Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)

Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other,...
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Autor*in:	Mahmoud Z. Iskandarani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Attractor selection connected vehicles intelligent transportation system Gaussian interpolation network connectivity

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 51110-51119
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:51110-51119

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3174578

Katalog-ID:	DOAJ041654757

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spelling	10.1109/ACCESS.2022.3174578 doi (DE-627)DOAJ041654757 (DE-599)DOAJ46f5b85572254f6aaf1df9514dc43a65 DE-627 ger DE-627 rakwb eng TK1-9971 Mahmoud Z. Iskandarani verfasserin aut Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one. Attractor selection connected vehicles intelligent transportation system Gaussian interpolation network connectivity Electrical engineering. Electronics. Nuclear engineering In IEEE Access IEEE, 2014 10(2022), Seite 51110-51119 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:51110-51119 https://doi.org/10.1109/ACCESS.2022.3174578 kostenfrei https://doaj.org/article/46f5b85572254f6aaf1df9514dc43a65 kostenfrei https://ieeexplore.ieee.org/document/9773174/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 51110-51119
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allfieldsSound	10.1109/ACCESS.2022.3174578 doi (DE-627)DOAJ041654757 (DE-599)DOAJ46f5b85572254f6aaf1df9514dc43a65 DE-627 ger DE-627 rakwb eng TK1-9971 Mahmoud Z. Iskandarani verfasserin aut Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST) 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one. Attractor selection connected vehicles intelligent transportation system Gaussian interpolation network connectivity Electrical engineering. Electronics. Nuclear engineering In IEEE Access IEEE, 2014 10(2022), Seite 51110-51119 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:51110-51119 https://doi.org/10.1109/ACCESS.2022.3174578 kostenfrei https://doaj.org/article/46f5b85572254f6aaf1df9514dc43a65 kostenfrei https://ieeexplore.ieee.org/document/9773174/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 51110-51119
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Iskandarani</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. 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callnumber-first	T - Technology
author	Mahmoud Z. Iskandarani
spellingShingle	Mahmoud Z. Iskandarani misc TK1-9971 misc Attractor selection misc connected vehicles misc intelligent transportation system misc Gaussian interpolation misc network connectivity misc Electrical engineering. Electronics. Nuclear engineering Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)
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topic_title	TK1-9971 Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST) Attractor selection connected vehicles intelligent transportation system Gaussian interpolation network connectivity
topic	misc TK1-9971 misc Attractor selection misc connected vehicles misc intelligent transportation system misc Gaussian interpolation misc network connectivity misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Attractor selection misc connected vehicles misc intelligent transportation system misc Gaussian interpolation misc network connectivity misc Electrical engineering. Electronics. Nuclear engineering
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title	Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)
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title_sort	computing effective vehicular network connectivity using gaussian based attractor selection technique (gast)
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title_auth	Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)
abstract	Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one.
abstractGer	Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one.
abstract_unstemmed	Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one.
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title_short	Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)
url	https://doi.org/10.1109/ACCESS.2022.3174578 https://doaj.org/article/46f5b85572254f6aaf1df9514dc43a65 https://ieeexplore.ieee.org/document/9773174/ https://doaj.org/toc/2169-3536
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Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)

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