Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs

This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Urrea, E, Şahin, Cem Şafak, Uyar, M Ümit, Conner, M, Bertoli, G, Pizzo, C
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1410
container_issue
container_start_page 1405
container_title
container_volume
creator Urrea, E
Şahin, Cem Şafak
Uyar, M Ümit
Conner, M
Bertoli, G
Pizzo, C
description This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.
doi_str_mv 10.1109/MILCOM.2010.5680143
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5680143</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5680143</ieee_id><sourcerecordid>5680143</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-d84805cffa0d52d3232cfdbb6171eb0ce8c82f07d4c947c04049d29051f9ad4f3</originalsourceid><addsrcrecordid>eNo9UMtuwjAQdF9SKc0XcPEPhO46dmwfEaIUiZQLnJHjB02VxiiOKvH3TVXU065mdkYzS8gMYY4I-qXabJe7as5gBESpAHlxQ56QM84VKhC3ZMJQiFwKVd6RTEt15cbl_p-T6pFkKX0CADJVMo0TclilofkyQ9OdaO0_zHcTexoDNXS9yGuTvKNDPMc2ni7Uxm7oY0vDeJJ8G_J07r1xv9IuOp9o09Fq8b7ap2fyEEybfHadU3J4Xe2Xb_l2t94sF9u8QSmG3Ck-hrchGHCCuYIVzAZX1yVK9DVYr6xiAaTjVnNpgQPXjmkQGLRxPBRTMvvzbbz3x3M_Nukvx-uHih8ivFXl</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Urrea, E ; Şahin, Cem Şafak ; Uyar, M Ümit ; Conner, M ; Bertoli, G ; Pizzo, C</creator><creatorcontrib>Urrea, E ; Şahin, Cem Şafak ; Uyar, M Ümit ; Conner, M ; Bertoli, G ; Pizzo, C</creatorcontrib><description>This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.</description><identifier>ISSN: 2155-7578</identifier><identifier>ISBN: 9781424481781</identifier><identifier>ISBN: 1424481783</identifier><identifier>EISSN: 2155-7586</identifier><identifier>EISBN: 1424481805</identifier><identifier>EISBN: 9781424481798</identifier><identifier>EISBN: 1424481791</identifier><identifier>EISBN: 9781424481804</identifier><identifier>DOI: 10.1109/MILCOM.2010.5680143</identifier><language>eng</language><publisher>IEEE</publisher><subject>Ad hoc networks ; Artificial neural networks ; Biological cells ; Force ; Gallium ; Genetic Algorithms ; MANET ; Mobile communication ; Nickel ; Self-organization</subject><ispartof>2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, 2010, p.1405-1410</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5680143$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,778,782,787,788,2054,27912,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5680143$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Urrea, E</creatorcontrib><creatorcontrib>Şahin, Cem Şafak</creatorcontrib><creatorcontrib>Uyar, M Ümit</creatorcontrib><creatorcontrib>Conner, M</creatorcontrib><creatorcontrib>Bertoli, G</creatorcontrib><creatorcontrib>Pizzo, C</creatorcontrib><title>Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs</title><title>2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE</title><addtitle>MILCOM</addtitle><description>This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.</description><subject>Ad hoc networks</subject><subject>Artificial neural networks</subject><subject>Biological cells</subject><subject>Force</subject><subject>Gallium</subject><subject>Genetic Algorithms</subject><subject>MANET</subject><subject>Mobile