Wireless Mesh Network - KNOM

Korea Advanced Institute of Science and Technology Network Systems Lab.

Wireless Mesh Network

Professor Song ChongNetwork Systems Laboratory

EECS, [email protected]


Architecture, Applications & Protocols


WMN ArchitectureWMNs (Wireless Mesh Networks) consist of: mesh routers and mesh clientsMesh routers

Conventional wireless AP (Access Point) functionsAdditional mesh routing functions to support multi-hop communicationsUsually multiple wireless interfaces built on either the same or different radio technologies

Mesh clientsCan also work as a router for client WMN Usually one wireless interface

Classification of WMN architectureInfrastructure/Backbone WMNsClient WMNsHybrid WMNs


Infrastructure/Backbone WMNsInfrastructure/Backbone WMNs


Hybrid WMNsHybrid WMNs


WMN CharacteristicsMulti-hop wireless network

Extend coverage with lower transmission powerProvide non-line-of-sight (NLOS) connectivity

Ad hoc network with self-forming, self-healing, and self-organization capability

Low upfront investmentEasy deploymentFault tolerance

Minimal mobilityMesh routers have minimal mobilityMesh clients can be stationary or mobile


WMN CharacteristicsWireless internetwork

Internetwork of heterogeneous wireless networks (e.g., Wi-Fi networks, WiMax networks, cellular networks, Zig-Bee networks etc.)

Traffic patternMost traffic is user-to-gateway or gateway-to-userIn ad hoc networks, most traffic is user-to-user

Not an energy-limited networkMesh routers are usually AC-poweredEnergy efficiency is not an issue in protocol design


WMN AdvantageVery low installation and maintenance cost.

No wiring! Wiring is always expensive/labor intensive, time consuming, inflexible.

Easy to provide coverage in outdoors and hard-to-wire areas.

Ubiquitous access.

Rapid deployment.Self-healing, resilient, extensible.


Application ScenariosBroadband home networking


Application ScenariosCommunity and neighborhood networking


Application ScenariosEnterprise networking


Application ScenariosMetropolitan area networking


Application ScenariosPublic safety


Application ScenariosSensor networking for environment monitoring (e.g., meteorological observatory network)

Beacon (Seosoo-Do)

Deukjeuk-Do

Sunmi-Do

Jaweol-Do

Deujeuk Buoy

Inchon MO

Muei-Do

Youngjong-Do

Bu-Do

Daebu-Do

YoungHung-Do

Palmi-Do


Application ScenariosTransportation systemsBuilding automationsHealth and medical systemsSecurity surveillance systemsSpontaneous networking


Market Opportunities

Municipalities,Public Safety

23%

Education15%

Warehousing andIndustrial

25%

Hospitality25%

Healthcare12%

Hotels, malls, conventions

Hospitals,Nursing homes

Access, security, surveillance

Asset tracking, RFID, sensor nets, dispatch

Access, campus,mobile classroom

Estimated $1B market by 2009 [source: IDC, ABI]


Industry PlayersFiretideIntelKiyonLocust WorldMesh DynamicsMicrosoftMotorola /Mesh NetworksNokia RooftopNortel Networks

Packet HopSkyPilot NetworksStrix SystemsTropos Networks

Not a comprehensive list.Technical details usually proprietary.Solutions typically based on standard802.11, single or multiple radios, standard routing solutions.


Commercial Technologies

Same as for client access

1 802.11b/g5210 MetroMesh Router

Tropos Networks

Up to 3 802.11aUp to 3 802.11b/gOWS 3600Strix Systems

1 802.11a 1 802.11bWireless AP 7220Nortel

Same as for client access

1 802.11a/b/g HotPort 3203Firetide

1 802.11a 1 802.11b/g Aironet 1500Cisco

Up to 3 proprietary 5GHz

1 802.11b/g BelAir 200BelAir Networks

Radios for backhaul

Radios for client access

ProductVendor


Service ModelsPrivate ISP (paid service)City/county/municipality effortsGrassroots community effortsMay be shared infrastructure for multiple uses

Internet accessGovernment, public safety, law enforcementEducation, community peer-to-peer


WMN Testbed StudiesField Testbeds

Technology For All – Houston, Texas (non-profit)Mesh network for low income residences, libraries, and small businesses

Research Testbeds Microsoft, MIT, UIUC, Stony Brook, UCSD, Rice, UCSB, KAIST …)

