Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing-Reference-Cited by-同舟云学术

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Fast/Slow-Pathway Bayesian Attractor Model for IoT Networks Based on Software-Defined Networking with Virtual Network Slicing

Published:2020-11-25 Issue: Volume: Page:135-154
ISSN:
Container-title:Fluctuation-Induced Network Control and Learning
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Author:

Alparslan Onur,Arakawa Shin’ichi

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-33-4976-6_6

Reference22 articles.

1. Alparslan, O., Arakawa, S., Murata, M.: SDN-based control of IoT network by brain-inspired Bayesian attractor model and network slicing. Appl. Sci. 10(17), 5773 (2020)

2. Benson, E.: The synaptic self. Monit. Psychol. 33(10), 40 (2002)

3. Bitzer, S., Bruineberg, J., Kiebel, S.J.: A Bayesian attractor model for perceptual decision making. PLoS Comput. Biol. 11(8) (2015)

4. Esaki, H., Nakamura, R.: Overlaying and slicing for IoT era based on Internet’s end-to-end discipline. In: 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), pp. 1–6. IEEE, Piscataway (2017)

5. Gribaudo, M., Iacono, M., Levis, A.H.: An IoT-based monitoring approach for cultural heritage sites: the Matera case. Concurren. Comput. Pract. Exp. 29(11), e4153 (2017)

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