A distributed routing protocol for providing QoS in Wireless Mesh Networks operating above 10 GHz

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2008; 8:1233–1245Published online 26 October 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/wcm.562

A distributed routing protocol for providing QoS in WirelessMesh Networks operating above 10GHz

Markos P. Anastasopoulos, Athanasios D. Panagopoulos*,y and Panayotis G. Cottis

Wireless and Satellite Communications Group, Division of Information Transmission Systems and Materials

Technology, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon

Polytechniou Street, Zografou 15780, Greece

Summary

Wireless Mesh Networks (WMNs) are considered for a plethora of complex applications, where different QoS

specifications should be satisfied. In such applications, QoS issues concerning the delay and the reliability of data

transmission are of utmost importance. In this paper, a distributed routing protocol for WMNs operating above

10GHz to support services with distributed QoS is presented. As far as propagation is concerned, the major factor

limiting the performance of millimetre wave radio systems is rain attenuation. The routing problem is formulated

as a problem of linear programming, where the objective is to determine the path with minimum transmission

delay while at the same time, satisfying a given set of constraints. Lagrangian Relaxation and dual decomposition

are used to solve the problem in a distributed way. The proposed algorithm exhibits a low computational

complexity, it is scalable and guarantees convergence to an optimal routing scheme. Its performance is verified

through extended simulations employing an accurate spatial-temporal channel model. Copyright # 2007 John

Wiley & Sons, Ltd.

KEY WORDS: distributed QoS routing; WMN; minimum delay; Lagrangian Relaxation; dual decomposition;

rain fades

1. Introduction

Wireless mesh networking (WMN) is an emerging

technology using wireless multi-hop networking to

efficiently provide Internet access and shared network

resources to community and corporate users. WMNs

have been proposed for a large variety of applications

including home networking, community and neigh-

bourhood networking, security and surveillance

systems [1]. A typical community WMN is depicted

in Figure 1.

WMNs comprise two types of nodes: mesh routers

and mesh clients. Besides the conventional wireless

routing capability, a mesh router is supplied with

additional routing functions to support mesh network-

ing. Through multi-hop transmission, the same cover-

age can be achieved using mesh routers with much

lower transmission power. To further improve the

*Correspondence to: A. D. Panagopoulos, Wireless and Satellite Communications Group, Division of Information Transmis-sion Systems and Materials Technology, School of Electrical & Computer Engineering, National Technical University ofAthens, 9 Iroon Polytechniou Street, Zografou 15780, Greece.yE-mails: [email protected], [email protected], [email protected]

Copyright # 2007 John Wiley & Sons, Ltd.

mesh networking flexibility, a mesh router is usually

equipped with multiple wireless interfaces built on

either the same or different wireless access technolo-

gies. Industrial standards groups, such as IEEE

802.11, IEEE 802.15 and IEEE 802.16, are actively

working on new specifications for WMNs.

In IEEE 802.16, the frequency range 10–66GHz is

suggested. At these frequencies, the dominant me-

chanism impairing the performance of wireless links

is attenuation due to rain. Several techniques have

been proposed to mitigate rain fading, including

adaptive coding and modulation, frequency, time

and site diversity etc. [2].

To support the high-data-rate requirements within

the IEEE 802.16 standard, Application Specific Inte-

grated Circuits (ASIC) [3,4] have been employed.

However, hardware-based implementations are ex-

pensive, they often lack flexibility and the hardware

development cycle is onerous [5]. Software-based

implementations enable elegant reuse of silicon area

and reduces significantly the time-to-market through

software modification. Therefore, to overcome these

difficulties, softFEC (software Forward Error Correc-

tion) has been proposed [6–8]. FEC techniques are

generally based on the use of error correction codes.

The key idea behind FEC codes is that, at the sending

end, k blocks of source data are encoded to produce n

blocks of encoded data so that any subset of k encoded

blocks suffices to reconstruct the source data. Such a

code is called an (n, k) code and allows the receiver to

recover from up to n� k losses in a group of n encoded

blocks. SoftFEC exhibits the advantages of traditional

FEC schemes and the weaknesses of a software-based

implementation, as well. In particular, its main dis-

advantage is that encoding and decoding times are not

negligible and cannot be ignored. It has been shown

[7] that the encoding time is a linear function of n� k,

while the decoding time depends on l � minðk; n� kÞ,that is, on the actual number of missing packets.

