Wireless Mesh Routing

7/27/2019 Wireless Mesh Routing

1/6

Optimization of Routing Algorithm in Wireless Mesh Networks

Bhupendra Kumar Gupta and B.M.Acharya Manoj Kumar MishraDept. of Computer Applications Dept. of Information TechnologyITER, SOA University ITER, SOA University

{bhpendragupta, biswamohanacharya}@iter.ac.in [email protected]

Abstract

Wireless mesh networks (WMNs) haveattracted increasing attention and deployment as ahigh-performance and low cost solution to last-mile

broadband Internet access. Wireless mesh networkshave emerged as a potential technology for next-generation wireless networking. WMNs have beenwidely accepted in the traditional applicationsectors of ad hoc networks because of their

advantages over other wireless networks. Withincreasing demand for real-time services in the next

generation wireless networks, quality-of-service(QoS) based routing offers significant challenges inWMNs. WMNs consist of mesh routers and meshclients where fixed mesh routers form the multi-hop

backbone of the network and it is assumed that eachindividual mesh client will follow the prescribed

protocols. However, these mobile devices, owned byindividual users, will likely do what is the mostbeneficial to their owners, i.e. act 'selfishly'. Traffic

routing plays a critical role in determining theperformance of wireless mesh networks. Routing inany network has a great impact on the overallnetwork performance, thus a routing protocol or an

algorithm for WMNs should be carefully designedtaking into account the specific characteristics of

that network. In addition, in wireless networks,

serious unfairness can occur between users if theissue is not addressed in the network protocols oralgorithms. In this paper, we formulate the problem

of routing as a network optimization problem, andpresent a general LP (linear programming)

formulation for modeling the problem. We proposethe optimized algorithm for known traffic demandand then explain the performance ratio for this. Therouting algorithms derived from these formulations

usually claim analytical properties such as optimalresource utilization and throughput fairness. Oursimulation results demonstrate that our statistical

problem formulation could effectively incorporatethe traffic demand uncertainty in routing

optimization, and its algorithm outperforms thealgorithm which only considers the static traffic

demand.

1. Introduction

WMNs have become popular due to itsuniversal networking capabilities. It supports a widerange of applications, such as public safety,emergency response communications, intelligent

transportation systems, and community networksetc. It consists of two types of wireless nodesnamely Mesh Routers and Mesh Clients.

Each node does not operate as a host onlybut also acts as a router, which forwards the packetson behalf of other nodes that may not be within thedirect transmission range of their destination.WMNs dynamically self organize and selfconfigure, with the nodes in the networksautomatically, establishing and maintaining meshconnectivity among themselves.

In wireless mesh networks, local accesspoints and stationary wireless mesh routerscommunicate with each other and form a backbone

network which forwards the traffic from mobileclients to the Internet. The backbone of the wirelessmesh network consists of mesh routers, whichconnect each other in an ad hoc manner via wirelesslinks. The presence of backbone mesh routers andutilization of multiple channels and interfaces allowthe wireless mesh network to have better capacitythan that of the infrastructure-free ad hoc networkformed by mesh clients directly. A special type ofthe mesh routers, referred to as gateway nodes, iscapable of Internet connection, and other meshrouters and associated terminal clients have toaccess the Internet through the gateway nodes asgiven in the Fig-1.

1.1. Need for load balanced algorithms

Data communication in WMNs is possible alongmultiple hops, nodes that are in communicationrange of each other can communicate directly, whilethe nodes that are spatially disjoint uses otherintermediate nodes to relay the packets. Applicationof such a network is in personal area networking,emergency operations such as search and rescue indisaster caused areas, policing and fire fighting,military applications such as on the battle field,civilian environment such as taxi cab networks,

meeting rooms, sports stadium etc.Almost all routing protocols in one way orother, try to converge into shortest path routing. Oneof the advantages of using shortest path routing isthat it is good for overall energy efficiency becauseenergy needed to transmit a packet is directlyproportional to path length or number of hops. Butthe shortest

