Fog and IoT: An Overview of Research Opportunities
Abstract
Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Platforms such as Smart IoT Gateway have emerged as solutions to communicate with all the heterogeneous IoT sensors potentially deployed in home environments and perform local processing before transmitting the data to the cloud layer.
“Platforms, such as Smart IoT Gateway, have emerged as solutions to communicate with all the heterogeneous IoT sensors potentially deployed in home environments and perform local processing before transmitting the data to the cloud layer84.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [84]
Fog computing offers the advantages of low latency and high computational capacity for IoT applications.
“Fog computing offers the advantages of low latency and high computational capacity for IoT applications [17], [18].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [17]
Cites
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660 · see citing works
Guide to Industrial Control Systems (ICS) Security
Stouffer, K., Pillitteri, V., Lightman, S., Abrams, M., & Hahn, A. (2015) · doi:10.6028/nist.sp.800-82r2 · Cited by 1,377 · see citing works
Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures
Chiang, M., Low, S. H., Calderbank, A. R., & Doyle, J. C. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887322 · Cited by 1,356 · see citing works
Self-citationFog Computing: A Platform for Internet of Things and Analytics
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014) · Studies in Computational Intelligence · doi:10.1007/978-3-319-05029-4_7 · Cited by 1,222 · see citing works
Countdown to Zero Day: Stuxnet and the Launch of the World's First Digital Weapon
Zetter, K. (2014) · Cited by 428 · see citing works
Ha, S., Sen, S., Joe‐Wong, C., Im, Y., & Chiang, M. (2012) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2342356.2342402 · Cited by 222 · see citing works
Self-citationRAT selection games in HetNets
Aryafar, E., Keshavarz‐Haddad, A., Wang, M. S.-Y., et al. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566889 · Cited by 212 · see citing works
Defending Connected Vehicles Against Malware: Challenges and a Solution Framework
Zhang, T., Antunes, H., & Aggarwal, S. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2302386 · Cited by 187 · see citing works
Self-citationDesign of a low-latency, high-reliability wireless communication system for control applications
Weiner, M., Jorgovanovic, M., Sahai, A., & Nikolić, B. (2014) · International Conference on Communications · doi:10.1109/icc.2014.6883918 · Cited by 144 · see citing works
Internet of Things Security Analysis
Gan, G., Lu, Z., & Jiang, J. (2011) · International Conference on Internet Technology and Applications · doi:10.1109/itap.2011.6006307 · Cited by 133 · see citing works
Social trust and social reciprocity based cooperative D2D communications
Chen, X., Proulx, B., Gong, X., & Zhang, J. (2013) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2491288.2491302 · Cited by 115 · see citing works
A minimalist approach to remote attestation
Francillon, A., Nguyen, Q., Rasmussen, K., et al. (2014) · Graduate School and Research Center in Digital Science (EURECOM) · doi:10.5555/2616606.2616905 · Cited by 93 · see citing works
Delgrossi, L., & Zhang, T. (2012) · Wiley eBooks · doi:10.1002/9781118452189 · Cited by 65 · see citing works
Self-citationA minimalist approach to Remote Attestation
Francillon, A., Nguyen, Q., Rasmussen, K., et al. (2014) · Design, Automation, and Test in Europe · doi:10.7873/date.2014.257 · Cited by 63 · see citing works
Coordinating cellular background transfers using loadsense
Chakraborty, A., Navda, V., Padmanabhan, V. N., et al. (2013) · doi:10.1145/2500423.2500447 · Cited by 59 · see citing works
Chung, J. Y., Joe‐Wong, C., Ha, S., et al. (2015) · European Conference on Computer Systems · doi:10.1145/2741948.2741951 · Cited by 38 · see citing works
iBeam: Intelligent client-side multi-user beamforming in wireless networks
Du, Y., Aryafar, E., Camp, J., et al. (2014) · doi:10.1109/infocom.2014.6848009 · Cited by 26 · see citing works
Real-time stream mining: online knowledge extraction using classifier networks
Canzian, L., & van der Schaar, M. (2015) · IEEE Network · doi:10.1109/mnet.2015.7293299 · Cited by 23 · see citing works
Securing Connected Vehicles End to End
Zhang, T., Antunes, H., & Aggarwal, S. (2014) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2014-01-0300 · Cited by 6 · see citing works
Self-citationA TCM-Based Remote Anonymous Attestation Protocol for Power Information System
Chen, R., Wei, L., Zou, H., et al. (2015) · Advances in engineering research/Advances in Engineering Research · doi:10.2991/ipemec-15.2015.134 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607 · see citing works
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2705720 · Cited by 1,775 · see citing works
A Survey on the Edge Computing for the Internet of Things