Experimental results verify that a software implemen-

tation of FEC is computationally demanding. None-

theless, the current trend of increased processing

Fig. 1. Wireless Mesh Network scenario.

1234 M. P. ANASTASOPOULOS, A. D. PANAGOPOULOS AND P. G. COTTIS

Copyright # 2007 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. 2008; 8:1233–1245

DOI: 10.1002/wcm

speeds and reduced cost renders softFEC one of the

key technologies for distributed QoS provisioning in

cost-effective broadband wireless networks.

In Reference [9], the authors consider jointly opti-

mal design of cross layer congestion control, routing

and scheduling for ad hoc wireless networks. By dual

decomposition, the resource allocation problem is

decomposed into three subproblems: congestion con-

trol, routing and scheduling which are related through

a specific congestion price. In Reference [10], the

authors proposed a Simultaneous Routing and Re-

source Allocation (SRRA) via dual decomposition.

Furthermore in References [11] and [12], dual decom-

position is employed to solve the problem of flow

control optimisation and maximum lifetime routing in

sensor networks, respectively, in a distributed way.

The same method is adopted in Reference [13] to

investigate the tradeoff between utility and lifetime

for sensor networks.

In the present paper, a distributed routing protocol

with QoS constraints is presented. The goal is to find

the flow route that minimises the total transmission

delay under the flow conservation condition as im-

posed by specific PER constraints. This optimisation

problem is formulated as a linear programming pro-

blem and is solved in a distributed manner using

Lagrangian Relaxation and dual decomposition, two

methods that have been extensively used in the litera-

ture to deal with linear optimisation problems. The

emphasis is placed on analytically incorporating a

spatio-temporal rain-fading model in WMN routing.

As attenuation due to rain increases, the Signal to

Noise Ratio (SNR) at the decoder input is reduced,

resulting in an increased packet error ratio (PER). At

each node, this increase can be dealt with, by making

use of more robust error correction schemes. It is

evident that this intermediate additional node opera-

tion will increase the transmission delay, since either

the sender and the relays or the destination mode will

have to process more redundant bits. The proposed

protocol avoids these nodes, routing the packets with

minimum overhead.

The rest of the paper is organised as follows. In

Section 2, the minimum transmission delay problem

under specific packet error ratio constraints is mod-

elled using linear programming. In Section 3, the dual

problem is formulated. Then using the subgradient

method a distributed solution of the above problem is

given. The proposed distributed algorithm is evaluated

for its stability and scalability in Section 4. Also in

Section 4, the simulation environment for a typical

WMN operating above 10GHz is presented, where

emphasis is given on channel modelling. Finally,

conclusions are given in Section 5.

2. Formulation of the Problem

In this section, the problem of minimum transmission

delay of unicast routing under specific PER con-

straints is formulated.

2.1. Network Topology

Consider a fixed WMN modelled as a directed graph

G(N, L) consisting of a set N of nodes and a set L of

pairs of distinct nodes from N. There exists a directed

edge ði; jÞ 2 L from node i to node j, if a single-hop

transmission from i to j is possible. The set of links

connected to node i are defined as Vi. It is assumed

that the network graph is connected, i.e. a path

between any pair of nodes in N always exists. Let rijbe the information transmission rate and PERij the

packet-error-ratio assigned by the routing algorithm to

the (i, j). Then, the total transmitted data yij are given

by

yij ¼ rij 1þ "ij� � ð1Þ

where "ij is a coefficient quantifying the redun-

dancy necessary to maintain PERij below a certain

threshold PERth determined by the type of services to

be provided.

2.2. The Minimum Transmission Delay Problem

Each node i acts as a regenerator and is assumed to

have a processor that can decode and encode data with

relevant speeds up to Pid and Pie (Mbps), respectively.

Let Si be the non-negative rate of information injected

into the network at node i and destined for the sink.

According to the flow conservation law, the sink flow

is given by

Ssink ¼ �X

i2N;i6¼sink

Si

where the summation is over all the destination nodes

except the sink node.

