Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks

Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are consider...
Ausführliche Beschreibung

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Autor*in:	Rossi, Michele [verfasserIn] Zorzi, Michele [verfasserIn] Rao, Ramesh R. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	Wireless sensor networks Optimal routing Greedy forwarding policies Online optimization techniques Protocol design System analysis

Übergeordnetes Werk:	Enthalten in: Wireless networks - [S.l.] : Proquest, 1995, 14(2006), 1 vom: 09. Juni, Seite 55-70
Übergeordnetes Werk:	volume:14 ; year:2006 ; number:1 ; day:09 ; month:06 ; pages:55-70

Links:	Volltext

DOI / URN:	10.1007/s11276-006-7791-8

Katalog-ID:	SPR018507492

Internformat


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520			\|a Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs.
650		4	\|a Wireless sensor networks \|7 (dpeaa)DE-He213
650		4	\|a Optimal routing \|7 (dpeaa)DE-He213
650		4	\|a Greedy forwarding policies \|7 (dpeaa)DE-He213
650		4	\|a Online optimization techniques \|7 (dpeaa)DE-He213
650		4	\|a Protocol design \|7 (dpeaa)DE-He213
650		4	\|a System analysis \|7 (dpeaa)DE-He213
700	1		\|a Zorzi, Michele \|e verfasserin \|4 aut
700	1		\|a Rao, Ramesh R. \|e verfasserin \|4 aut
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matchkey_str	article:15728196:2006----::ttsialasseruigloihsaaohponbsdowrig
hierarchy_sort_str	2006
bklnumber	53.74 54.32
publishDate	2006
allfields	10.1007/s11276-006-7791-8 doi (DE-627)SPR018507492 (SPR)s11276-006-7791-8-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Rossi, Michele verfasserin aut Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs. Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213 Zorzi, Michele verfasserin aut Rao, Ramesh R. verfasserin aut Enthalten in Wireless networks [S.l.] : Proquest, 1995 14(2006), 1 vom: 09. Juni, Seite 55-70 (DE-627)319427366 (DE-600)2006505-X 1572-8196 nnns volume:14 year:2006 number:1 day:09 month:06 pages:55-70 https://dx.doi.org/10.1007/s11276-006-7791-8 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_101 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_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_2919 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 53.74 ASE 54.32 ASE AR 14 2006 1 09 06 55-70
spelling	10.1007/s11276-006-7791-8 doi (DE-627)SPR018507492 (SPR)s11276-006-7791-8-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Rossi, Michele verfasserin aut Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs. Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213 Zorzi, Michele verfasserin aut Rao, Ramesh R. verfasserin aut Enthalten in Wireless networks [S.l.] : Proquest, 1995 14(2006), 1 vom: 09. Juni, Seite 55-70 (DE-627)319427366 (DE-600)2006505-X 1572-8196 nnns volume:14 year:2006 number:1 day:09 month:06 pages:55-70 https://dx.doi.org/10.1007/s11276-006-7791-8 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_101 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_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_2919 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 53.74 ASE 54.32 ASE AR 14 2006 1 09 06 55-70
allfields_unstemmed	10.1007/s11276-006-7791-8 doi (DE-627)SPR018507492 (SPR)s11276-006-7791-8-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Rossi, Michele verfasserin aut Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs. Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213 Zorzi, Michele verfasserin aut Rao, Ramesh R. verfasserin aut Enthalten in Wireless networks [S.l.] : Proquest, 1995 14(2006), 1 vom: 09. Juni, Seite 55-70 (DE-627)319427366 (DE-600)2006505-X 1572-8196 nnns volume:14 year:2006 number:1 day:09 month:06 pages:55-70 https://dx.doi.org/10.1007/s11276-006-7791-8 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_101 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_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_2919 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 53.74 ASE 54.32 ASE AR 14 2006 1 09 06 55-70
allfieldsGer	10.1007/s11276-006-7791-8 doi (DE-627)SPR018507492 (SPR)s11276-006-7791-8-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Rossi, Michele verfasserin aut Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs. Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213 Zorzi, Michele verfasserin aut Rao, Ramesh R. verfasserin aut Enthalten in Wireless networks [S.l.] : Proquest, 1995 14(2006), 1 vom: 09. Juni, Seite 55-70 (DE-627)319427366 (DE-600)2006505-X 1572-8196 nnns volume:14 year:2006 number:1 day:09 month:06 pages:55-70 https://dx.doi.org/10.1007/s11276-006-7791-8 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_101 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_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_2919 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 53.74 ASE 54.32 ASE AR 14 2006 1 09 06 55-70
allfieldsSound	10.1007/s11276-006-7791-8 doi (DE-627)SPR018507492 (SPR)s11276-006-7791-8-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.74 bkl 54.32 bkl Rossi, Michele verfasserin aut Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs. Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213 Zorzi, Michele verfasserin aut Rao, Ramesh R. verfasserin aut Enthalten in Wireless networks [S.l.] : Proquest, 1995 14(2006), 1 vom: 09. Juni, Seite 55-70 (DE-627)319427366 (DE-600)2006505-X 1572-8196 nnns volume:14 year:2006 number:1 day:09 month:06 pages:55-70 https://dx.doi.org/10.1007/s11276-006-7791-8 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_101 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_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_2919 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 53.74 ASE 54.32 ASE AR 14 2006 1 09 06 55-70
language	English
source	Enthalten in Wireless networks 14(2006), 1 vom: 09. Juni, Seite 55-70 volume:14 year:2006 number:1 day:09 month:06 pages:55-70
sourceStr	Enthalten in Wireless networks 14(2006), 1 vom: 09. Juni, Seite 55-70 volume:14 year:2006 number:1 day:09 month:06 pages:55-70
