Accessibility navigation

IoT-fog optimal workload via fog offloading

Al-Khafajiy, M. ORCID:, Baker, T., Waraich, A., Al-Jumeily, D. and Hussain, A. (2018) IoT-fog optimal workload via fog offloading. In: IEEE/ACM International Conference on Utility and Cloud Computing Companion, 17-20 Dec 2018, Zurich, Switzerland, pp. 359-364,

[img] Text - Published Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1109/UCC-Companion.2018.00081


Billions of devises are expected to be connected to the Internet of Things network in the near future, therefore, a considerable amount of data will be generated, and gathered every second. The current network paradigm, which relies on centralised data-centres (a.k.a. Cloud computing), becomes impractical solution for IoT data due to the long distance between the data source and designated data-center. In other words, the amount of time taken by data to travel to a data-centre makes the importance of the data vanished. Therefore, the network topology have been evolved to permit data processing at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service via efficient and quick responding to sensors requests. In this paper, we are proposing a fog computing architecture and framework to enhance QoS via request offloading method. The proposed method employ a collaboration strategy among fog nodes in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of IoT requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of fog computing into the computing ecosystem.

Item Type:Conference or Workshop Item (Paper)
Divisions:No Reading authors. Back catalogue items
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:90123

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation