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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Moura, J. & Hutchison, D. (2020). Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV. SN Applied Sciences. 2
Exportar Referência (IEEE)
J. A. Moura and D. Hutchison,  "Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV", in SN Applied Sciences, vol. 2, 2020
Exportar BibTeX
@article{moura2020_1618111057540,
	author = "Moura, J. and Hutchison, D.",
	title = "Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV",
	journal = "SN Applied Sciences",
	year = "2020",
	volume = "2",
	number = "",
	doi = "10.1007/s42452-020-03335-4",
	url = "https://www.springer.com/journal/42452"
}
Exportar RIS
TY  - JOUR
TI  - Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV
T2  - SN Applied Sciences
VL  - 2
AU  - Moura, J.
AU  - Hutchison, D.
PY  - 2020
SN  - 2523-3963
DO  - 10.1007/s42452-020-03335-4
UR  - https://www.springer.com/journal/42452
AB  - Cyber-Physical Systems (CPSs) are increasingly important in everyday applications including the latest mobile devices, power grids and intelligent buildings. CPS functionality has intrinsic characteristics including considerable heterogeneity, variable dynamics, and complexity of operation. These systems also typically have insufficient resources to satisfy their full demand for specialized services such as data edge storage, data fusion, and reasoning. These novel CPS characteristics require new management strategies to support the resilient global operation of CPSs. To reach this goal, we propose a Software Defined Networking based solution scaled out by Network Function Virtualization modules implemented as distributed management agents. Considering the obvious need for orchestrating the distributed agents towards the satisfaction of a common set of global CPS functional goals, we analyze distinct incentive strategies to enact a cooperative behavior among the agents. The repeated operation of each agent's local algorithm allows that agent to learn how to adjust its behavior following both its own experience and observed behavior in neighboring agents. Therefore, global CPS management can evolve iteratively to ensure a state of predictable and resilient operation.
ER  -