Assuming uniform arrivals, the delay Tie at node i

required for the encoding of rij data is obtained from

Tie ¼

Pj2Vi

"ijrij

Pie

ð2Þ

DISTRIBUTED ROUTING IN WIRELESS MESH NETWORKS 1235


DOI: 10.1002/wcm

Taking into account Equation (1), Equation (2)

yields

Tie ¼

Pj2Vi

"ij1þ"ij

yij

Pie

ð3Þ

Correspondingly, in the worst case scenario, where all

redundant packets will be used to reconstruct the origi-

nal information, the decoding delay Tid is defined by

Tid ¼

Pj2Vi

"ji1þ"ji

yji

Pid

ð4Þ

It should be noted that Pid is slightly smaller than

Pie due to the additional overhead in the decoding

phase. Hence, Pid and Pie could be expressed using the

equivalent processing metric Pi, where

Pi ¼ Pie ¼ Pidð1þ �iÞ; �i > 0 ð5Þ

Then the total delay at node i is written as

TPi ¼

Pj2Vi

"ij1þ "ij

yij þ 1þ �ið ÞXj2Vi

"ji1þ "ji

yji

Pi

ð6Þ

The goal is to find a flow algorithm that minimises

the total packet transmission delay, while, at the same

time, it satisfies the flow conservation condition and

the QoS constraint. Therefore, the above-constrained

minimum delay transmission problem, hereafter

called primal problem P, is defined as follows:

Problem P :

minimiseXi2N

TPi ð7:1Þ

under the constraints :

Xj2Vi

1

1þ "ijyij � 1

1þ "jiyji

� �¼ Si 8i 2 N ð7:2Þ

0 � yij � Yij 8ði; jÞ 2 L ð7:3Þ

Conditions (7.2) and (7.3) are referred to in the

literature as conservation of flow and capacity con-

straint, respectively. A flow vector satisfying both

these constraints is called feasible. If there exists at least

one feasible flow vector, the minimum cost flow problem

is called feasible; otherwise it is called infeasible.

Through an approach similar to that presented in

Reference [12], the minimisation problem P presented

via (7.1) through (7.3) is equivalent to the following

linear programming

problem :

minimiseXi2N

TPi ð8:1Þ

under the constraints :

Xj2Vi

"ij1þ "ij


"ji1þ "ji

yji � TPiPi

ð8:2Þ

Xj2Vi

1

1þ "ijyij

� ��Xj2Vi

1

1þ "jiyji

� �¼ Si 8i 2 N

ð8:3Þ

0 � yij � Yij 8ði; jÞ 2 L ð8:4Þ

Constraint (8.2) denotes that node i can process up

to TPiPi Mbits in the interval TPi. Note that the

variable TPi in Equation (6) should be considered as

an independent variable in order to see the equation as

linear programming problem.

2.3. Satisfying the PER Constraint

For any service class, a specific PERservice constraint

must be satisfied. PER, however, is a multiplicative

constraint denoting the probability that a corrupted

packet reaches the destination node, is the product of

the probabilities concerning individual links. In the

worst case scenario, a packet will be routed via all

network nodes. Let PERth denote a PER threshold to

be calculated later on. Suppose that along a link ði; jÞsatisfies

PERij ¼ PERth 8ði; jÞ 2 L ð9Þ

Then, PERservice is given by

PERservice ¼ PERN�1th



DOI: 10.1002/wcm

or

PERth ¼ ðPERserviceÞ1=ðN�1Þ ð10Þ

Assuming that, along link (i, j), a certain code FEC(k,

n), 1 � k � n, is applied, the corresponding packet

loss probability is

PERij ¼ pij 1�Xn�k�1

m¼0

n� 1

m

� �pij

m 1� pij� �n�m�1

!

ð11Þ

where pij denotes the BER along link (i, j).

Solving Equation (11), for n, one obtains "ij from

"ij ¼ n

k� 1:

Due to the worst case assumption that a packet is

routed via all the network nodes, the end-to-end

packet loss probability is higher than what is actually

achieved, resulting in bandwidth misuse. However,

using the above procedure and solving the linear

programming problem (8), a flow with minimum

transmission delay from the source to the destination

node is established. If inequality (8.2) is always true

(e.g. by selecting Pi to be large enough) and the

bandwidth constraints are not violated by the redun-

dancy inserted onto the network packets (due to

stricter PERij), the minimum delay path for a specific

end-to-end PER is the same as that calculated for a

higher PER. Hence, having determined the routing

flow for higher PER, the number of hops is known and

a new value of PERth is calculated in order to cover

the actual requirements.

3. A Distributed Solution

In this section, an algorithm that solves the linear

problem (8) in a distributed way is presented, adopting

the Lagrangian Relaxation.

3.1. Dual Problem

Using Lagrangian Relaxation, the side constraints

(8.2) and (8.3) are eliminated by adding to the cost

function the terms

�i

Xj2Vi

1

1þ "ijyij

� ��Xj2Vi

1

1þ "jiyji

� �� Si

( )

�i

Xj2Vi

"ij1þ "ij


"ji1þ "ji

yji � TPiP

( )

Thereby, the following Lagrangian function is formed

which can be rewritten as

where � ¼ ð�1; . . . ; �NÞ and � ¼ ð�1; . . . ; �NÞ are

vectors of non-negative scalars. Each pair (�i; �i)

may be viewed as a penalty for each violation of the

corresponding side constraints (8.2) and (8.3). It may

also be viewed as a Lagrange multiplier.

Finally, the following dual function is formed:

qð�; �Þ ¼ InfT ;y;�;�

LðT; y; �; �Þ j 0 � yij � Yij8ði; jÞ 2 L� �

L T ; y; �; �ð Þ ¼Xi2N

TPi þXi2N

�i

Xj2Vi

1

1þ "ijyij

� ��Xj2Vi

1

1þ "jiyji

� �� Si

( )

þXi2N

�i

Xj2Vi

"ij1þ "ij


"ji1þ "ji

yji � TpiP

! ð12Þ

LðT ; y; �; �Þ ¼ �Xi2N

�iSi þXi2N

TPi þXi2N

Xj2Vi

yij�i

1þ "ij� �j

1þ "ji

� �

�Xi2N

�iTPiPi þXi2N

Xj2Vi

yij �i

"ij1þ "ij

þ 1þ �ið Þ�j

"ji1þ "ji

� �



DOI: 10.1002/wcm

Then the dual problem is considered

maximise qð�; �Þ ð13:1Þ

under the constraints

�i � 0 i ¼ 1; . . . ;N ð13:2Þ

�i � 0 i ¼ 1; . . . ;N ð13:3Þ

Since, the network graph is always assumed connected

and there exists a feasible solution (T, y, �, �), theSlater’s condition [15] for constraint qualification is

satisfied for the minimum delay problem, if the non-

linear constraints hold with strict inequality (in this

case, the only non-linear constraint is the processing

constraint (8.2)).

Xj2Vi

"ij1þ "ij


"ji1þ "ji

yji < TPiPi

Nevertheless, the latter is valid due to the assumption

the high-speed processors are selected, as mentioned

in Subsection 2.3. Hence, strong duality holds. Thus,

the primal problem given in Equation (8) can be solved

by solving the dual one expressed in Equation (13).

3.2. Solving the Dual Problem

In this section, a new method for solving the above

dual problem will be presented. The most common

methods used to solve non-differentiable convex pro-

blems are the subgradient algorithm and the cutting

plane algorithm. The former has been adopted in the

present approach.

The dual problem given in Equation (13) is not

strictly concave. Hence, it is only piecewise differ-

entiable. To overcome the above difficulty

� Pi2N T2Pi is minimised instead of minimising the

initial objective functionP

i2N TPi� A strictly concave regularisation term is added to

the initial objective function (for related methods

see [10–16]).