1150978-1-4244-5612-3/09/$26.00 c2009 IEEE


2/6

Fig.1 Structure of WMNs

path routing is restricted to use the same set of hopsto route the data packets, thus causing some of theheavily loaded nodes and thus causing some of the

nodes to die earlier resulting into holes in thenetwork or even worst into partitioning of thenetwork. Thus the need for load balanced routingemerges.A common routing performance metric with respectto a known traffic demand is resource utilization.For example, link utilization is commonly used fortraffic engineering in the Internet [1], where theobjective is to minimize the utilization at the mostcongested link. The existing research on optimalmesh network routing [2] usually aim to maximizethe flow throughput, while satisfying the fairnessconstraints. In this formulation, traffic demand isreflected as the flow weight in the fairness

constraints.We formulate the problem of routing as a

network optimization problem, and present a generalLP (linear programming) formulation for modelingthe problem and then we propose the optimizedalgorithm for known traffic demand and explain theperformance ratio for this. The performance ratiocan be extended to develop the optimized algorithmunder unknown (uncertain) traffic. The routingalgorithms derived from these formulations usuallyclaim analytical properties such as optimal resourceutilization and throughput fairness.

2. Literature Review

Network routing plays a critical role in determiningthe performance of a wireless mesh network. Tostudy the best mesh network routing strategy whichcan maximize the network throughput whilesatisfying the fairness constraints, a lot of researchhas been performed.

The problem of wireless mesh networkrouting has been extensively studied in the existingliteratures. Routing algorithms are proposed to

improve the throughput for wireless mesh networksvia integrating MAC layer information [3], such asexpected packet transmission time [4]. Jointsolutions for channel allocation and routing areexplored in [5] in a centralized fashion and in [6] ina distributed fashion. These heuristic solutions lackthe theoretical foundation to analyze how well thenetwork performs globally (e.g., whether thenetwork resource is fully utilized, whether the flows

share the network in a fair fashion) under theirrouting schemes. There are also theoretical studiesthat formulate these network planning decisions intooptimization problems. In [7] [8] the authors studythe optimal solution of joint channel assignment androuting for maximum throughput under a multi-commodity flow problem formulation and solve itvia linear programming. The authors in [9] presentbandwidth allocation schemes to achieve maximumthroughput and lexicographical max-min fairnessrespectively. A rate limiting scheme to enforce thefairness among different local access points isdiscussed in [10]. These results provide valuableanalytical insights to the mesh network design under

ideal assumptions such as known static traffic input.It is not clear whether they will be suitable for

practical use under highly dynamic traffic situation.Distributed algorithms have been presented

for joint scheduling and routing, and for jointchannel assignment, scheduling and routing in [11].These distributed algorithms only use localinformation for traffic routing and thus have thepotential to accommodate dynamic traffic. However,their crucial properties, such as convergence speedand messaging overhead, are yet to be evaluatedunder realistic traffic conditions.

The existing research proposes to formulatethe mesh network routing problem as an

optimization problem. These works usually makeideal assumptions such as known static traffic input.Whether they could be applied for practical useunder the highly dynamic and uncertain traffic inwireless mesh network is still an open issue. Ourapproach is also related to dynamic trafficengineering [12] in Internet and oblivious routing[13], which also considers the impact of demanduncertainty in making routing decisions. The majordifference between our approach and these existingstudies lay in the different network models ofwireless mesh network, Internet and the problemformulations. In particular, traffic engineering triesto minimize the congestion (utilization) of the wired

links of a network. In multi-hop wireless networks,wireless link utilization can not be used tocharacterize the network performance due to thelocation dependent contention in the vicinity area.The objective of our research is to maximize theratio between flow throughput and its demand,subject to the schedulability and fairness constraints.The model for the wireless mesh network isrepresented hierarchically as shown in Fig-2.

3. Network Model

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) 1151


3/6

Fig. 2 Network model

In wireless mesh network, local access pointsaggregate the traffic from mobile clients that areassociated with them. They communicate with each

other and also with stationary wireless routersnetwork forming a multi-hop wireless backbonewhich forwards the user traffic to a gateway accesspoint connecting to the Internet. In our discussion,local access point, gateway access point, and meshrouters are collectively called mesh nodes.

Here the backbone of a wireless mesh

network has been modeled as a directed grap , where each node represents a meshnode. Among these nodes, is the gatewayaccess point that connects to the Internet. A directed

edge denotes that can transmit to directly. We assume that all mesh nodes have auniform transmission range denoted by . We

denote as the distance between and .An edge . We alsouse to represent the length of edge . Let bit/sec be the data rate of edge , which is themaximum data that can be carried in a second along

the edge .

