Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments
Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more eff...
Ausführliche Beschreibung
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| Autor*in: |
Galati, Adriano [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Wien 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Social network analysis and mining - Wien : Springer, 2011, 5(2015), 1 vom: 03. Apr. |
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| Übergeordnetes Werk: |
volume:5 ; year:2015 ; number:1 ; day:03 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s13278-015-0251-7 |
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SPR031183166 |
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| 520 | |a Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. | ||
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| 650 | 4 | |a Human mobility patterns |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Network measurements |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mobile networks |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Djemame, Karim |4 aut | |
| 700 | 1 | |a Greenhalgh, Chris |4 aut | |
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10.1007/s13278-015-0251-7 doi (DE-627)SPR031183166 (SPR)s13278-015-0251-7-e DE-627 ger DE-627 rakwb eng Galati, Adriano verfasserin aut Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 Djemame, Karim aut Greenhalgh, Chris aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 03. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:03 month:04 https://dx.doi.org/10.1007/s13278-015-0251-7 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_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_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 5 2015 1 03 04 |
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10.1007/s13278-015-0251-7 doi (DE-627)SPR031183166 (SPR)s13278-015-0251-7-e DE-627 ger DE-627 rakwb eng Galati, Adriano verfasserin aut Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 Djemame, Karim aut Greenhalgh, Chris aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 03. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:03 month:04 https://dx.doi.org/10.1007/s13278-015-0251-7 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_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_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 5 2015 1 03 04 |
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10.1007/s13278-015-0251-7 doi (DE-627)SPR031183166 (SPR)s13278-015-0251-7-e DE-627 ger DE-627 rakwb eng Galati, Adriano verfasserin aut Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 Djemame, Karim aut Greenhalgh, Chris aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 03. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:03 month:04 https://dx.doi.org/10.1007/s13278-015-0251-7 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_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_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 5 2015 1 03 04 |
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10.1007/s13278-015-0251-7 doi (DE-627)SPR031183166 (SPR)s13278-015-0251-7-e DE-627 ger DE-627 rakwb eng Galati, Adriano verfasserin aut Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 Djemame, Karim aut Greenhalgh, Chris aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 03. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:03 month:04 https://dx.doi.org/10.1007/s13278-015-0251-7 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_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_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 5 2015 1 03 04 |
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10.1007/s13278-015-0251-7 doi (DE-627)SPR031183166 (SPR)s13278-015-0251-7-e DE-627 ger DE-627 rakwb eng Galati, Adriano verfasserin aut Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 Djemame, Karim aut Greenhalgh, Chris aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 03. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:03 month:04 https://dx.doi.org/10.1007/s13278-015-0251-7 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_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_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 5 2015 1 03 04 |
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Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. 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Galati, Adriano |
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Galati, Adriano misc Opportunistic networking misc Human mobility patterns misc Network measurements misc Mobile networks Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments |
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Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments Opportunistic networking (dpeaa)DE-He213 Human mobility patterns (dpeaa)DE-He213 Network measurements (dpeaa)DE-He213 Mobile networks (dpeaa)DE-He213 |
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analysis of human mobility patterns for opportunistic forwarding in shopping mall environments |
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Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments |
| abstract |
Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. © Springer-Verlag Wien 2015 |
| abstractGer |
Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. © Springer-Verlag Wien 2015 |
| abstract_unstemmed |
Abstract Mobility plays a key role in the forwarding of data in delay-tolerant mobile ad hoc networks, as it is mobility that gives rise to local connection opportunities. Different patterns of mobility may give rise to different opportunity for communication, and different protocols may be more effective in particular situations. It is thus becoming increasingly important to understand user mobility patterns. In this paper, we seek to improve understanding of human mobility patterns in environments having definite and highly organized structure, such as shopping malls. We analyze contact traces between devices carried by people in a medium-scale shopping mall to characterize human mobility in such an environment. This will allow us to design suitable routing algorithms for delay-tolerant network applications for such scenarios as well as to develop and validate a novel mobility model which can be used by the research community to simulate mobile networks in such settings. We show that people’s motion is different according to their relationship to the environment in which they are and present a method to identify individuals expressing different mobility patterns based on delay-tolerant network metrics. From the contact traces, we identify two main groups with different mobility patterns that we name customers and sellers. For these two groups, we observe and quantify mobility characteristics, and present real-world measurement results. Finally, to understand better the role of groups of message carriers expressing different mobility patterns, we perform simulations of a derivative of the Epidemic protocol with real-world mobility traces, which distinguishes between two groups of carriers and entrusts messages through either one or the other. We discuss the implications of our results and make recommendations to guide the design of ad hoc forwarding algorithms for delay-tolerant mobile ad hoc networks in shopping mall environments and to help model realistic simulation scenarios. © Springer-Verlag Wien 2015 |
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Analysis of human mobility patterns for opportunistic forwarding in shopping mall environments |
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https://dx.doi.org/10.1007/s13278-015-0251-7 |
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Djemame, Karim Greenhalgh, Chris |
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10.1007/s13278-015-0251-7 |
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| score |
7.397253 |