An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks
Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Yaguang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Surgeon-patient matching based on pairwise comparisons information for elective surgery - Jiang, Yan-Ping ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:82 ; year:2018 ; pages:220-234 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.future.2018.01.007 |
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| Katalog-ID: |
ELV041996224 |
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| 520 | |a Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. | ||
| 520 | |a Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. | ||
| 650 | 7 | |a High performance sensing |2 Elsevier | |
| 650 | 7 | |a Node self-deployment |2 Elsevier | |
| 650 | 7 | |a Mobile cellular learning automata |2 Elsevier | |
| 650 | 7 | |a Big data perception |2 Elsevier | |
| 650 | 7 | |a Mobile sensing network |2 Elsevier | |
| 650 | 7 | |a On-demand coverage |2 Elsevier | |
| 700 | 1 | |a Wang, Xiaoming |4 oth | |
| 700 | 1 | |a Hao, Fei |4 oth | |
| 700 | 1 | |a Wang, Liang |4 oth | |
| 700 | 1 | |a Zhang, Lichen |4 oth | |
| 700 | 1 | |a Zhao, Ruonan |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Jiang, Yan-Ping ELSEVIER |t Surgeon-patient matching based on pairwise comparisons information for elective surgery |d 2020 |g Amsterdam [u.a.] |w (DE-627)ELV004280385 |
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10.1016/j.future.2018.01.007 doi GBV00000000000497.pica (DE-627)ELV041996224 (ELSEVIER)S0167-739X(17)31326-2 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Lin, Yaguang verfasserin aut An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. High performance sensing Elsevier Node self-deployment Elsevier Mobile cellular learning automata Elsevier Big data perception Elsevier Mobile sensing network Elsevier On-demand coverage Elsevier Wang, Xiaoming oth Hao, Fei oth Wang, Liang oth Zhang, Lichen oth Zhao, Ruonan oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:82 year:2018 pages:220-234 extent:15 https://doi.org/10.1016/j.future.2018.01.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 82 2018 220-234 15 |
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10.1016/j.future.2018.01.007 doi GBV00000000000497.pica (DE-627)ELV041996224 (ELSEVIER)S0167-739X(17)31326-2 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Lin, Yaguang verfasserin aut An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. High performance sensing Elsevier Node self-deployment Elsevier Mobile cellular learning automata Elsevier Big data perception Elsevier Mobile sensing network Elsevier On-demand coverage Elsevier Wang, Xiaoming oth Hao, Fei oth Wang, Liang oth Zhang, Lichen oth Zhao, Ruonan oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:82 year:2018 pages:220-234 extent:15 https://doi.org/10.1016/j.future.2018.01.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 82 2018 220-234 15 |
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10.1016/j.future.2018.01.007 doi GBV00000000000497.pica (DE-627)ELV041996224 (ELSEVIER)S0167-739X(17)31326-2 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Lin, Yaguang verfasserin aut An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. High performance sensing Elsevier Node self-deployment Elsevier Mobile cellular learning automata Elsevier Big data perception Elsevier Mobile sensing network Elsevier On-demand coverage Elsevier Wang, Xiaoming oth Hao, Fei oth Wang, Liang oth Zhang, Lichen oth Zhao, Ruonan oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:82 year:2018 pages:220-234 extent:15 https://doi.org/10.1016/j.future.2018.01.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 82 2018 220-234 15 |
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10.1016/j.future.2018.01.007 doi GBV00000000000497.pica (DE-627)ELV041996224 (ELSEVIER)S0167-739X(17)31326-2 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Lin, Yaguang verfasserin aut An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. High performance sensing Elsevier Node self-deployment Elsevier Mobile cellular learning automata Elsevier Big data perception Elsevier Mobile sensing network Elsevier On-demand coverage Elsevier Wang, Xiaoming oth Hao, Fei oth Wang, Liang oth Zhang, Lichen oth Zhao, Ruonan oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:82 year:2018 pages:220-234 extent:15 https://doi.org/10.1016/j.future.2018.01.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 82 2018 220-234 15 |
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10.1016/j.future.2018.01.007 doi GBV00000000000497.pica (DE-627)ELV041996224 (ELSEVIER)S0167-739X(17)31326-2 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Lin, Yaguang verfasserin aut An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. High performance sensing Elsevier Node self-deployment Elsevier Mobile cellular learning automata Elsevier Big data perception Elsevier Mobile sensing network Elsevier On-demand coverage Elsevier Wang, Xiaoming oth Hao, Fei oth Wang, Liang oth Zhang, Lichen oth Zhao, Ruonan oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:82 year:2018 pages:220-234 extent:15 https://doi.org/10.1016/j.future.2018.01.007 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 82 2018 220-234 15 |
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Enthalten in Surgeon-patient matching based on pairwise comparisons information for elective surgery Amsterdam [u.a.] volume:82 year:2018 pages:220-234 extent:15 |
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Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. |
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Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. |
| abstract_unstemmed |
Mobile Sensing Networks have been widely applied to many fields for big data perception such as intelligent transportation, medical health and environment sensing. However, in some complex environments and unreachable regions of inconvenience for human, the establishment of the mobile sensing networks, the layout of the nodes and the control of the network topology to achieve high performance sensing of big data are increasingly becoming a main issue in the applications of the mobile sensing networks. To deal with this problem, we propose a novel on-demand coverage based self-deployment algorithm for big data perception based on mobile sensing networks in this paper. Firstly, by considering characteristics of mobile sensing nodes, we extend the cellular automata model and propose a new mobile cellular automata model for effectively characterizing the spatial–temporal evolutionary process of nodes. Secondly, based on the learning automata theory and the historical information of node movement, we further explore a new mobile cellular learning automata model, in which nodes can self-adaptively and intelligently decide the best direction of movement with low energy consumption. Finally, we propose a new optimization algorithm which can quickly solve the node self-adaptive deployment problem, thus, we derive the best deployment scheme of nodes in a short time. The extensive simulation results show that the proposed algorithm in this paper outperforms the existing algorithms by as much as 40% in terms of the degree of satisfaction of network coverage, the iterations of the algorithm, the average moving steps of nodes and the energy consumption of nodes. Hence, we believe that our work will make contributions to large-scale adaptive deployment and high performance sensing scenarios of the mobile sensing networks. |
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