Then, the Lagrangian function is given by

where e ! 0. Thus, the objective function of the dual

problem is

We now turn to algorithms that use subgradients to

solve the dual problem. The subgradient method

consists of the iterations

�ðkþ1Þi ¼ �

ðkÞi � sðkÞf ðkÞi

h iþð16:1Þ

�ðkþ1Þi ¼ �

ðkÞi � sðkÞhðkÞi

h iþð16:2Þ

where fðkÞi and h

ðkÞi are any subgradients of the second

and third term of Equation (15) at ð�ðkÞ; �ðkÞÞ defined as

fðkÞi ¼

Xj2Ni

"ij1þ "ij

yðkÞij þ 1þ �ið Þ

Xj2Nj

"ji1þ "ji

yðkÞji � T

ðkÞPi Pi

ð17:1Þ

hðkÞi ¼

Xj2Ni

1

1þ "ijyðkÞij � 1

1þ "jiyðkÞji

� �� Si ð17:2Þ

LðT ; y; �; �Þ ¼ �Xi2N

�iSi þXi2N

TPi TPi � �iPið Þ

þXi2N

Xj2Vi

ey2ij þ yij1

1þ "ij�i þ "ij�i

� �� 1

1þ "ji�j � 1þ �ið Þ�j"ji� �� ð14Þ

qð�; vÞ ¼ �Xi2N

�iSi þXi2N

infT ;�

TPi TPi � �iPið Þ j�i � 0f g

þXi2N

Xj2Vi

infy;�;�

ey2ij þ yij1

1þ "ij�i þ "ij�i

� �� 1

1þ "ji�j � 1þ �ið Þ�j"ji� ��

0 � yij � Yij; 8 i; jð Þ 2 L

�i � 0; �i � 0

��

ð15Þ



DOI: 10.1002/wcm

sðkÞ is a positive scalar stepsize and ½x�þ is the opera-

tion that sets to 0 all the negative components of the

vector x. A convergence condition commonly used is

[10, 16, 17]

sðkÞ ! 0;X1k¼1

sðkÞ ! 1

More sophisticated approaches to improve converge

can be found in [15, Ch. 10]

The values of TðkÞPi and y

ðkÞij are readily determined

solving the following equations:

TðkÞPi ¼ argmin TPi TPi � �

ðkÞi Pi

� D Eð18:1Þ

Concluding, the above algorithm is executed at

each node starting with the initial vectors ð�ðoÞ; �ðoÞÞ.Then, the values of T

ðkÞPi , y

ðkÞij calculated via (18.1) and

(18.2), respectively, are substituted in Equations

(17.1) and (17.2). Note that, in order yðkÞij to be

calculated, messages are exchanged between neigh-

bourhood nodes at each round containing parameters

�ðkÞj and �

ðkÞj . Furthermore, it is seen from Equations

(17.1) and (17.2) that, if the flow in node i exceeds

(does not exceed) its processing capabilities, fðkÞi takes

a negative (positive) value and �ðkÞi is reduced (in-

creased). This results in a reduction (increase) of yðkÞij

as seen from Equation (18.2).

The above procedure is repeated until convergence

is achieved. Similarly, if at a node the net flow, that is

the outgoing minus the incoming flow, is greater (less)

than the total generated packets in that node, then

hðkÞi < 0 (h

ðkÞi > 0) and �

ðkÞi is reduced (increased)

until the conservation flow condition is satisfied.

4. Performance Evaluation

4.1. Channel Modelling

The performance of the proposed algorithm was

checked using a Matlab based simulation scenario.

The operational frequency of the system was assumed

to be 40GHz. As already mentioned, at this frequency

the main performance impairment of a wireless link is

rain attenuation. For this reason, a dynamic rain rate

field has been implemented. Protocols underlying the

channel exhibit both spatial and temporal variations.

For the simulation, the spatial characteristics of rain

were simulated using HYCELL, a model for the

structure rain fields and rain cells developed by

ONERA [18–20]. HYCELL is used to produce two

dimensional rain rate fields, Rðx; yÞ over an area

corresponding to the size of a typical WMN. That

is, Rðx; yÞ denotes the rainfall rate at a specific point

ðx; yÞ. The produced rain fields follow the properties

of the local climatic conditions by using the long-term

parameters proposed by ITU-R in rainmaps [21]. The

temporal characteristics of the rainfall are described

based on the Maseng–Bakken model [22].

