The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Fog Computing Architecture, Evaluation, and Future Research Directions
Abstract
IoT has been gaining a lot of attention in recent years. In fact, the number of connected devices has already surpassed the total population on Earth. Current developments in various technologies have paved the way for many IoT services that are now being deployed in different sectors. IoT can extend its scope and service provisioning capabilities with the integration of the cloud computing paradigm. Similarly, cloud cannot only access underlying IoT nodes and provide them with cloud services, but can also create further enhanced services based on the data collected from IoT nodes. However, IoT and sensor networks on the ground are often far from the cloud, which is normally accessible via the Internet. Some services require fast response and a great deal of pre-processing and filtering, and may also have security and privacy requirements. For all such cases, middleware, also known as fog, is required between the cloud and IoT devices. We present cloud-IoT integration issues followed by a comparison between fog and cloud computing. We evaluate the performance of fog computing using performance metrics such as processing delay, processing costs, and processing power, and derive the performance gains obtained in comparison to a cloud computing only approach. Finally, we identify some future research directions for fog computing.
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Cites
Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing
Vaquero, L. M., & Rodero-Merino, L. (2014) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2677046.2677052 · Cited by 1,066
Guerrero-ibanez, J. A., Zeadally, S., & Contreras-Castillo, J. (2015) · IEEE Wireless Communications · doi:10.1109/mwc.2015.7368833 · Cited by 517
Self-citationMobile fog: a programming model for large-scale applications on the internet of things
Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013) · Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing · doi:10.1145/2491266.2491270 · Cited by 486
Fog Computing: Focusing on Mobile Users at the Edge
Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., & Sun, L. (2015) · arXiv · doi:10.48550/arxiv.1502.01815 · Cited by 398
Wan, J., Zhang, D., Zhao, S., Yang, L., & Lloret, J. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6871677 · Cited by 369
UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator
Abdelnasser, H., Harras, K. A., & Youssef, M. (2015) · Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing · doi:10.1145/2746285.2755969 · Cited by 267
Self-citationClarifying Fog Computing and Networking: 10 Questions and Answers
Chiang, M., Ha, S., Risso, F., Zhang, T., & Chih-Lin, I. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.7901470 · Cited by 236
Cloud-based smart waste management for smart cities
Aazam, M., St-Hilaire, M., Lung, C.-H., & Lambadaris, I. (2016) · 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad.2016.7790356 · Cited by 163
Self-citationAn open ecosystem for mobile-cloud convergence
Satyanarayanan, M., Schuster, R., Ebling, M. R., Fettweis, G. P., Flinck, H., Joshi, K., & Sabnani, K. K. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7060484 · Cited by 128
Distefano, S., Merlino, G., & Puliafito, A. (2012) · International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) · doi:10.1109/imis.2012.61 · Cited by 92
Cited by
Autonomous Cars: Research Results, Issues, and Future Challenges
Hussain, R., & Zeadally, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2869360 · Cited by 631
Self-citationIoT in smart cities: A survey of technologies, practices and challenges
Syed, A. S., Sierra-Sosa, D., Kumar, A., & Elmaghraby, A. (2021) · Smart Cities · doi:10.3390/smartcities4020024 · Cited by 527
An Empirical Study on System Level Aspects of Internet of Things (IoT)
Swamy, S. N., & Kota, S. R. (2020) · IEEE Access · doi:10.1109/access.2020.3029847 · Cited by 267
Edge computing: current trends, research challenges and future directions
Carvalho, G., Cabral, B., Pereira, V., & Bernardino, J. (2021) · Computing · doi:10.1007/s00607-020-00896-5 · Cited by 223
Secure Healthcare Data Aggregation and Transmission in IoT—A Survey
Ullah, A., Azeem, M., Ashraf, H., Alaboudi, A. A., Humayun, M., & Jhanjhi, N. (2021) · IEEE Access · doi:10.1109/access.2021.3052850 · Cited by 213
Metadata
- Authors
- Aazam, M., Zeadally, S., & Harras, K. A.
- Venue
- IEEE Communications Magazine
- Published
- 2018-05
- Volume
- 56
- Issue
- 5
- Pages
- 46-52
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2804175422
- Cited by
- 253
- References
- 14
- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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