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Secure and efficient data delivery for fog-assisted wireless body area networks

  • Thaier HayajnehEmail author
  • Kristen Griggs
  • Muhammad Imran
  • Bassam J. Mohd
Article
  • 48 Downloads
Part of the following topical collections:
  1. Special issue on Fog Computing for Healthcare

Abstract

The growth of remote patient monitoring technology introduces new opportunities for improving patient outcomes, and Wireless Body Area Networks (WBANs) are a key piece in building a successful system. However, due to the limited power and computational resources of WBAN sensor nodes, combined with user mobility and large network coverage areas, integrating WBANs with cloud and fog computing presents one of the most viable options for successful remote monitoring. In order to help maintain the real-time operations of a fog-assisted WBAN, we propose a secure and efficient data delivery protocol that will reduce delay and protect against malicious attacks on the wireless signal. The protocol is composed of three custom algorithms that address channel assignment, gateway association, and introduce a new delay- and energy-aware routing metric. The channel assignment algorithm is designed to minimize and avoid interference, including jamming nodes. The fog gateway association algorithm helps to improve the efficiency and security of the connection between the WBAN and the remote resources. Similarly, the proposed routing metric is used to construct routes that both minimize delay and conserve power at the nodes along the path for improved efficiency and lifespan of the network. The system was simulated and tested under a variety of conditions to evaluate its performance in regards to mutual interference, human mobility, fog density, and attacks by jamming nodes. The results showed clear improvements in the efficiency and resiliency of the fog-assisted WBAN system when utilizing our protocol.

Keywords

E-Health Body area networks routing Fog computing Cloud computing Efficient routing Channel assignment 

Notes

Acknowledgements

Imran’s work is supported by the Deanship of Scientific Research, King Saud University, through Research Group No. RG-1435-051.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fordham Center for CybersecurityFordham UniversityNew YorkUSA
  2. 2.College of Applied Computer ScienceKing Saud UniversityAlMuzahmiahSaudi Arabia
  3. 3.Computer Engineering DepartmentHashemite UniversityZarqaJordan

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