joint flow routing and relay node assignment
TRANSCRIPT
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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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Mathematic constraints
(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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Mathematic constraints
(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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Algorithms: Overview
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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Algorithms: Overview
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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