communication</subject><subject>Nickel</subject><subject>Self-organization</subject><issn>2155-7578</issn><issn>2155-7586</issn><isbn>9781424481781</isbn><isbn>1424481783</isbn><isbn>1424481805</isbn><isbn>9781424481798</isbn><isbn>1424481791</isbn><isbn>9781424481804</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9UMtuwjAQdF9SKc0XcPEPhO46dmwfEaIUiZQLnJHjB02VxiiOKvH3TVXU065mdkYzS8gMYY4I-qXabJe7as5gBESpAHlxQ56QM84VKhC3ZMJQiFwKVd6RTEt15cbl_p-T6pFkKX0CADJVMo0TclilofkyQ9OdaO0_zHcTexoDNXS9yGuTvKNDPMc2ni7Uxm7oY0vDeJJ8G_J07r1xv9IuOp9o09Fq8b7ap2fyEEybfHadU3J4Xe2Xb_l2t94sF9u8QSmG3Ck-hrchGHCCuYIVzAZX1yVK9DVYr6xiAaTjVnNpgQPXjmkQGLRxPBRTMvvzbbz3x3M_Nukvx-uHih8ivFXl</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Urrea, E</creator><creator>Şahin, Cem Şafak</creator><creator>Uyar, M Ümit</creator><creator>Conner, M</creator><creator>Bertoli, G</creator><creator>Pizzo, C</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201010</creationdate><title>Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs</title><author>Urrea, E ; Şahin, Cem Şafak ; Uyar, M Ümit ; Conner, M ; Bertoli, G ; Pizzo, C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d84805cffa0d52d3232cfdbb6171eb0ce8c82f07d4c947c04049d29051f9ad4f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Ad hoc networks</topic><topic>Artificial neural networks</topic><topic>Biological cells</topic><topic>Force</topic><topic>Gallium</topic><topic>Genetic Algorithms</topic><topic>MANET</topic><topic>Mobile communication</topic><topic>Nickel</topic><topic>Self-organization</topic><toplevel>online_resources</toplevel><creatorcontrib>Urrea, E</creatorcontrib><creatorcontrib>Şahin, Cem Şafak</creatorcontrib><creatorcontrib>Uyar, M Ümit</creatorcontrib><creatorcontrib>Conner, M</creatorcontrib><creatorcontrib>Bertoli, G</creatorcontrib><creatorcontrib>Pizzo, C</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Urrea, E</au><au>Şahin, Cem Şafak</au><au>Uyar, M Ümit</au><au>Conner, M</au><au>Bertoli, G</au><au>Pizzo, C</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs</atitle><btitle>2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE</btitle><stitle>MILCOM</stitle><date>2010-10</date><risdate>2010</risdate><spage>1405</spage><epage>1410</epage><pages>1405-1410</pages><issn>2155-7578</issn><eissn>2155-7586</eissn><isbn>9781424481781</isbn><isbn>1424481783</isbn><eisbn>1424481805</eisbn><eisbn>9781424481798</eisbn><eisbn>1424481791</eisbn><eisbn>9781424481804</eisbn><abstract>This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.</abstract><pub>IEEE</pub><doi>10.1109/MILCOM.2010.5680143</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2155-7578
ispartof 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, 2010, p.1405-1410
issn 2155-7578
2155-7586
language eng
recordid cdi_ieee_primary_5680143
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Ad hoc networks
Artificial neural networks
Biological cells
Force
Gallium
Genetic Algorithms
MANET
Mobile communication
Nickel
Self-organization
title Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T04%3A34%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Estimating%20behavior%20of%20a%20GA-based%20topology%20control%20for%20self-spreading%20nodes%20in%20MANETs&rft.btitle=2010%20-%20MILCOM%202010%20MILITARY%20COMMUNICATIONS%20CONFERENCE&rft.au=Urrea,%20E&rft.date=2010-10&rft.spage=1405&rft.epage=1410&rft.pages=1405-1410&rft.issn=2155-7578&rft.eissn=2155-7586&rft.isbn=9781424481781&rft.isbn_list=1424481783&rft_id=info:doi/10.1109/MILCOM.2010.5680143&rft_dat=%3Cieee_6IE%3E5680143%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424481805&rft.eisbn_list=9781424481798&rft.eisbn_list=1424481791&rft.eisbn_list=9781424481804&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5680143&rfr_iscdi=true