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520 · see citing works
All one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368 · see citing works
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
Wang, S., Zhang, X., Zhang, Y., Wang, L., YANG, J., & Wang, W. (2017) · IEEE Access · doi:10.1109/access.2017.2685434 · Cited by 952 · see citing works
Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges
Tran, T. X., Hajisami, A., Pandey, P., & Pompili, D. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600863 · Cited by 874 · see citing works
Future Edge Cloud and Edge Computing for Internet of Things Applications
Pan, J., & McElhannon, J. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2767608 · Cited by 868 · see citing works
Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues
Čolaković, A., & Hadžialić, M. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.07.017 · Cited by 825 · see citing works
Bibri, S. E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.12.034 · Cited by 799 · see citing works
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Collaborative Cloud and Edge Computing for Latency Minimization
Ren, J., Yu, G., He, Y., & Li, G. Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2904244 · Cited by 685 · see citing works
A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT
Sharma, P. K., Chen, M.-Y., & Park, J. H. (2018) · IEEE Access · doi:10.1109/access.2017.2757955 · Cited by 676 · see citing works
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890685 · Cited by 560 · see citing works
Security and privacy in fog computing: Challenges
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017) · IEEE Access · doi:10.1109/access.2017.2749422 · Cited by 533 · see citing works
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Xu, J., Chen, L., & Ren, S. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2725277 · Cited by 368 · see citing works
The future of sleep health: a data-driven revolution in sleep science and medicine
Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., Garcia-Gomez, J. M., Taheri, S., Guan, Y., & Fernandez-Luque, L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0244-4 · Cited by 311 · see citing works
Trends in IoT based solutions for health care: Moving AI to the edge
Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.016 · Cited by 318 · see citing works
Bibri, S. E. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0182-7 · Cited by 278 · see citing works
Bibri, S. E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.12.032 · Cited by 273 · see citing works
Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges
Aslam, S., Michaelides, M. P., & Herodotou, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2993411 · Cited by 261 · see citing works
A drone-based networked system and methods for combating coronavirus disease (covid-19) pandemic
Kumar, A., Sharma, K., Singh, H., Naugriya, S. G., Gill, S. S., & Buyya, R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.046 · Cited by 252 · see citing works
Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks
Sharma, S. K., & Wang, X. (2017) · IEEE Access · doi:10.1109/access.2017.2682640 · Cited by 241 · see citing works
Edge computing for Internet of Things: a survey, e-healthcare case study and future direction
Ray, P. P., Dash, D., & De, D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.05.005 · Cited by 241 · see citing works
khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
Sun, Y., Peng, M., & Mao, S. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2871020 · Cited by 219 · see citing works
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8486403 · Cited by 209 · see citing works
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 · see citing works
Bibri, S. E., & Krogstie, J. (2020) · Energy Informatics · doi:10.1186/s42162-020-00130-8 · Cited by 224 · see citing works
Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting
Min, M., Wan, X., Xiao, L., Chen, Y., Xia, M., Wu, D., & Dai, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2875926 · Cited by 213 · see citing works
Singh, A., Kanaujia, A., Singh, V. K., & Vinuesa, R. (2024) · Sustainable Development · doi:10.1002/sd.2706 · Cited by 211 · see citing works
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197 · see citing works
Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management
Li, C., Xue, Y., Wang, J., Zhang, W., & Li, T. (2019) · ACM Computing Surveys · doi:10.1145/3154815 · Cited by 180 · see citing works
Rate and Energy Efficiency Improvements for 5G-Based IoT with Simultaneous Transfer
Liu, X., & Zhang, X. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2863267 · Cited by 176 · see citing works
Efficient Live Migration of Edge Services Leveraging Container Layered Storage
Ma, L., Yi, S., Carter, N., & Li, Q. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2871842 · Cited by 175 · see citing works