Having implemented the appropriate model for

Rðx; y; tÞ, the next step is to determine AijðtÞ, that is,the rain attenuation induced along link ði; jÞ at everytime t. This is achieved by integrating the specific rain

attenuation A0 ðx; y; tÞ (in dB/km) over the path length

Lij of link ði; jÞ taking into account the characteristics

of the rain medium through Rðx; y; tÞ

AijðtÞ ¼ZLij0i

A0ðx; y; tÞ dl ð19:1Þ

where

A0ðx; y; tÞ ¼ aRðx; y; tÞb ð19:2Þ

is the specific rain attenuation in (dB/km). a, b are para-

meters depending on frequency, elevation angle,

incident polarisation, temperature and raindrop size distri-

bution [23]. Finally, the dynamic properties of the rain

attenuation induced on the microwave path are calcu-

lated by incorporating the model in Reference [24].

4.2. Numerical Results

Consider the WMN shown in Figure 2. The network

consists of N ¼ 36 nodes and L ¼ 85 bidirectional

links. The nodes are supposed to be uniformly dis-

tributed over a 625 km2 surface. Each node acts as a

regenerator, that is, it decodes and then encodes the

received information to achieve a specified PER

yðkÞij ¼ argmin

0�yij�Yij;8ði;jÞ2L

ey2ijðkÞ þ y

ðkÞij

1

1þ "ij�ðkÞi þ "ij�

ðkÞi

� � 1

1þ "ji�ðkÞj � 1þ �ið Þ�ðkÞ

j "ji

� � �� ð18:2Þ



DOI: 10.1002/wcm

value. Furthermore, under clear sky conditions and

assuming that the system uses DPSK modulation, the

BER along link ði; jÞ is given by

BER ¼ 0:5exp½�ðEb=noÞj� ð20Þ

where ðEb=noÞj is the bit energy to noise spectral densityratio at the decoder input of node j under clear sky con-

ditions. BER depends on the transmitter power and antenna

pattern, the free space loss, the receiver antenna pattern

and noise temperature and losses due to catastrophic

failure. Under rain fades, the attenuation due to rain,

Aij must be taken into account introducing it into

Equation (20). Thus, BER along link ði; jÞ is given by

BERij ¼ 1

2e� Eb=noð Þj�Aij½ � ð21Þ

For the purpose of simulation, a flow from the source

to the destination nodes must be set up. The data

transmission rate is assumed at 5Mbps and the para-

meter PERservice is set at 10�5. The system can decode

and encode at speeds up to 9.06 and 10.78Mbps,

respectively. These speeds match the performance of a

Pentium 133 running FreeBSD. The maximum trans-

mission rate over the whole WMN is assumed to be

10Mbps.

In Figures 3a and b, the path exhibiting the mini-

mum transmission delay is shown, when the system

operates under clear sky conditions. It is evident that

the path with the minimum number of hops is

selected. Since all the nodes have the same infrastruc-

ture, this should be expected. Note that the thickness

of an edge on the figure is proportional to the amount

of flow on the corresponding wireless link.

The optimal path under rain conditions is depicted

in Figure 4a and b. It may be observed that the

computed flows avoid the paths suffering more from

rain fades. Note that the contour lines represent the

level of rainfall rate (mm/h). The evolution of La-

grangian Multipliers � and �, concerning the destina-

tion node for the simulation scenario presented in

Fig. 2. WMN topology.



DOI: 10.1002/wcm

Fig. 3. Simulated routing under clear sky conditions.



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Fig. 4. Simulated routing under rain conditions.



DOI: 10.1002/wcm

Figure 4a, is depicted in Figure 5. The stepsize in

iteration k is sk ¼ 0:1=k. It may be easily observed

that they both converge rapidly to their optimal value.

In Figure 6, the normalised flow in edge (29, 36) for

the route considered in Figure 4a is depicted. After

about 3400 iterations, the flow converges to the

optimal solution.

Finally in Figure 7, the average end-to-end transmis-

sion delays concerning a hypothetical WMN deployed

in areas subjected to different climatic conditions such

Fig. 5. Convergence of Lagrange Multipliers. Evolution of parameters �36, �36 in the simulation scenario depicted in Figure 4a.

Fig. 6. Computed flow along link (29, 36) for the simulation scenario presented in Figure 4a.



