for Constructing a Reliable MCDS in Probabilistic Wireless Networks - PowerPoint PPT Presentation

WASA 2011 A Genetic Algorithm for Constructing a Reliable MCDS in Probabilistic Wireless Networks Jing He, Zhipeng Cai, Shouling Ji, and Yi Pan Department of Computer Science Georgia State University, Atlanta, GA, 30303 1

O UTLINE  Motivation  Problem Definition  Genetic Algorithm  Overview  Population Initialization  Fitness function  Genetic operations  Simulation Results  Conclusions 2

Motivation • Probabilistic Network Model • Reliable MCDS • Chanllenges 3

Motivation T RANSITIONAL R EGION P HENOMENON 0-2.6M 2.6-6M >6M 7 > 97% 8 > 95% 4 6 < 5% Total: 8 27 15

Motivation R ELIABLE MCDS IN P ROBABILISTIC WSN S 5

Motivation C HALLENGES How to measure the transmission quality of CDS under Probabilistic Network Model (PNM)?  CDS reliability : the minimum upper limit of the node- to-node delivery ratio between any pair of dominators in a CDS How to find a minimum-sized CDS?  NP-Hard How to find a proper trade-off between the minimum- sized CDS and the CDS reliability while satisfying the user predefined constraint? 6

Problem Definition R ELIABLE MCDS (RMCDS) P ROBLEM For a WSN represented by graph G = (V, E, P(E)={<e,TSR(e)> |    e }) under the Probabilistic Network Model E , 0 TSR ( e ) 1   (PNM), and a pre-defined threshold , the RMCDS ( 0 , 1 ] D  problem is to find a minimum-sized node set , such that: V 1) The induced graph G[D] = (D,E’) , '      where , is connected. E { e | e ( u , v ), u D , v D , ( u , v ) E } u    v   u  D ( u , v ) E D 2) and , , such that . V 3) CDS Reliability (minimum upper limit of the node-to-node   delivery ratio between any pair of dominators in a CDS) . 7 7

Genetic Algorithm • Overview • Population Initialization • Fitness function • Crossover Operations 8

Genetic Algorithm RMCDS-GA 9 9

Genetic Algorithm E NCODE SCHEME AND P OPULATION I NITIALIZATION 10 10

Genetic Algorithm F ITNESS FUNCTION  Given a solution, its quality should be accurately evaluated by the fitness value. 2 R  D f ( C ) i 2 | D | where R is the of CDS reliablity D | D | is the size of CDS D 11

Genetic Algorithm G ENETIC OPERATIONS 12 12

Simulation Results S IMULATION 13 13

Conclusions C ONCLUSIONS  We identify and highlight the use of lossy links.  In order to measure the quality of a CDS under the PNM model, we define a new metric CDS Reliability.  We propose a GA to build a Reliable MCDS under the PNM model.  We also conduct simulations to validate our proposed algorithm. 14 14

Q & A 15