3.1. Traffic Demand Model

This paper investigates the throughput optimizationrouting scheme for wireless mesh backbonenetwork. Thus we consider the aggregated trafficamong the mesh nodes. In particular, we regard the

gateway access points as the source of all incomingtraffic and the destination of all outgoing traffic of amesh network. Similarly, the local access points,which aggregate the client traffic, serve as thesources of all outgoing traffic and the destinations ofincoming traffic.

For simplicity, we call the aggregatedtraffic that shares the same source and destination as

a flow and denote it as , where is the set ofall aggregated flows. It is worth noting that althoughwe consider only one gateway access point in this

paper, our problem formulation and algorithmpresented here could be easily extended to handlemultiple gateway routers and inter-mesh-routertraffic. Finally, we denote the rate of an aggregated

flow as , and use torepresent the aggregated flow rate vector.

3.2 Interference Model & Schedulability

In a wireless network, packet transmissions aresubject to location responsive interference. Here theassumptions are that all the mesh nodes have the

uniform transmission range denoted by .Generally the interference range is larger than itstransmission range. Here the interference range of a

mesh node is denote as , where is a constant. In this paper, we consider theprotocol modelpresented in [14]. Let be thedistancebetween and such that . In theprotocol model, packet transmission from node to is successful iff

1. The distance between these two nodes

satisfies .2. Any other node within the

interference range of the receiving node ,i.e., ,is not transmitting.

If node can transmit to directly, they form anedge . We denote the capacity of this edgeas which is the maximum data rate throughedge . Let be the set of all edges. We say twoedges interfere with each other, if they cannot transmit simultaneously based on the protocol

model. Further we define the interference set which contains the edges that interfere with edge .

To study the throughput optimization routingproblem, we first need to understand the constraint

of the flow rates. Let denote thewireless link rate vector, where is theaggregated flow rate along wireless link . Link rate

vector is said to be schedulable, if there exists astable schedule that ensures every packettransmission with a bounded delay. Essentially, theconstraint of the flow rates is defined by the

schedulable region of the link rate vector .The link rate schedulability problem has beenstudied by several existing researchers, which leadto different models [15,16,17]. In this paper, we

adopt the model in [16], In particular, [16] presents asufficient condition under which a link schedulingalgorithm is given to achieve stability with boundedand fast approximation of an ideal schedule. Based

on this model we define the subset of ,where each that has length isin set .In the following discussion we refer

as the adjusted interference set of . Based onthe results presented in [16] we have the followingclaims.

1152 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)


4/6

Claim 1. (Sufficient Condition of Schedulability)

The link rate vector is schedulable if thefollowing condition is satisfied: Eq.(1)where bit/sec is the data rate of edge . Forease of exposition, we assume that for all in the following discussion.4. Problem Formulation

We know that is the aggregated traffic flowbetween local access points and the gateway. We

use to denote the demand of flow and to denote the demand vectorconsisting of all flow demands. Consider the

fairness constraint that, for each flow , itsthroughput being routed is in proportion to its

demand . Our goal is to maximize (scalingfactor) where at least amount of throughput canbe routed for flow . We assume an infinitesimally

divisible flow model where the aggregated trafficflow could be routed over multiple paths and we

have used to denote the set of unicast paths that

could route flow .

Let be the rate of flow over path . Obviously the aggregated flow rate alongedge is given by which is the sum of flow rates that are routed

through paths passing edge . Based on thesufficient condition of schedulability in Eq.(1) ofclaim 1, we have

Eq.(2)The throughput optimization routing with fairnessconstraint is then formulated as the following linearprogramming (LP) problem:

Maximize Eq.(3)Subjected to the constraints Eq.(4) Eq.(5) , Eq.(6)Here the optimization objective is to maximize ,such that at least units of data can be routed foreach aggregated flow with demand . Inequality

(4) enforces fairness by requiring that thecomparative ratio of traffic routed for different flowssatisfies the comparative ratio of their demands.

Thus, the absolute value is scaling up orscaling down all demands, while staysunchanged. Inequality (5) enforces capacityconstraint by requiring the traffic aggregation of all

flows passing wireless link satisfy thesufficient condition of schedulability. This problem

formulation follows the classical maximumconcurrent flow problem, which has also been usedin Internet traffic engineering and load balancingrouting.Note that the above problem formulation follows theclassical maximum concurrent flow problem.Although being extensively used to study meshnetwork routing schemes under known and fixedtraffic demand, such throughput optimization

problem formulation is hard to extend to handle thecase of uncertain demand. In light of this need, weproceed to study the congestion minimizationrouting. This differs from the throughputoptimization problem where the traffic demand maynot be completely routed subject to the constraintsof the network capacity. Rather, the congestionminimization problem will route all the trafficdemands, which may violate the network capacityconstraint, and thus the goal is to minimize thenetwork congestion.