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wireless sensor networks Optimal routing Greedy forwarding policies Online optimization techniques Protocol design System analysis
dewey-raw	620
isfreeaccess_bool	false
container_title	Wireless networks
authorswithroles_txt_mv	Rossi, Michele @@aut@@ Zorzi, Michele @@aut@@ Rao, Ramesh R. @@aut@@
publishDateDaySort_date	2006-06-09T00:00:00Z
hierarchy_top_id	319427366
dewey-sort	3620
id	SPR018507492
language_de	englisch
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author	Rossi, Michele
spellingShingle	Rossi, Michele ddc 620 bkl 53.74 bkl 54.32 misc Wireless sensor networks misc Optimal routing misc Greedy forwarding policies misc Online optimization techniques misc Protocol design misc System analysis Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks
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topic_title	620 004 ASE 53.74 bkl 54.32 bkl Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks Wireless sensor networks (dpeaa)DE-He213 Optimal routing (dpeaa)DE-He213 Greedy forwarding policies (dpeaa)DE-He213 Online optimization techniques (dpeaa)DE-He213 Protocol design (dpeaa)DE-He213 System analysis (dpeaa)DE-He213
topic	ddc 620 bkl 53.74 bkl 54.32 misc Wireless sensor networks misc Optimal routing misc Greedy forwarding policies misc Online optimization techniques misc Protocol design misc System analysis
topic_unstemmed	ddc 620 bkl 53.74 bkl 54.32 misc Wireless sensor networks misc Optimal routing misc Greedy forwarding policies misc Online optimization techniques misc Protocol design misc System analysis
topic_browse	ddc 620 bkl 53.74 bkl 54.32 misc Wireless sensor networks misc Optimal routing misc Greedy forwarding policies misc Online optimization techniques misc Protocol design misc System analysis
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title	Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks
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title_full	Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks
author_sort	Rossi, Michele
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author_browse	Rossi, Michele Zorzi, Michele Rao, Ramesh R.
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title_sort	statistically assisted routing algorithms (sara) for hop count based forwarding in wireless sensor networks
title_auth	Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks
abstract	Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs.
abstractGer	Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs.
abstract_unstemmed	Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs.
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title_short	Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks
url	https://dx.doi.org/10.1007/s11276-006-7791-8
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author2	Zorzi, Michele Rao, Ramesh R.
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doi_str	10.1007/s11276-006-7791-8
up_date	2024-07-03T20:13:49.617Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018507492</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111061450.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2006 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11276-006-7791-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018507492</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11276-006-7791-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rossi, Michele</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistically assisted routing algorithms (SARA) for hop count based forwarding in wireless sensor networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The main goal of this paper is to provide routing–table-free online algorithms for wireless sensor networks (WSNs) to select cost (e.g., node residual energies) and delay efficient paths. As basic information to drive the routing process, both node costs and hop count distances are considered. Particular emphasis is given to greedy routing schemes, due to their suitability for resource constrained and highly dynamic networks. For what concerns greedy forwarding, we present the Statistically Assisted Routing Algorithm (SARA), where forwarding decisions are driven by statistical information on the costs of the nodes within coverage and in the second order neighborhood. By analysis, we prove that an optimal online policy exists, we derive its form and we exploit it as the core of SARA. Besides greedy techniques, sub–optimal algorithms where node costs can be partially propagated through the network are also presented. These techniques are based on real time learning LRTA algorithms which, through an initial exploratory phase, converge to quasi globally optimal paths. All the proposed schemes are then compared by simulation against globally optimal solutions, discussing the involved trade–offs and possible performance gains. The results show that the exploitation of second order cost information in SARA substantially increases the goodness of the selected paths with respect to fully localized greedy routing. Finally, the path quality can be further increased by LRTA schemes, whose convergence can be considerably enhanced by properly setting real time search parameters. However, these solutions fail in highly dynamic scenarios as they are unable to adapt the search process to time varying costs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless sensor networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal routing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Greedy forwarding policies</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Online optimization techniques</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protocol design</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zorzi, Michele</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Ramesh R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wireless networks</subfield><subfield code="d">[S.l.] : Proquest, 1995</subfield><subfield code="g">14(2006), 1 vom: 09. 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