Sensors Network Optimization by a Novel Genetic Algorithm
This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination...
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| creator | Wang, Hui Buczak, Anna L. Jin, Hong Wang, Hongan Li, Baosen |
| description | This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired objects. The system constructs a GA with the appropriate internal structure for the optimization problem at hand, and King Mutation C2 finds the quasi-optimal combination of sensors that can detect and/or locate the objects. The study is performed for the sensor network optimization problem with five objects to detect/track and the results obtained by a canonical GA and King Mutation C2 are compared. |
| doi_str_mv | 10.1007/978-3-540-30141-7_80 |
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For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired objects. The system constructs a GA with the appropriate internal structure for the optimization problem at hand, and King Mutation C2 finds the quasi-optimal combination of sensors that can detect and/or locate the objects. The study is performed for the sensor network optimization problem with five objects to detect/track and the results obtained by a canonical GA and King Mutation C2 are compared.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540233881</identifier><identifier>ISBN: 9783540233886</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540301410</identifier><identifier>EISBN: 9783540301417</identifier><identifier>DOI: 10.1007/978-3-540-30141-7_80</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Computer science; control theory; systems ; Exact sciences and technology ; Genetic Algorithm ; Network Objective ; Object Tracking ; Reproduction Process ; Sensor Network ; Telecommunications ; Telecommunications and information theory ; Teleprocessing networks. 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Simulation</subject><ispartof>Lecture notes in computer science, 2004, p.536-543</ispartof><rights>IFIP International Federation for Information Processing 2004</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/978-3-540-30141-7_80$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/978-3-540-30141-7_80$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4036,4037,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16367625$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Gao, Guang R.</contributor><contributor>Jin, Hai</contributor><contributor>Chen, Hao</contributor><contributor>Xu, Zhiwei</contributor><creatorcontrib>Wang, Hui</creatorcontrib><creatorcontrib>Buczak, Anna L.</creatorcontrib><creatorcontrib>Jin, Hong</creatorcontrib><creatorcontrib>Wang, Hongan</creatorcontrib><creatorcontrib>Li, Baosen</creatorcontrib><title>Sensors Network Optimization by a Novel Genetic Algorithm</title><title>Lecture notes in computer science</title><description>This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. For a given distribution of sensors, the goal of the system is to determine the optimal combination of sensors that can detect and/or locate the objects. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired objects. The system constructs a GA with the appropriate internal structure for the optimization problem at hand, and King Mutation C2 finds the quasi-optimal combination of sensors that can detect and/or locate the objects. The study is performed for the sensor network optimization problem with five objects to detect/track and the results obtained by a canonical GA and King Mutation C2 are compared.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Genetic Algorithm</subject><subject>Network Objective</subject><subject>Object Tracking</subject><subject>Reproduction Process</subject><subject>Sensor Network</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Teleprocessing networks. Isdn</subject><subject>Theoretical computing</subject><subject>Valuation and optimization of characteristics. Simulation</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540233881</isbn><isbn>9783540233886</isbn><isbn>3540301410</isbn><isbn>9783540301417</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2004</creationdate><recordtype>book_chapter</recordtype><recordid>eNotkE9PAyEQxfFfYq39Bh64eESBYYE9No1Wk6Y92DsBylbsdncDG0399G5b5zLJey8zLz-EHhh9YpSq51JpAqQQlABlghFlNL1AdzAoJ4FeohGTjBEAUV6dDQ6gNbtGoyHCSakE3KJJzl90GE5L0GKEyo_Q5DZlvAz9T5t2eNX1cR9_bR_bBrsDtnjZfocaz0MT-ujxtN62Kfaf-3t0U9k6h8n_HqP168t69kYWq_n7bLogHVeiJ1JRzwK1zrHArapU2Hjuw1CvqISqhGdc6WKjS6mddKEA6za6AOqFA1oVMEaP57Odzd7WVbKNj9l0Ke5tOhgmQSrJjzl-zuXBarYhGde2u2wYNUeAZgBowAxvzYmXOQKEPxNvXjk</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Wang, Hui</creator><creator>Buczak, Anna L.</creator><creator>Jin, Hong</creator><creator>Wang, Hongan</creator><creator>Li, Baosen</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Sensors Network Optimization by a Novel Genetic Algorithm</title><author>Wang, Hui ; Buczak, Anna L. ; Jin, Hong ; Wang, Hongan ; Li, Baosen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p274t-670c1e0abb1e2a7f7edc2ce5405f47f4c12785d8968b6be53abd8530c4b30f53</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Genetic Algorithm</topic><topic>Network Objective</topic><topic>Object Tracking</topic><topic>Reproduction Process</topic><topic>Sensor Network</topic><topic>Telecommunications</topic><topic>Telecommunications and information theory</topic><topic>Teleprocessing networks. Isdn</topic><topic>Theoretical computing</topic><topic>Valuation and optimization of characteristics. Simulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Hui</creatorcontrib><creatorcontrib>Buczak, Anna L.</creatorcontrib><creatorcontrib>Jin, Hong</creatorcontrib><creatorcontrib>Wang, Hongan</creatorcontrib><creatorcontrib>Li, Baosen</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Hui</au><au>Buczak, Anna L.</au><au>Jin, Hong</au><au>Wang, Hongan</au><au>Li, Baosen</au><au>Gao, Guang R.</au><au>Jin, Hai</au><au>Chen, Hao</au><au>Xu, Zhiwei</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Sensors Network Optimization by a Novel Genetic Algorithm</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2004</date><risdate>2004</risdate><spage>536</spage><epage>543</epage><pages>536-543</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540233881</isbn><isbn>9783540233886</isbn><eisbn>3540301410</eisbn><eisbn>9783540301417</eisbn><abstract>This paper describes the optimization of a sensor network by a novel Genetic Algorithm (GA) that we call King Mutation C2. 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| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2004, p.536-543 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16367625 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems Exact sciences and technology Genetic Algorithm Network Objective Object Tracking Reproduction Process Sensor Network Telecommunications Telecommunications and information theory Teleprocessing networks. Isdn Theoretical computing Valuation and optimization of characteristics. Simulation |
| title | Sensors Network Optimization by a Novel Genetic Algorithm |
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