Report card so far

A+ connectivity, deployability, economics

C performance

I service model, security, protocols, …


Capacity of WMN under 802.11 MAC [Jun03]

Assume each node has external load GAssume MAC layer capacity is BBottleneck collision domain

Collision domain by link 2-3{2-3, GW-1, 1-2, 3-4, 4-5}Traffic: 4G+5G+6G+7G+8G = 30G

Throughput per node: G ≤ B/30


Capacity of WMN under 802.11 MAC

The collision domain of link 17-18 is the bottleneck with traffic of 97GThroughput per node: G≤B/97


Link Quality Measurement in Indoor WMN [Morris03]

Packet delivery ratio for individual links for a 29 node (406 links) indoor mesh network shown.Links widely vary in loss characteristics. Also, many asymmetric links.

Delivery ratio in one direction

Delivery ratio in the other direction


Link Quality Measurement in Outdoor WMN [Morris04]


Routing ProtocolsRevisit ad hoc network routing protocolsTwo main thrusts

Proactive routingBased on traditional distance vector (DV) or link states (LS) routing protocolsAlways maintain shortest-path routesDSDV, OLSR, TBRPF

Reactive (on-demand) routingDiscover and maintain routes only “on-demand”Save routing loadAODV, DSR

Special considerations in WMNWMN routers differ from MANET routers

Power supplyMobility

Separation of WMN routers and clients


Routing MetricsLQSR (Link Quality Source Routing) [Daves04]

DSR with link quality metrics• Hop count • per-hop RTT• per-hop packet pair• ETX (Expected Transmission Count)

Lessons• ETX metric performs best in static scenarios.• Hop count metric performs best in mobile scenarios.• RTT performs worst.• PacketPair metric suffers from self-interference on

multi-hop paths.


ETX: Expected Transmission Count [Morris03]

ETX of a link = Expected no. of transmissions required to send a packet over that link.Assumes a link layer ACK (e.g., 802.11) -> link has to work in both directions.

ETX of path = sum of link ETXs

rf ddETX

×=

1

fd = forward delivery ratio (measurement based)

rd = reverse delivery ratio (measurement based)


ETT: Expected Transmission Time [Davies04-2]

Adjust ETX for bandwidth.Probe size = S, link bandwidth = B

Each transmission lasts for S/B time.

ETT = (S/B)*ETXCan add channel access delay to this.

This delay includes any channel sensing delay, backoff.

Cost of path = sum of constituent link ETTs.


Extremely Opportunistic Routing (ExOR) [Morris05]

Source broadcasts each packet without intended receiver.Learn the set of nodes which actually received the packet.The receiver in the set that is closest (in terms of ETX) to thedestination is selected to forward the packet.This continues until the destination receives the packet.

ExOR provides more throughput than conventional routingEach transmission has more independent chances of being received and forwarded (broadcast gain)Take advantage of transmissions that reach unexpectedly far (opportunistic gain)ETX: In ExOR, (1-(1-0.25)^4)^-1+1 = 2.46

In conventional routing, 4+1 = 5


TCP over Wireless TCP interprets any packet loss as a sign of congestion.

TCP sender reduces congestion window.

On wireless links, packet loss can also occur due to random channel errors, handoffs or route changes.

Not due to congestion.Reducing window may be too conservative.Leads to poor throughput.

How to distinguish loss due to congestion from loss due to other wireless/mobility reasons?


TCP on Mutihop Wireless [Gerla99,Holland99]

First sign of problem: TCP throughput drops with increase in #hops. Reasons:

Each hop adds additional self-contention.Also, more delay, more variability in delay, and more chance of packet loss.


Impact of MAC Unfairness [Knightly04]

320.5 320.5 320.5

2 20

1247

3 27 40.7

1000

38.5 48

11771058.7

988

0

400

800

1200

1600

TA(1) TA(2) TA(3) TA(4) Total

Goo

dput

[kb/

sec]

Obj. Basic RTS/CTS

ACK Traffic

Ethe

rnet

Ethe

rnet

Ethe

rnet

Ethe

rnet

TAP1 TAP2 TAP3 TAP4

Notations: Obj = objective throughput

for fair sharingBasic = no RTS/CTS

Two longer flows starve Similar effect regardless whether RTS/CTS are used


Cross-layer Control


Multi-hop Wireline Network

Network Utility Maximization

― Link capacity is given and constant― Rate allocation problem

( )