For defining above minimization problem

we need to define as the number ofwireless links a path passes in the adjusted

interference set . Therefore the objective here isto minimize the aggregated congestion for alladjusted interference sets i.e. to minimize where be the congestion to each set for Subjected to the constraints that the total congestion

over .i.e for any path for flowmust be at least the congestion of flow and thesecond constraint is that the weighted flow

congestion over its demand must be at leastone. Therefore the minimization problem here canbe modeled as follows

Minimize Eq.(7)Subjected to the constraints

Eq.(8)

Eq.(9)where is the weighted flow congestion overdemand .

Based on the above dual problem we can write thefollowing algorithm

4.1 Algorithm based on the above simplex

problem

1. Initialize the congestion for each edge

as .2. Initialize the traffic on all

to zero.

3. For each flow 3.1 Rout units of data by finding

lowest congested path in the path

set , then fill the traffic to this

path to its bottleneck capacity.

3.2 Then update the congestion for theedges appeared in this path.



5/6

3.3 Fill the traffic to flow until all

units are routed.

5. Simulation Study

We evaluate the performance of our algorithmthrough simulation study. In the simulated wireless

mesh network, mesh nodes are randomly

deployed over a region, among whichnodes are local access points that forward trafficfor clients. Node which resides in the center ofthe deploy region is selected as the gateway router.Each mesh nodes has a transmission range of

. The simulated network topology is shownin Fig-3.

Fig.3 Mesh Network Topology

Fig.4: Traffic Time Series

Fig. 5: Traffic Demand Distribution

To simulate the traffic demand at each local accesspoint, we analyze the traces collected in the campuswireless LAN. By analyzing the log trace at eachaccess point, we are able to derive its incoming and

outgoing traffic volume in a -minute period. Weargue that the local access points of a wireless meshnetwork serve a similar role as the access points of

wireless LAN at aggregating and forwarding client

traffic. Thus, we select access points and assigntheir traffic traces to the local access points in oursimulation. Fig-4 plots the time series of the trafficvolume during one hour period at the same time of a

day (12pm-1pm) from access points sample andderive its probability density function. Fig-5 plots

the probability density function for the corresponding access points of Fig-4. or

consecutive work days. We remove the weekenddays from the traces due to their extreme low trafficvolume. From the figure, we observe that the trafficat each access point is highly dynamic andunpredictable due to the insufficient level ofaggregation. This observation motivates the need ofmesh routing schemes that are aware of the trafficuncertainty.

Based on the one-hour traffic volume datafrom the traces, we further derive the traffic demanddistribution for each access point. We divide theobtained raw traffic volume into finite collection oftraffic demand ranges.This algorithm keeps track of the dynamically

changing demand and maximizes the throughputbased on the current demand of each access point,meanwhile maintaining the fairness among them.Since the access point demand keeps changing, ithas to continuously rerun the given algorithm toadapt to the new demand. This solution yields theoptimal routing result at the cost of frequent routingcomputation and update.

6. Conclusions and Future Work

In this paper we have studied the throughputoptimization routing problem for wireless meshnetworks, which is different from existing algorithmwith the assumption of certain traffic demand knowna-priori. In order to maximize the outgoing flow wehave designed the maximized simplex problemwhere we have maximized the scaling factorkeeping in mind the constraints. To achieve thisobjective the corresponding minimized problem forcongestion has been designed and extensivesimulation results confirm our claim.

Here we have considered the certain trafficdemand, which may not be the real scenario alwaysas the demand for traffic changes dynamically.Hence in future we aim to extend our claim foruncertain traffic demand to focus on the realscenario.

References

[1] X. Lin and S. Rasool, A distributed jointchannel assignment scheduling and routingalgorithm for multichannel ad hoc wirelessnetworks inINFOCOM, 2007.

1154 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)


6/6

[2] Y. Azar, E. Cohen, A. Fiat, H. Kaplan, and H.Racke. Optimal oblivious routing in polynomialtime.J. Comput. Syst.Sci., 69(3), 2004.

