Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things
Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cao, Hongtang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Earth science informatics - Berlin : Springer, 2008, 16(2023), 2 vom: 14. Feb., Seite 1195-1205 |
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| Übergeordnetes Werk: |
volume:16 ; year:2023 ; number:2 ; day:14 ; month:02 ; pages:1195-1205 |
| Links: |
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| DOI / URN: |
10.1007/s12145-023-00942-8 |
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| Katalog-ID: |
SPR05157005X |
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| 520 | |a Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. | ||
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| 650 | 4 | |a Ocean Internet of Things |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Underwater nodes |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Distributed spatial crowdsourcing |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Guo, Ying |4 aut | |
| 700 | 1 | |a Li, Fei |4 aut | |
| 700 | 1 | |a Zhang, Keyi |4 aut | |
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10.1007/s12145-023-00942-8 doi (DE-627)SPR05157005X (SPR)s12145-023-00942-8-e DE-627 ger DE-627 rakwb eng Cao, Hongtang verfasserin aut Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. Spatial function (dpeaa)DE-He213 Ocean Internet of Things (dpeaa)DE-He213 Underwater nodes (dpeaa)DE-He213 Distributed spatial crowdsourcing (dpeaa)DE-He213 Guo, Ying aut Li, Fei aut Zhang, Keyi aut Enthalten in Earth science informatics Berlin : Springer, 2008 16(2023), 2 vom: 14. Feb., Seite 1195-1205 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:16 year:2023 number:2 day:14 month:02 pages:1195-1205 https://dx.doi.org/10.1007/s12145-023-00942-8 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 AR 16 2023 2 14 02 1195-1205 |
| spelling |
10.1007/s12145-023-00942-8 doi (DE-627)SPR05157005X (SPR)s12145-023-00942-8-e DE-627 ger DE-627 rakwb eng Cao, Hongtang verfasserin aut Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. Spatial function (dpeaa)DE-He213 Ocean Internet of Things (dpeaa)DE-He213 Underwater nodes (dpeaa)DE-He213 Distributed spatial crowdsourcing (dpeaa)DE-He213 Guo, Ying aut Li, Fei aut Zhang, Keyi aut Enthalten in Earth science informatics Berlin : Springer, 2008 16(2023), 2 vom: 14. Feb., Seite 1195-1205 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:16 year:2023 number:2 day:14 month:02 pages:1195-1205 https://dx.doi.org/10.1007/s12145-023-00942-8 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 AR 16 2023 2 14 02 1195-1205 |
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10.1007/s12145-023-00942-8 doi (DE-627)SPR05157005X (SPR)s12145-023-00942-8-e DE-627 ger DE-627 rakwb eng Cao, Hongtang verfasserin aut Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. Spatial function (dpeaa)DE-He213 Ocean Internet of Things (dpeaa)DE-He213 Underwater nodes (dpeaa)DE-He213 Distributed spatial crowdsourcing (dpeaa)DE-He213 Guo, Ying aut Li, Fei aut Zhang, Keyi aut Enthalten in Earth science informatics Berlin : Springer, 2008 16(2023), 2 vom: 14. Feb., Seite 1195-1205 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:16 year:2023 number:2 day:14 month:02 pages:1195-1205 https://dx.doi.org/10.1007/s12145-023-00942-8 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 AR 16 2023 2 14 02 1195-1205 |
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10.1007/s12145-023-00942-8 doi (DE-627)SPR05157005X (SPR)s12145-023-00942-8-e DE-627 ger DE-627 rakwb eng Cao, Hongtang verfasserin aut Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. Spatial function (dpeaa)DE-He213 Ocean Internet of Things (dpeaa)DE-He213 Underwater nodes (dpeaa)DE-He213 Distributed spatial crowdsourcing (dpeaa)DE-He213 Guo, Ying aut Li, Fei aut Zhang, Keyi aut Enthalten in Earth science informatics Berlin : Springer, 2008 16(2023), 2 vom: 14. Feb., Seite 1195-1205 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:16 year:2023 number:2 day:14 month:02 pages:1195-1205 https://dx.doi.org/10.1007/s12145-023-00942-8 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 AR 16 2023 2 14 02 1195-1205 |
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10.1007/s12145-023-00942-8 doi (DE-627)SPR05157005X (SPR)s12145-023-00942-8-e DE-627 ger DE-627 rakwb eng Cao, Hongtang verfasserin aut Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. Spatial function (dpeaa)DE-He213 Ocean Internet of Things (dpeaa)DE-He213 Underwater nodes (dpeaa)DE-He213 Distributed spatial crowdsourcing (dpeaa)DE-He213 Guo, Ying aut Li, Fei aut Zhang, Keyi aut Enthalten in Earth science informatics Berlin : Springer, 2008 16(2023), 2 vom: 14. Feb., Seite 1195-1205 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:16 year:2023 number:2 day:14 month:02 pages:1195-1205 https://dx.doi.org/10.1007/s12145-023-00942-8 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 AR 16 2023 2 14 02 1195-1205 |
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To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. 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distributed spatial crowdsourcing based task allocation in ocean internet of things |
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Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things |
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Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things |
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Guo, Ying Li, Fei Zhang, Keyi |
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2024-07-03T22:34:47.106Z |
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| score |
7.3991556 |