( )

max

s.t. ,

0

sr s

s ls S l

U r

r c l

r∈

≤ ∀

≥

∑

∑

1r

2r 3r

1c 2c


Functional DecompositionLagrangian function

Dual problem

Dual decomposition― Flow control at source

― Congestion price at link

TCP is an approximation of this dual decomposition

( ) ( )( )

, = s l s ls l s S l

L r U r r c∈

⎛ ⎞λ − λ −⎜ ⎟

⎝ ⎠∑ ∑ ∑

( )min max ,rL r

λλ

( ) 1

( ) ( )max s s l s lr s l L s l L s

U r r r U −

∈ ∈

⎛ ⎞ ⎛ ⎞′− λ ⇒ = λ⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠∑ ∑ ∑

( ) ( )min ll s l s l

l s S l s S lr c r c

λ∈ ∈

⎛ ⎞λ − ⇒ λ = −⎜ ⎟⎝ ⎠

∑ ∑ ∑g


Multi-hop Wireless Network

Long-term Network Utility Maximization

― Link capacity is time-varying and a function of resource control― Joint rate, power allocation and link scheduling

( )

( )

, , max

s.t. ,

sR P I sU R

R F P I∈

∑

1R

2R 3R

( )1 , ,C P I h ( )2 , ,C P I h : power allocation: link scheduling: channel state

PIh


Functional DecompositionFor a realization of channelsLagrangian function

Dual problem

Dual decomposition― Flow control at source

― Scheduling/power control at link

― Congestion price at link

Joint MAC and transport problemDistributed scheduling/power control is a challenge

( ) ( ) ( )( )

, , , = , ,s l s ls l s S l

L r P I U r r C P I h∈

⎛ ⎞λ − λ −⎜ ⎟

⎝ ⎠∑ ∑ ∑

( ), ,

min max , , ,r P I

L r P Iλ

λ

( ) 1

( ) ( )max s s l s lr s l L s l L s

U r r r U −

∈ ∈

⎛ ⎞ ⎛ ⎞′− λ ⇒ = λ⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠∑ ∑ ∑

( ) ( )( ) ( )

min , , , ,ll s l s ll s S l s S l

r C P I h r C P I hλ

∈ ∈

⎛ ⎞λ − ⇒ λ = −⎜ ⎟⎝ ⎠

∑ ∑ ∑g

( ),

max , ,l lP I lC P I hλ∑


Per-link Queueing Case

User 0

User 2

)( 11 xU

)( 00 xU

)( 22 xU

cA=1 cB=1

subject to μa is the fraction oftime link A is used


Lagrange Multipliers


Functional DecompositionCongestion control (sources and nodes)

MAC or scheduling (network)


Per-flow Queueing Case

User 0

User 1

User 2

)( 11 xU

)( 00 xU)( 22 xU

cA=1 cB=1

subject to

μa0 is the fraction oftime link A is used foruser 0


Functional DecompositionCongestion control (sources)

MAC or scheduling (network)

x0 μa0 μb0

x1 μa1 x2 μb2

pa0 pb0

pa1 pb2


Notations

( )ab tμ

{0,1,2, }t∈ L

a

b

Assume slotted time N nodes, L linksLink (a, b) is distinct from link (b, a)

is the matrix of transition rates offered over each link (a, b) during slot t (in units of bits/slot)

( ) [ ( )]abt tμΜ =


Link Transmission Rate FunctionThe link transmission rates are determined by a link transmission rate function

The topology state process represents all uncontrollable properties of the network that influence the set of feasible transmission ratesFor example, time-varying channel conditions due to- user mobility- fading- weather- other external environmental factors

( , )I SC( ) ( ( ), ( ) )

where s(t) = network topology state during slot tI(t) = link control action taken by the network according to a policy during slot t

t I t s tΜ = C

( )s t ∈S

( )s t


Link Transmission Rate FunctionThe link control input represents all of the possible resource allocation options available in the link/MAC layer under a given topology state

Examples- might specify the particular set of links chosen for activation

during slot t- might represent the matrix of power values allocated for

transmission over each data link- might represent the channel (or subcarrier) allocation

decisions for every data link

Note that

( )I t

( )s t( )( ) s tI t I∈

( )I t

( )I t

( )I t

( ) ( ( ), ( ) )ab abt C I t s tμ =


Assume topology state is constant, i.e., stationary nodes and time-invariant channels etc.

