Fog computing architecture to enable consumer centric internet of things services
Abstract
Fog Computing is a recent computing paradigm that is extending cloud computing towards the edge of network. Due to its proximity to end-users, dense geographical distribution, open platform and support for high mobility, Fog Computing platforms can provide services with reduced latency and improved QoS. Thus it is becoming an important enabler for consumer centric Internet of Things based applications and services that require real time operations e.g. connected vehicles, smart road intersection management and smart grid. The paper discusses one such architecture for connected vehicles with Road Side Units (RSUs) and M2M gateways including the Fog Computing Platform. M2M data processing with semantics, discovery and management of connected vehicles are briefly discussed as consumer centric IoT services enabled by the distinct characteristics of Fog Computing.
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Fog computing architecture is well situated to process machine-to-machine data streams, for example from connected vehicles.
“Data handling is an obvious concern in the Big Data era; FC architecture is well situated to process machine-to-machine data streams with, for example, connected vehicles [172,173].”
From Edge AI: A survey (2023) · cites this paper as [172]
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Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.050 · Cited by 1,157 · see citing works
Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing
Dolui, K., & Datta, S. K. (2017) · 2017 Global Internet of Things Summit (GIoTS) · doi:10.1109/giots.2017.8016213 · Cited by 492 · see citing works
Self-citationFog Computing for Sustainable Smart Cities: A Survey
Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., & Vasilakos, A. V. (2018) · ACM Computing Surveys · doi:10.1145/3057266 · Cited by 459 · see citing works
Bilal, K., Khalid, O., Erbad, A., & Khan, S. U. (2018) · Computer Networks · doi:10.1016/j.comnet.2017.10.002 · Cited by 334 · see citing works
Blockchain-based resource allocation model in fog computing
Wang, H., Wang, L., Zhou, Z., Tao, X., Pau, G., & Arena, F. (2019) · Applied Sciences · doi:10.3390/app9245538 · Cited by 58 · see citing works
Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm
Lu, L., Xu, L., Xu, B., Li, G., & Cai, H. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2871694 · Cited by 41 · see citing works
- Authors
- Datta, S. K., Bonnet, C., & Haerri, J.
- Affiliations
- EURECOM (FR)
- Venue
- 2015 International Symposium on Consumer Electronics (ISCE)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1-2
- OpenAlex
- W2130329024
- Cited by
- 172
- References
- 0
- Citations by year
- 2026: 4 · 2024: 7 · 2023: 10 · 2022: 14 · 2021: 27 · 2020: 22 · 2019: 28 · 2018: 29 · 2017: 19 · 2016: 11 · 2015: 1
- FWCI
- 14.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Consumer Electronics (ISCE) (publishedVersion)
- HAL (submittedVersion)
- Graduate School and Research Center in Digital Science (EURECOM) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- fog computing, Internet of Things, connected vehicles, roadside units, machine-to-machine communication, semantic data processing, service discovery, quality of service, latency reduction, edge computing, smart grid
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