Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing

Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones s...
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Autor*in:	M, Thejaswini [verfasserIn] Rajalakshmi, P. [verfasserIn] Desai, U. B. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Duration of stay Mobile phone Opportunistic crowd sensing Scheduling Sensors

Übergeordnetes Werk:	Enthalten in: Peer-to-peer networking and applications - New York, NY : Springer, 2008, 9(2015), 4 vom: 15. Juli, Seite 721-730
Übergeordnetes Werk:	volume:9 ; year:2015 ; number:4 ; day:15 ; month:07 ; pages:721-730

Links:	Volltext

DOI / URN:	10.1007/s12083-015-0382-7

Katalog-ID:	SPR024231134

Internformat


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520			\|a Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.
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allfields	10.1007/s12083-015-0382-7 doi (DE-627)SPR024231134 (SPR)s12083-015-0382-7-e DE-627 ger DE-627 rakwb eng 004 ASE M, Thejaswini verfasserin aut Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Rajalakshmi, P. verfasserin aut Desai, U. B. verfasserin aut Enthalten in Peer-to-peer networking and applications New York, NY : Springer, 2008 9(2015), 4 vom: 15. Juli, Seite 721-730 (DE-627)565518895 (DE-600)2424434-X 1936-6450 nnns volume:9 year:2015 number:4 day:15 month:07 pages:721-730 https://dx.doi.org/10.1007/s12083-015-0382-7 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_65 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2015 4 15 07 721-730
spelling	10.1007/s12083-015-0382-7 doi (DE-627)SPR024231134 (SPR)s12083-015-0382-7-e DE-627 ger DE-627 rakwb eng 004 ASE M, Thejaswini verfasserin aut Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Rajalakshmi, P. verfasserin aut Desai, U. B. verfasserin aut Enthalten in Peer-to-peer networking and applications New York, NY : Springer, 2008 9(2015), 4 vom: 15. Juli, Seite 721-730 (DE-627)565518895 (DE-600)2424434-X 1936-6450 nnns volume:9 year:2015 number:4 day:15 month:07 pages:721-730 https://dx.doi.org/10.1007/s12083-015-0382-7 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_65 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2015 4 15 07 721-730
allfields_unstemmed	10.1007/s12083-015-0382-7 doi (DE-627)SPR024231134 (SPR)s12083-015-0382-7-e DE-627 ger DE-627 rakwb eng 004 ASE M, Thejaswini verfasserin aut Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Rajalakshmi, P. verfasserin aut Desai, U. B. verfasserin aut Enthalten in Peer-to-peer networking and applications New York, NY : Springer, 2008 9(2015), 4 vom: 15. Juli, Seite 721-730 (DE-627)565518895 (DE-600)2424434-X 1936-6450 nnns volume:9 year:2015 number:4 day:15 month:07 pages:721-730 https://dx.doi.org/10.1007/s12083-015-0382-7 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_65 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2015 4 15 07 721-730
allfieldsGer	10.1007/s12083-015-0382-7 doi (DE-627)SPR024231134 (SPR)s12083-015-0382-7-e DE-627 ger DE-627 rakwb eng 004 ASE M, Thejaswini verfasserin aut Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Rajalakshmi, P. verfasserin aut Desai, U. B. verfasserin aut Enthalten in Peer-to-peer networking and applications New York, NY : Springer, 2008 9(2015), 4 vom: 15. Juli, Seite 721-730 (DE-627)565518895 (DE-600)2424434-X 1936-6450 nnns volume:9 year:2015 number:4 day:15 month:07 pages:721-730 https://dx.doi.org/10.1007/s12083-015-0382-7 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_65 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2015 4 15 07 721-730
allfieldsSound	10.1007/s12083-015-0382-7 doi (DE-627)SPR024231134 (SPR)s12083-015-0382-7-e DE-627 ger DE-627 rakwb eng 004 ASE M, Thejaswini verfasserin aut Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213 Rajalakshmi, P. verfasserin aut Desai, U. B. verfasserin aut Enthalten in Peer-to-peer networking and applications New York, NY : Springer, 2008 9(2015), 4 vom: 15. Juli, Seite 721-730 (DE-627)565518895 (DE-600)2424434-X 1936-6450 nnns volume:9 year:2015 number:4 day:15 month:07 pages:721-730 https://dx.doi.org/10.1007/s12083-015-0382-7 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_65 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2015 4 15 07 721-730
language	English
source	Enthalten in Peer-to-peer networking and applications 9(2015), 4 vom: 15. Juli, Seite 721-730 volume:9 year:2015 number:4 day:15 month:07 pages:721-730
sourceStr	Enthalten in Peer-to-peer networking and applications 9(2015), 4 vom: 15. Juli, Seite 721-730 volume:9 year:2015 number:4 day:15 month:07 pages:721-730
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Duration of stay Mobile phone Opportunistic crowd sensing Scheduling Sensors
dewey-raw	004
isfreeaccess_bool	false
container_title	Peer-to-peer networking and applications
authorswithroles_txt_mv	M, Thejaswini @@aut@@ Rajalakshmi, P. @@aut@@ Desai, U. B. @@aut@@
publishDateDaySort_date	2015-07-15T00:00:00Z
hierarchy_top_id	565518895
dewey-sort	14
id	SPR024231134
language_de	englisch
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author	M, Thejaswini
spellingShingle	M, Thejaswini ddc 004 misc Duration of stay misc Mobile phone misc Opportunistic crowd sensing misc Scheduling misc Sensors Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
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topic_title	004 ASE Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing Duration of stay (dpeaa)DE-He213 Mobile phone (dpeaa)DE-He213 Opportunistic crowd sensing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Sensors (dpeaa)DE-He213
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title	Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
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title_full	Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
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title_sort	duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
title_auth	Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
abstract	Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.
abstractGer	Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.
abstract_unstemmed	Abstract Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability.
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container_issue	4
title_short	Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing
url	https://dx.doi.org/10.1007/s12083-015-0382-7
remote_bool	true
author2	Rajalakshmi, P. Desai, U. B.
author2Str	Rajalakshmi, P. Desai, U. B.
ppnlink	565518895
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doi_str	10.1007/s12083-015-0382-7
up_date	2024-07-04T00:04:28.521Z
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Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing

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