Event Detection in Molecular Communication Networks With Anomalous Diffusion
A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mai, Trang C [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE communications letters - New York, NY : IEEE, 1997, 21(2017), 6, Seite 1249-1252 |
|---|---|
| Übergeordnetes Werk: |
volume:21 ; year:2017 ; number:6 ; pages:1249-1252 |
| Links: |
|---|
| DOI / URN: |
10.1109/LCOMM.2017.2669315 |
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OLC1995388483 |
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| 520 | |a A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. | ||
| 650 | 4 | |a Event detection | |
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| 700 | 1 | |a Duong, Trung Q |4 oth | |
| 700 | 1 | |a Di Renzo, Marco |4 oth | |
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10.1109/LCOMM.2017.2669315 doi PQ20171125 (DE-627)OLC1995388483 (DE-599)GBVOLC1995388483 (PRQ)c1378-1c36809858c374acf74773ea214377f05efc0f79c19b482a8df763c54d19c2d0 (KEY)0326031320170000021000601249eventdetectioninmolecularcommunicationnetworkswith DE-627 ger DE-627 rakwb eng 620 004 DE-600 Mai, Trang C verfasserin aut Event Detection in Molecular Communication Networks With Anomalous Diffusion 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. Event detection Molecular communications Molecular communication Diffusion processes Training anomalous diffusion Random variables Throughput Nanobioscience Egan, Malcolm oth Duong, Trung Q oth Di Renzo, Marco oth Enthalten in IEEE communications letters New York, NY : IEEE, 1997 21(2017), 6, Seite 1249-1252 (DE-627)224842838 (DE-600)1360382-6 (DE-576)058070435 1089-7798 nnns volume:21 year:2017 number:6 pages:1249-1252 http://dx.doi.org/10.1109/LCOMM.2017.2669315 Volltext http://ieeexplore.ieee.org/document/7856998 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 21 2017 6 1249-1252 |
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10.1109/LCOMM.2017.2669315 doi PQ20171125 (DE-627)OLC1995388483 (DE-599)GBVOLC1995388483 (PRQ)c1378-1c36809858c374acf74773ea214377f05efc0f79c19b482a8df763c54d19c2d0 (KEY)0326031320170000021000601249eventdetectioninmolecularcommunicationnetworkswith DE-627 ger DE-627 rakwb eng 620 004 DE-600 Mai, Trang C verfasserin aut Event Detection in Molecular Communication Networks With Anomalous Diffusion 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. Event detection Molecular communications Molecular communication Diffusion processes Training anomalous diffusion Random variables Throughput Nanobioscience Egan, Malcolm oth Duong, Trung Q oth Di Renzo, Marco oth Enthalten in IEEE communications letters New York, NY : IEEE, 1997 21(2017), 6, Seite 1249-1252 (DE-627)224842838 (DE-600)1360382-6 (DE-576)058070435 1089-7798 nnns volume:21 year:2017 number:6 pages:1249-1252 http://dx.doi.org/10.1109/LCOMM.2017.2669315 Volltext http://ieeexplore.ieee.org/document/7856998 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 21 2017 6 1249-1252 |
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10.1109/LCOMM.2017.2669315 doi PQ20171125 (DE-627)OLC1995388483 (DE-599)GBVOLC1995388483 (PRQ)c1378-1c36809858c374acf74773ea214377f05efc0f79c19b482a8df763c54d19c2d0 (KEY)0326031320170000021000601249eventdetectioninmolecularcommunicationnetworkswith DE-627 ger DE-627 rakwb eng 620 004 DE-600 Mai, Trang C verfasserin aut Event Detection in Molecular Communication Networks With Anomalous Diffusion 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. Event detection Molecular communications Molecular communication Diffusion processes Training anomalous diffusion Random variables Throughput Nanobioscience Egan, Malcolm oth Duong, Trung Q oth Di Renzo, Marco oth Enthalten in IEEE communications letters New York, NY : IEEE, 1997 21(2017), 6, Seite 1249-1252 (DE-627)224842838 (DE-600)1360382-6 (DE-576)058070435 1089-7798 nnns volume:21 year:2017 number:6 pages:1249-1252 http://dx.doi.org/10.1109/LCOMM.2017.2669315 Volltext http://ieeexplore.ieee.org/document/7856998 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 21 2017 6 1249-1252 |
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10.1109/LCOMM.2017.2669315 doi PQ20171125 (DE-627)OLC1995388483 (DE-599)GBVOLC1995388483 (PRQ)c1378-1c36809858c374acf74773ea214377f05efc0f79c19b482a8df763c54d19c2d0 (KEY)0326031320170000021000601249eventdetectioninmolecularcommunicationnetworkswith DE-627 ger DE-627 rakwb eng 620 004 DE-600 Mai, Trang C verfasserin aut Event Detection in Molecular Communication Networks With Anomalous Diffusion 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. Event detection Molecular communications Molecular communication Diffusion processes Training anomalous diffusion Random variables Throughput Nanobioscience Egan, Malcolm oth Duong, Trung Q oth Di Renzo, Marco oth Enthalten in IEEE communications letters New York, NY : IEEE, 1997 21(2017), 6, Seite 1249-1252 (DE-627)224842838 (DE-600)1360382-6 (DE-576)058070435 1089-7798 nnns volume:21 year:2017 number:6 pages:1249-1252 http://dx.doi.org/10.1109/LCOMM.2017.2669315 Volltext http://ieeexplore.ieee.org/document/7856998 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 21 2017 6 1249-1252 |
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A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. |
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A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1995388483</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715061955.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170721s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/LCOMM.2017.2669315</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1995388483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1995388483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1378-1c36809858c374acf74773ea214377f05efc0f79c19b482a8df763c54d19c2d0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0326031320170000021000601249eventdetectioninmolecularcommunicationnetworkswith</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mai, Trang C</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Event Detection in Molecular Communication Networks With Anomalous Diffusion</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. 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