The objective is to schedule interference-free link activation

Let be the set contains all sets of links that can be simultaneously activated without inter-link interference, called feasible link activation sets or independent setsThen, if

Note the difficulty in distributed implementation

Example: Static Multihop Wireless Network( )s t

( )I t

I

( ) ,I t I∈if ( ) 1

( ( ), )0 if ( ) 0ab ab

abab

C I tC I t s

I t=⎧

= ⎨ =⎩


Interference Model

2r

1 2 3 4

5

node

link

5 2

1 3

4

1r

Network connectivity graph G

Conflict graph CG

- Links in G = nodes in CG- CG-Edge if links in G interfere with each other


Maximal clique model

- Clique = complete subgraph of CG- Maximal clique = clique not a subset of any other- Only one node in a clique can be active at a time- Network utility maximization (NUM) problem

Interference Model

2

1max ( )i

iU r

=∑

5 2

1 3

4

2

3

1 1 2 1 2 2

1 2 3 5

1 2 1 2 1 2

2 3 4

subject to clique constrainsts

1

1

r r r r r rc c c cr r r r r rc c c

+ ++ + + ≤

+ + ++ + ≤


Interference ModelLimitations of maximal clique model- Link activation schedule to achieve clique capacity is not

always feasible- Not easy to find a clique in a distributed manner

- The maximal throughput in the Pentagon CG is 40% per node. But, the clique constraints allow 50% per node.

1 2

5

4

3 1 2 2 3 3 4 4 5 5 1

Pentagon CG

Maximal cliques


Interference ModelMaximal independent set model

- Only one maximal independent set can be active at a time-- NUM problem

{1, 4}, {2}, {3}, {4, 5}

5 2

1 3

4

CGMaximal independent sets

( )I t I∈ { }where {1, 4}, {2}, {3}, {4, 5}I =

2

1max ( )i

iU r

=∑

1 1

1 2 2

1 2 3 41 2 3

41 2 4

52

4

1

subject to independent set constraints00 000 000 0

0 00 0 0

1ii

r cr r c

a a a ar r ccr r ccr

a=

⎡ ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥+ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥≤ + + ++⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥+ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦ ⎣ ⎦⎣ ⎦ ⎣ ⎦

=∑


Example: Ad-hoc Network with Interference

The transmission rate function for link l

where p(t) represents the power allocation vector

SINR interference model

where

= background noise intensity at each receiver= attenuation factor from the transmitter of link k to

the receiver of link l

( ( ), ( ) ) ( ( ), ( ) )l lC I t s t C p t s t=

( ( ), ( ) ) log(1 ( ( ), ( )) )l lC p t s t SINR p t s t= +

0

( ) ( ( ))( ( ), ( ))( ) ( ( ))

l lll

k klk Lk l

p t s tSINR p t s tN p t s t

αα

∈≠

=+∑

0N( ( ))kl s tα



NUM at particular state s [Chiang05]

is a nonconcave function of pAssuming high SINR regime, i.e,

can be converted into a concave function of pthrough a log transformation (geometric programming)

Joint optimization of congestion control and power control

,

( )

max ( )

subject to( , ) ,

where

ip r i

i li I l

U r

r C p s l∈

≤ ∀

∑

∑

( , )lC p slog(1 ( , )) log( ( , ))l lSINR p s SINR p s+ ≈

( , )lC p s

0

( )( , ) log(1 ( , )) log 1( )

l lll l

k klk Lk l

p sC p s SINR p sN p s

αα

∈≠

⎛ ⎞⎜ ⎟⎜ ⎟= + = +⎜ ⎟+⎜ ⎟⎝ ⎠

∑



Lagrangian function

Dual problem

Dual decomposition

( ),

min max , ,r pL r p

λλ

( )( )

( , , ) ( ) ,i i l i li l i I l

L r p U r r C p s∈

⎛ ⎞λ = − λ −⎜ ⎟

⎝ ⎠∑ ∑ ∑

( )max ( )i i i lr i i l L i

U r r∈

− λ∑ ∑ ∑ Transport layer problem

Physical layer problem( )max ,l lp lC p sλ∑



AlgorithmFlow control at source

Power control at link

Congestion price at link

1

( )i l

l L ir U

+

−

∈

⎡ ⎤⎛ ⎞′= λ⎢ ⎥⎜ ⎟

⎢ ⎥⎝ ⎠⎣ ⎦∑

( )( ) ( ),( )

