Self-Configuration and Optimization for Cognitive Networked Devices

Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Balamuralidhar, P. [verfasserIn] Ramjee, Prasad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Cognitive networks Cognitive networked device Signed graph Dependency graph Influence graph

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 59(2011), 3 vom: 18. Feb., Seite 471-486
Übergeordnetes Werk:	volume:59 ; year:2011 ; number:3 ; day:18 ; month:02 ; pages:471-486

Links:	Volltext

DOI / URN:	10.1007/s11277-011-0240-8

Katalog-ID:	SPR018540139

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520			\|a Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example.
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allfields	10.1007/s11277-011-0240-8 doi (DE-627)SPR018540139 (SPR)s11277-011-0240-8-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Balamuralidhar, P. verfasserin aut Self-Configuration and Optimization for Cognitive Networked Devices 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example. Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213 Ramjee, Prasad verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 59(2011), 3 vom: 18. Feb., Seite 471-486 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:59 year:2011 number:3 day:18 month:02 pages:471-486 https://dx.doi.org/10.1007/s11277-011-0240-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 59 2011 3 18 02 471-486
spelling	10.1007/s11277-011-0240-8 doi (DE-627)SPR018540139 (SPR)s11277-011-0240-8-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Balamuralidhar, P. verfasserin aut Self-Configuration and Optimization for Cognitive Networked Devices 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example. Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213 Ramjee, Prasad verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 59(2011), 3 vom: 18. Feb., Seite 471-486 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:59 year:2011 number:3 day:18 month:02 pages:471-486 https://dx.doi.org/10.1007/s11277-011-0240-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 59 2011 3 18 02 471-486
allfields_unstemmed	10.1007/s11277-011-0240-8 doi (DE-627)SPR018540139 (SPR)s11277-011-0240-8-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Balamuralidhar, P. verfasserin aut Self-Configuration and Optimization for Cognitive Networked Devices 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example. Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213 Ramjee, Prasad verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 59(2011), 3 vom: 18. Feb., Seite 471-486 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:59 year:2011 number:3 day:18 month:02 pages:471-486 https://dx.doi.org/10.1007/s11277-011-0240-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 59 2011 3 18 02 471-486
allfieldsGer	10.1007/s11277-011-0240-8 doi (DE-627)SPR018540139 (SPR)s11277-011-0240-8-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Balamuralidhar, P. verfasserin aut Self-Configuration and Optimization for Cognitive Networked Devices 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example. Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213 Ramjee, Prasad verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 59(2011), 3 vom: 18. Feb., Seite 471-486 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:59 year:2011 number:3 day:18 month:02 pages:471-486 https://dx.doi.org/10.1007/s11277-011-0240-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 59 2011 3 18 02 471-486
allfieldsSound	10.1007/s11277-011-0240-8 doi (DE-627)SPR018540139 (SPR)s11277-011-0240-8-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Balamuralidhar, P. verfasserin aut Self-Configuration and Optimization for Cognitive Networked Devices 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example. Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213 Ramjee, Prasad verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 59(2011), 3 vom: 18. Feb., Seite 471-486 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:59 year:2011 number:3 day:18 month:02 pages:471-486 https://dx.doi.org/10.1007/s11277-011-0240-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 59 2011 3 18 02 471-486
language	English
source	Enthalten in Wireless personal communications 59(2011), 3 vom: 18. Feb., Seite 471-486 volume:59 year:2011 number:3 day:18 month:02 pages:471-486
sourceStr	Enthalten in Wireless personal communications 59(2011), 3 vom: 18. Feb., Seite 471-486 volume:59 year:2011 number:3 day:18 month:02 pages:471-486
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cognitive networks Cognitive networked device Signed graph Dependency graph Influence graph
dewey-raw	620
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Balamuralidhar, P. @@aut@@ Ramjee, Prasad @@aut@@
publishDateDaySort_date	2011-02-18T00:00:00Z
hierarchy_top_id	271179120
dewey-sort	3620
id	SPR018540139
language_de	englisch
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author	Balamuralidhar, P.
spellingShingle	Balamuralidhar, P. ddc 620 bkl 53.00 misc Cognitive networks misc Cognitive networked device misc Signed graph misc Dependency graph misc Influence graph Self-Configuration and Optimization for Cognitive Networked Devices
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topic_title	620 ASE 53.00 bkl Self-Configuration and Optimization for Cognitive Networked Devices Cognitive networks (dpeaa)DE-He213 Cognitive networked device (dpeaa)DE-He213 Signed graph (dpeaa)DE-He213 Dependency graph (dpeaa)DE-He213 Influence graph (dpeaa)DE-He213
topic	ddc 620 bkl 53.00 misc Cognitive networks misc Cognitive networked device misc Signed graph misc Dependency graph misc Influence graph
topic_unstemmed	ddc 620 bkl 53.00 misc Cognitive networks misc Cognitive networked device misc Signed graph misc Dependency graph misc Influence graph
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title	Self-Configuration and Optimization for Cognitive Networked Devices
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title_full	Self-Configuration and Optimization for Cognitive Networked Devices
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title_sort	self-configuration and optimization for cognitive networked devices
title_auth	Self-Configuration and Optimization for Cognitive Networked Devices
abstract	Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example.
abstractGer	Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example.
abstract_unstemmed	Abstract Connected devices are an integral part of the ubiquitous and pervasive systems. They are highly dynamic and can grow into complex networks with multiple connectivity options, application requirements and user contexts. Since manual configuration and optimization are tedious for such systems, autonomic management would be beneficial. This motivates the conceptualization of cognitive networked devices that follows a cognitive approach for management. One of the major architectural components considered here is a cognitive management entity supporting self configuration and self optimization with an end-to-end perspective. Here we explore an optimizing controller which follows a cognitive process that can run in real-time efficiently on resource constrained device platforms in the absence of a mathematically tractable model. A methodology is developed based on dependency graph as an ontology modeling relative influences between state variables. Concepts from monotonic influence graphs and signed graphs have been used to formulate an approach and algorithm for a qualitative optimization. Functionality of the approach has been demonstrated with an example.
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title_short	Self-Configuration and Optimization for Cognitive Networked Devices
url	https://dx.doi.org/10.1007/s11277-011-0240-8
remote_bool	true
author2	Ramjee, Prasad
author2Str	Ramjee, Prasad
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doi_str	10.1007/s11277-011-0240-8
up_date	2024-07-03T20:27:17.093Z
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