Joint Flow Routing and Relay Node Assignment

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Joint Flow Routing and Relay Node

Assignment in Cooperative Multi-

hop Networks

Sushant Sharma et al.

IEEE Journal on Selected Areas inCommunications, vol. 30, no.2, Feb. 2012

Speaker: Pham Tran Anh Quang

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Presentation History

Y.Li et al., Enhancing Real-Time Delivery in WSNs with Two-HopInformation, in IEEE TII, Vol. 5, No.2, May 2009

Shuo Guo et al., Opportunistic Flooding in Low-Duty-Cycle WirelessSensor Networks with Unreliable Links, in Mobicom 09

Sinem Coleri Ergen and Pravin Varaiya, TDMA scheduling algorithms for

wireless sensor networks, Wireless Network, Springer Z. Liang et al., Delay Performance Analysis for Supporting Real-Time

Traffic in a Cognitive Radio Sensor Network, IEEE TWC, Vol. 10, No. 1, Jan.2011

Vehbi Cargi Gungor et al., A Real-time and Reliable Transport Protocol forWSANs, IEEE ToN, Vol.16, No.2, April 2008

P.T.A. Quang and Dong-Sung Kim, Enhancing Real-time Delivery ofGradient Routing for Industrial Wireless Sensor Networks, IEEE TII, Vol. 8,No.2, May 2012

Emanuele Toscano et al., Multichannel Superframe Scheduling for IEEE802.15.4 Industrial Wireless Sensor Networks, IEEE TII (early access)

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Motivations and goals

Motivations: Achieve better throughput using cooperative scheme

Joint problem: (1)relay node assignment and (2) multi-hop flow

High computational complexity (MILP with largesolution space) long calculation time

Goals: Combine Branch and Bound (BB) and Gomory-cutting

planes(CP) speed up computation Limitations:

Complexity: exponential !!!

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Models (Overview)

s d: direct transmission

s rd: cooperative communications

(1) CC with amplify and forward (PHY layer)

(2) CC with decode and forward (MAC layer)

(3) direct transmission

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Models (Channel capacity)

(1) CC with amplify and forward

(2) CC with decode-and-forward

(3) Direct transmission

Above equations can be achieved by using Shannonstheorem and Information theory

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Models (Node)

2 types of relay nodes: Cooperative relay (CR) andMulti-hop relay (MR)

Full-duplex transmission (1 transmitter + 1 receiver)

a relay node will be CR or MRsingle in-stream and single out-stream

S != CR D != CR

S !=MR D !=MR

Ns=NdNr+Ns+Nd=N

S: source, D: dest., CR=coop. relay, MR= multi-hop relay

Ns: # souce nodes, Nd:#dest. nodes, Nr: #relay nodes

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Mathematic constraints

(1) Role of relay nodes:

w is CR on hop (u,v)

- Link u,v is active

CR MR

In-stream = out-stream

One CR can be assigned for only one-hop

1 CR 1 hop

Single in-stream MR

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(2) Flow routing

Source always transmits to another node

A node (except dest.) can receive (=1) or not (=0)

Destination always receives from another node

Destination can forward to another node

Flow balance at the intermediate node

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(3) Rate constraints

Direct transmission

Amplify and forward (AF) transmission

All streams go through hop (u,v) are either Direct

transmission or CC transmission

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Problem formulation

For a given session (si, di), e2e flow-rate:

Optimize the minimum flow rate

MILP problem:

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Algorithms: Overview

MILP can be solved by using Branch and

Bound (BB) or Gomory Cutting Plane (CP)

BB: partition relaxed problem into 2 sub-

problems. Then solving sub-problems until

satisfying integer requirement.

CP: add linear constraint to reduce feasible

solution region until satisfying integerrequirement Upper bound is improved (moce

accurate)

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Proposed algorithm: Combined BB and CP

and FSC (finding lower bound)

Proposed selection conditions to reduce

calculation time

Obtain (1-e)-optimal solution acceptable

solution belongs to [U, (1-e)U] (1-e)U > L

(with U: upper bound and L: lower bound of

optimal solution)

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Assume that:

r>e and (u2 >u1 or (1-e)u1 e continue

Else Finish

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Algorithms: FSC

Feasible solution construction (FSC) is a local

search algorithm

Upper bound (determining by relaxed MILP):

Optimal value but not meet integer conditions

Lower bound (FSC):

Based on solution ofupper bound, satisfy integer

requirement but not optimal value

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Algorithms: FSC

Finding path based on throughput (widest pipe)If(route si encounters relay nodes)

finish and move to next hop; (0)

else (route si encounters source (sj) or destination (dk) node)

{

If(encounter sj)

sjL (1)

else

{

if(sk

in L;)

L=L \{sk}; (2)

else if (dk is di)

{

if(L is empty)

finish and move on to the next remaining nodes; (3)

else

{

move on to the sm in L ;

L = L \{sm}; (4)}

}

else

{

dk must not be included in the path; (5)

}

}

}

1-0-0-2

1-0-1-0-4

1-0-0-5

Question: Find an example go to condition (3)16

Phase 1: Path determination


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Algorithms: FSC

Phase 2: CR assignment: (for free CR)

Capacity-flow-ratio (CFR): hops capacity to the

number of overlapping sessions

CR assignments start with the minimum CFR hop

and so on.

Phase 3: Flow recalculation

After phase 2, every integer variables aredeterminedMILP became LP solve theproblem to find throughput for each flowlower bound of branching process

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Simulation results

Bandwidth = 22MHz / channel

Transmission power = 1W

Path loss exponential = 4 (Multi-path models)

Noise variance 10^-10 W

e=0.1

40 nodes (Ns=Nd=8, Nr=24)

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Simulation resultsRouting map with CC Routing map without CC

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Conclusions

The most important contributes are: Combined BB and Gomory- CP to reduce computational time

Proposed FSC to find down lower bound

Weakness:- Cannot reduce computational complexity (exponentialcomplexity!!!)

- Not prove that proposed scheme can reduce computing time!!! (even that it is the most important contribution)

- Lack of simulations

- Theoretical solution not stick on any standard

- Ideal assumption (how can they handle interference???)

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Future works

Combine that solution to n-hop region routing

(n-hop region is much simpler than whole

network feasible solution for WSN)

what else??? I have not found out yet ~~

s

11

2

2

R

RR

R1

2

Its much simpler than the problem of this paper

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Thank you

for your listening

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Documents

Joint Flow Routing and Relay Node Assignment