( ) ( )k k

kk kk

t SINR p t sm t

p t sλ ⋅

=α

( )( 1) ( ) ( ) ( )( )l

l l kl kk Llk l

tp t p t s m tp t ∈

≠

⎛ ⎞λ⎜ ⎟+ = + γ − α⎜ ⎟⎜ ⎟

⎝ ⎠∑

( )( )

( 1) ( ) ( ) ( ),l l i li I l

t t r t C p t s+

∈

⎡ ⎤⎛ ⎞λ + = λ + γ −⎢ ⎥⎜ ⎟

⎢ ⎥⎝ ⎠⎣ ⎦∑



InterpretationBalancing transport and physical layers

Physical layer

λr C

Transport layer

Source NodeFlow Control

Transmit LinkPower Controlλλ

Intermediate Congst. Price

r


Long-term Capacity RegionTime-varying achievable rate region

Long-term rate region

: long-term rate region(can be shown to be convex)F

1R

2R

F

1r

2r

1r

2r

1r

2r

t

: long-term rate of user iR i


Long-term NUM

Utility function [Mo00]

Network Utility Maximization (NUM)

max ( )

where = long-term rate of user i= long-term capacity region

iR F i

i

U R

RF

∈∑

( )11 , if 0 & 1

1log , if 1

ii i

i

RU RR

−α⎧⎪ α < α ≠= ⎨ −α⎪ α ≠⎩

α→0: throughput maximizationα=1: proportional fairness (PF)α→∞: max-min fairness


Maximization of sum of weighted rates

Both problems yield an unique and identical solution if we set , where is the optimal solution of the long-

term NUM problem.

Sum of Weighted Rates (SWR)

max i iR F iRω

∈∑

*'( )i iU Rω = *iR

2R

1R

F

*2R

*1R

1 2( ) ( )U R U R J+ =

01 1 2 2R R Kω ω+ =


Gradient-based Scheduling

Assuming stationarity and ergodicity, we have

The long-term NUM problem can be solved if we solve with at each state sThe resource allocation problem during slot t

where Ri(t) is the average rate of user i up to time t and is the replacement of Ri

* which is unknown a priori

Convergence of Ri(t) to Ri* can be proved by

stochastic approximation theory [Kush04] or fluid limit technique [Stol05].

( )max maxi i s i iR F r F si i

R E rω ω∈ ∈

⎡ ⎤= ⎢ ⎥

⎣ ⎦∑ ∑ where rate of user at state

( ) capacity region at state ir i s

F s s==

( )max i ir F s i

rω∈

∑ *'( )i iU Rω =

( )( )

max '( ( )) ( )

subject to ( ) ( )

i ir t iU R t r t

r t F s t∈

∑


Transport and Network Layer Queueing1 2

3

45

transportlayer

networklayer

(3)1A

(4)1A

(3)5A

(1)4A (1)

4R (1)4U

= set of commodities in the network= the amount of new commodity c data that

exogenously arrives to node i during slot t= the amount of commodity c data allowed to enter the

network layer from the transport layer at node i during slot t= the backlog of commodity c data stored in the

network layer queue at node i during slot t

K( ) ( )ciA t

( ) ( )ciR t

( ) ( )ciU t

( and )i N c K∈ ∈


Dynamic Control for Network Stability

The stabilizing dynamic backpressure algorithm [Tassiulas92]

- An algorithm for resource allocation and routing whichstabilizes the network whenever the vector of arrival rateslies within the capacity region of the network

Resource allocation- For each link , determine optimal commodity and optimal weight by

- Find optimal resource allocation action by solving

)(* tCab)(* tabω

]0,[max)(

)]()([maxarg)(

))(())((*

)()(

}),|({

*

** tCb

tCaab

cb

ca

Lbacab

abab

c

UUt

tUtUtC

−=

−=∈

ω

)(

*

)(

)(.

))(),(()(max

ts

abababtI

ItIts

tstICt

∈

⋅∑ω

),( ba

)(* tI


Dynamic Control for Network Stability

Routing- For each link such that , offer a transmission rate of to data of commodity .

The algorithm requires in general knowledge of thewhole network state. However, there are importantspecial cases where the algorithm can run in adistributed fashion with each node requiringknowledge only of the local state information oneach of its outgoing links.

Interpretation- The resulting algorithm assigns larger transmission rates to links with larger differential backlog, and zero transmissionrates to links with negative differential backlog.