[3] S. Biswas and R. Morris. Exor: opportunisticmulti-hop routing for wireless networks, in Proc. ofACM SIGCOMM, pages 133144, 2005.

[4] R . Draves, J. Padhye and B. Zill, Routing inmulti-radio multi-hop wireless mesh networks, inProc. of ACM Mobicom, pages 114128, 2004.

[4] A. Raniwala, K. Gopalan, and T. Chiueh.Centralized channel assignment and routingalgorithms for multi-channel wireless meshnetworks, Mobile Computing and communicationsReview, 8(2):5065, 2004.

[6] A. Raniwala and T. Chiueh, Architecture andalgorithms for an IEEE 802.11 based multi-channelwireless mesh network, in Proc. of IEEEINFOCOM, 2005.

[7] M. Alicherry, R. Bhatia and L. Li, Joint channelassignment and routing for throughput optimizationin multi-radio wireless mesh networks, in Proc. ofACM MobiCom, 2005.

[8] M. Kodialam and T. Nandagopal,Characterizing the capacity region in multi-radiomulti-channel wireless mesh networks, in Proc. ofIEEE WiMesh, 2005.

[9] J. Tang, G. Xue, and W. Zhang, Maximumthroughput and fair bandwidth allocation in multi-channel wireless mesh networks, in Proc. of IEEEINFOCOM, 2006.

[10] V. Gambiroza, B. Sadeghi, and E. W. Knightly.End-to-end performance and fairness in multihopwireless backhaul networks, in Proc. of ACMMobiCom, 2004.

[11] X. Lin and S. Rasool, A distributed jointchannel assignment, scheduling and routingalgorithm for multichannel ad hoc wirelessnetworks, inINFOCOM, 2007.

[12] H. Wang, H. Xie, L. Qiu, Y. R. Yang, Y.

Zhang, and A. Greenberg, Cope:Trafficengineering in dynamic networks, in Proc. of ACMSIGCOMM, 2006.

[13] Y. Azar, E. Cohen, A. Fiat, H. Kaplan, and H.Racke, Optimal oblivious routing in polynomialtime, J. Comput. Syst. Sci., 69(3), 2004.

[14] D. Aguayo, J. Bicket, S. Biswas, G. Judd, R.Morris, Link-level measurements from an 802.11bmesh network, in ACM Annual Conference of the

Special Interest Group on Data Communication(SIGCOMM), August 2004, pp. 121131.

[15] K. Jain, J. Padhye, V. Padmanabhan, and L.Qiu, Impact on interference on multi-hop wirelessnetwork performance. in Proc. of Mobicom,September 2003.

[16] V. S. A. Kumar, M. V. Marathe, S.

Parthasarathy, and A. Srinivasan. Algorithmicaspects of capacity in wireless networks, in Proc.of ACM SIGMETRICS, pp 133144, 2005.

[17] Y. Xue, B. Li, and K. Nahrstedt, Optimalresource allocation in wireless ad hoc networks: Aprice-based approach, IEEE Transactions onMobile Computing , 5(4):347364, April 2006.

[18] I. Chlamtac, M. Conti, J. Liu, Mobile ad hocnetworking: imperatives and challenges, Ad HocNetworks 1 (1) (2003) 1364.

[19] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam,

E. Cayirci, Wireless sensor networks: a survey,Computer Networks 38 (4) (2002) 393422.

[20] I.F. Akyildiz, I.H. Kasimoglu, Wireless sensorand actor networks: research challenges, Ad HocNetworks 2 (2004) 351367.

[21] L. Krishnamurthy, S. Conner, M. Yarvis, J.Chhabra, C. Ellison, C. Brabenac, E. Tsui, Meetingthe demands of the digital home with high-speedmulti-hop wireless networks, Intel TechnologyJournal 6 (4) (2002) 5768.

[22] S. Tierney, Mesh Networks, whitepaper ofcommunitynetworking.org.

[23] J. Jun, M.L. Sichitiu, The nominal capacity ofwireless mesh networks, IEEE WirelessCommunications 10 (5) (2003) 814.

[24] R. Poor, Wireless mesh networks, Sensors,February 2003.

[25] R. Poor, Wireless mesh links everydaydevices, Electronic Engineering Times, 5 July2004.


Documents

Wireless Mesh Routing