Synergies between cloud-fag-thing and brain-spinal cord-nerve networks

Balevi E., Gitlin R. D.

2018 Information Theory and Applications Workshop, ITA 2018, California, United States Of America, 11 - 16 February 2018 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ita.2018.8502957
  • City: California
  • Country: United States Of America
  • Keywords: Central nervous system, Fog networking, Machine learning, Spinal cord, Stochastic geometry
  • Middle East Technical University Affiliated: No


© 2018 IEEE.This paper is directed towards describing the striking similarities and synergies between cloud and fog nodes that constitute the cloud-Jog-thing [Fog Network] architecture proposed for 5G networks and the human brain-spinal cord-nerve network model. On the one hand, the central nervous system can be better modeled considering the duality with Fog Networks, and, on the other hand, novel algorithms/protocols inspired from the central nervous system can be developed for throughput and latency performance improvement in Fog Networks. Designing and managing large-scale Fog Networks using stochastic geometry and machine learning is applied to determine the optimum number of fog nodes and their locations that optimize throughput and latency for 5G networks. Having observed the close relation between the Fog Networks and the spinal cord, these results may be adapted to increase understanding of the role of spinal cord plasticity in learning and ultimately suggest new means of treating central nervous system disorders associated with the spinal cord plasticity. Inspired by the cooperation between the brain and the spinal cord, a modified coded caching policy is proposed for Fog Networks, that is, the files to be stored at the fog nodes are determined as a result of continuous information flow between cloud and fog nodes through the latent variables assigned to files.