),( ba 0)(* >tabω))(,)(()( ** tstICt abab =μ )(* tCab


Dynamic Control for Infinite Demands

AssumptionInfinite backlog at every transport layer queue

Cross-layer control algorithm [Neely05]Flow control at node i

Each time t, set Ri(c)(t) to

( )( ) ( ) ( )arg max ( )i

c c ci i nr c

VU r r U t⎡ ⎤−⎣ ⎦∑ 0 : control parameterV >

( )( ) 1( ) max ,0

cc ii

UR t UV

−⎡ ⎤⎛ ⎞′= ⎢ ⎥⎜ ⎟⎝ ⎠⎣ ⎦


Dynamic Control for Infinite Demands

Routing and resource allocationSame as previous

Algorithm performance

Tradeoff between utility and delayControl parameter V determines the tradeoff

( )1

( )

0

1limsup ( )t

ci

t iU O V

t

−

→∞ τ=

τ ≤∑∑

{ } ( )1

( ) *

, 0 ,

1 1liminf ( )t

ci it i c i c

U E R U R Ot V

−

→∞τ=

⎛ ⎞ ⎛ ⎞τ ≥ − ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠∑ ∑ ∑

Stability

Optimality


References[Kum03] K. Kumaran and L. Qian, “Uplink Scheduling in CDMA Packet-Data Systems,” IEEE INFOCOM 2003.[Mo00] J. Mo and J. Walrand, “Fair End-to-End Window-Based Congestion Control,” IEEE/ACM Trans. Networking, Vol. 8, No. 5, pp. 556-567, Oct. 2000.[Kush04] H. J. Kushner and P. A. Whiting, “Convergence of Proportional-Fair Sharing Algorithms Under General Conditions,” IEEE Trans. Wireless Comm., vol. , no., 2004.[Stol05] A. L. Stolyar, “On the Asymptotic Optimality of the Gradient Scheduling Algorithm for Multiuser Throughput Allocation,” Operations Research, vol. 53, no. 1, pp. 12-25, Jan. 2005.[Neely05] M. J. Neely et al., “Fairness and Optimal Stochastic Control for Heterogeneous Networks,” IEEE INFOCOM 2005.[Jun03] J. Jun, ML Sichitiu, The nominal capacity of wireless mesh networks, IEEE Wireless Communications 10 (5), 2003.[Morris03] D. Decouto, D. Aguayo, J. Bicket, and R. Morris, "A high-throughput path metric for multi-hop wireless networks", in Proceedings of MobiCom, 2003.[Morris04] Daniel Aguayo, John Bicket, Sanjit Biswas, Glenn Judd, Robert Morris, Link-level Measurements from an 802.11b Mesh Network, SIGCOMM 2004, Aug 2004.[Daves04] R. Draves, J. Padhye, and B. Zill. The architecture of the Link Quality Source Routing Protocol. Technical Report MSR-TR-2004-57, Microsoft Research, 2004.


References

[Daves04-2] R. Draves, J. Padhye, and B. Zill. Routing in Multi-radio, Multi-hop Wireless Mesh Networks. ACM MobiCom, Philadelphia, PA, September 2004.[Morris05] Sanjit Biswas and Robert Morris, Opportunistic Routing in Multi-Hop Wireless Networks, SIGCOMM 2005, August 2005.[Chiang05] M. Chiang, “Balancing Transport and Physical Layers in Wireless Multihop Networks: Jointly Optimal Congestion Control and Power Control,”IEEE J. Sel. Areas Comm., vol. 23, no. 1, pp. 104-116, Jan. 2005.[Tassiulas92] L. Tassiulas and A. Ephremides, “Stability Properties of Constrained Queueing Systems and Scheduling Policies for Maximum Throughput in Multihop Radio Networks,” IEEE Trans. Automatic Control, vol. 37, no. 12, Dec. 1992.[Gerla99] M. Gerla, K. Tang, and R. Bagrodia, "TCP Performance in Wireless Multihop Networks," IEEE WMCSA 1999.[Holland99] G. Holland and N. Vaidya. Analysis of TCP performance over mobile ad hoc networks. In Proc. of MobiCom, 1999.[Knightly04] V. Gambiroza, B. Sadeghi and E. Knightly, “End-to-End Performance and. Fairness in Multihop Wireless Backhaul Networks”, in Proceedings of ACM. MobiCom’04, Philadelphia, PA, September 2004.

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Wireless Mesh Network - KNOM

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