreases the average BCD to one third with SAFARI and CD metric. Distance metric case does not decrease its average BCD as much as the others.
The simulation results show that the proposed routing algorithm SAFARI outperforms CD and distance-based routing algorithms in terms of the increased network throughput and the number of admitted users. The performance gain comes mainly from the fact that users positions are taken into account instead of neglecting them, as in the CD and distance-based routing. The information of user position is exploited in the CA and in the selection of the best router to each user to attach to. The second factor that contributes to the performance gain is the routing order. By first routing the users in low CD regions (i.e., usually users far away from the gateways), shorter paths are obtained and which leads to less strict capacity constraint and fairness is easier to achieve. The CD metric is shown to be a suitable metric for WMN and its inherent capability to avoid congested areas in the network is a very useful quality. In addition, SAFARI's LP-based rate allocation leads to user rates that can be scheduled.
The performance gain comes with the cost of increased complexity, transmit power, and statistical variation of the achieved throughput. The increase in complexity can be remarkable when compared to a simple hop count-based routing with fixed rates. Factors effecting the complexity are the router selection procedure, LP-based rate allocation, and the recursive calling of FW's algorithm and CD estimate update in the final routing phase. The increased complexity and the need for more transmit power can be too much for some systems or users. Nevertheless, SAFARI can be solved in polynomial time. If the increased complexity of SAFARI is too much for a system, CD metric-based routing can be used with reasonable performance. An estimate of the CD of each link can be obtained by a centralized entity or by spectrum sensing at each node.
The scientific contribution of this work is the developed SAFARI algorithm. The novelty of SAFARI comes from the usage of the information of user positions in CA, router selection, and routing. Another new feature is the routing order selection that is based on the network congestion so that users in low-congestion areas are routed first. This routing order selection leads to higher throughput, mainly since users in low-congested areas are usually at the edge of a network and thus routing them first leads to shorter routing paths on the average.
Since our rate allocation is based on the one proposed in , the assigned rates can be feasibly scheduled. On a more widespread scope for future research, the overall feasibility and practicality of SAFARI needs to be investigated in more detail.
This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes), Nokia, Nokia Siemens Networks, Elektrobit, and the Centre for Wireless Communications.
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