DOI: 10.1002/wcm

as Athens, GR, Amsterdam, ND and Paris, FR, are

given. As expected, in areas suffering more from

intensive rainfalls, the total delay is significantly larger,

due to the processing time of increased redundant

information. The long term rainfall rate exceedance

probabilities required for the simulation are taken from

Reference [21], while the model in Reference [24] is

used to derive the properties of rain attenuation.

5. Conclusions

A distributed routing protocol for QoS provisioning in

WMNs operating in above 10GHz has been pre-

sented. Due to the harsh transmission medium in the

millimetre wave range, a software based mitigation

technique called SoftFEC has been applied. Even

though this technique efficiently increases data trans-

mission reliability, its main disadvantage is the intro-

duction of significant delay due to encoding and

decoding. Since delay increases linearly with the

amount of redundant information, in the case where

we are interested in setting up a network in an area

that suffer from intensive rainfalls, this metric plays a

crucial role in the systems overall performance.

The performance of the proposed protocol was

investigated using a Matlab/Cþ þ based simulator.

Extensive simulation results have demonstrated that

the routing algorithm converges in a limited number

of iterations. Furthermore, it was observed that the

nodes aim at avoiding rain by routing data via the

most reliable path. Finally, network designers should

seriously consider both long term and the dynamic

characteristics of rainfall fades when installing WMNs,

assuming that QoS provisioning is an objective.

Acknowledgments

Markos P. Anastastopoulos wishes to acknowledge

Propondis Foundation for providing kind support of

this research.

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Fig. 7. Average value of the normalised end-to-end transmission delay for a hypothetical WMN deployed in Athens, Amsterdamand Paris.



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Authors’ Biographies

Markos P. Anastasopoulos was bornin Athens, Greece, in February 1982.He received the Dipl. Ing. Degree inElectrical and Computer Engineeringfrom the National Technical Universityof Athens (NTUA), Zografou, Greece,in 2004 and the M.Sc. in Techno-Eco-nomics in 2006. He is currently work-ing toward the Dr. Ing. Degree at the

same university. He has been awarded for his academicprogress by the Kyprianides, Eugenides and Propondisfoundations. His research interests include applications ofgame theory in wireless networks, routing and resourceallocation issues for ad-hoc and sensor networks. He is amember of Technical Chamber of Greece (TEE).

Athanasios D. Panagopoulos was bornin Athens, Greece on January 26, 1975.He received the Diploma Degree inElectrical and Computer Engineering(summa cum laude) and the Dr. Engi-neering Degree from National Techni-cal University of Athens (NTUA) inJuly 1997 and in April 2002. FromMay 2002 to July 2003, he had servedthe Technical Corps of Hellenic Army.

In September 2003, he joined School of Pedagogical andTechnological Education, as Assistant Professor. He is alsoResearch Assistant in the Wireless & Satellite Communica-tions Group of NTUA. He has authored and co-authoredmore than 100 papers in international journals, transactionsand conference proceedings. He is the recipient of URSIGeneral Assembly Young Scientist Award in 2002 and 2005,respectively. His research interests include radio commu-nication systems design, wireless and satellite communica-tions networks and the propagation effects on multipleaccess systems and on communication protocols for routingand resource allocation issues. He is a member of IEEE andmember of Technical Chamber of Greece and also partici-pates in ITU-R Study Group 3 as Greek Delegate.

Panayotis G. Cottis was born in Thes-saloniki, Greece, in 1956. He receivedthe Dipl. (mechanical and electrical engi-neering) and Dr.Eng. Degrees from theNational Technical University of Athens(NTUA), Greece, in 1979 and 1984,respectively, and the M.Sc. Degreefrom the University of Manchester,(UMIST), Manchester, U.K., in 1980.

In 1986, he joined the School of Electrical and ComputerEngineering, NTUA, where he has been a Professor since1996. He has published more than 90 papers in internationaljournals and transactions. His research interests includeelectromagnetic scattering, microwave theory and applica-tions, wave propagation in anisotropic media, wireless net-works and satellite communications.Dr. Cottis is member of the Technical Chamber of Greece.From September 2003 to September 2006, he was the Vice-Rector of NTUA.



DOI: 10.1002/wcm

Documents

A distributed routing protocol for providing QoS in Wireless Mesh Networks operating above 10 GHz