Trust management in a blockchain based fog computing platform with trustless smart oracles
Kochovski, P., Gec, S., Stankovski, V., Bajec, M., & Drobintsev, P. D. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.07.030 · Cited by 174 · see citing works
Multi-tier computing networks for intelligent IoT
Yang, Y. (2019) · Nature Electronics · doi:10.1038/s41928-018-0195-9 · Cited by 167 · see citing works
Intelligent Cooperative Edge Computing in Internet of Things
Gong, C., Lin, F., Gong, X., & Lu, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2986015 · Cited by 158 · see citing works
Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing
Lu, X., Liao, Y., Lio, P., & Hui, P. (2020) · IEEE Access · doi:10.1109/access.2020.2978082 · Cited by 159 · see citing works
Fog computing: a taxonomy, systematic review, current trends and research challenges
Singh, J., Singh, P., & Gill, S. S. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.06.005 · Cited by 154 · see citing works
Bandit Convex Optimization for Scalable and Dynamic IoT Management
Chen, T., & Giannakis, G. B. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2839563 · Cited by 139 · see citing works
A Hierarchical Architecture for the Future Internet of Vehicles
Liu, K., Xu, X., Chen, M., Liu, B., Wu, L., & Lee, V. C. S. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800772 · Cited by 133 · see citing works
Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications
Plastiras, G., Terzi, M., Kyrkou, C., & Theocharides, T. (2018) · 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap.2018.8445118 · Cited by 126 · see citing works
CIoT-Net: a scalable cognitive IoT based smart city network architecture
Park, J.-H., Salim, M. M., Jo, J. H., Sicato, J. C. S., Rathore, S., & Park, J. H. (2019) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-019-0190-9 · Cited by 121 · see citing works
IoT reliability: a review leading to 5 key research directions
Moore, S. J., Nugent, C. D., Zhang, S., & Cleland, I. (2020) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-020-00037-z · Cited by 106 · see citing works
Task Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks
Wang, K., Jin, J., Yang, Y., Zhang, T., Nallanathan, A., Tellambura, C., & Jabbari, B. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3227102 · Cited by 95 · see citing works
Self-citationA Detection and Prevention Technique for Man in the Middle Attack in Fog Computing
Aliyu, F., Sheltami, T., & Shakshuki, E. M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.125 · Cited by 79 · see citing works
Access Control Policy Enforcement for Zero-Trust-Networking
Vanickis, R., Jacob, P., Dehghanzadeh, S., & Lee, B. (2018) · 2018 29th Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc.2018.8585365 · Cited by 86 · see citing works
Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge
George, A., Ravindran, A., Mendieta, M., & Tabkhi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3055775 · Cited by 71 · see citing works
“The concept and motivation behind Edge computing are described in a number of recent publications [5]–[9], [29]–[32].”
BackgroundSoftware-Defined Networking Enhanced Edge Computing: A Network-Centric Survey
Wang, A., Zha, Z., Guo, Y., & Chen, S. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2924377 · Cited by 63 · see citing works
Alzoubi, Y. I., Gill, A., & Mishra, A. (2022) · Journal of Cloud Computing · doi:10.1186/s13677-022-00353-y · Cited by 60 · see citing works
A Video Surveillance System Based on Permissioned Blockchains and Edge Computing
Wang, R., Tsai, W.-T., He, J., Liu, C., Li, Q., & Deng, E. (2019) · 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2019.8679354 · Cited by 61 · see citing works
FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things
Lyu, L., Bezdek, J. C., Jin, J., & Yang, Y. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3000374 · Cited by 34 · see citing works
Harnessing Bandit Online Learning to Low-Latency Fog Computing
Chen, T., & Giannakis, G. B. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461641 · Cited by 8 · see citing works
Distributing the Cloud into Fog and Edge: New Weather in IoT Based Deep Learning
Naveen, S., & Kounte, M. R. (2022) · Lecture Notes in Networks and Systems · doi:10.1007/978-981-16-6407-6_64 · Cited by 2 · see citing works
- Authors
- Chiang, M., & Zhang, T.
- Affiliations
- Princeton University (US)
- Cisco Systems (United States) (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2016-12
- Volume
- 3
- Issue
- 6
- Pages
- 854-864
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2472333518
- Cited by
- 2,362
- References
- 20
- Citations by year
- 2026: 120 · 2025: 141 · 2024: 146 · 2023: 195 · 2022: 217 · 2021: 279 · 2020: 378 · 2019: 404 · 2018: 344 · 2017: 135 · 2016: 3
- FWCI
- 277.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- fog computing, Internet of Things, edge computing, 5G wireless systems, embedded AI, fog computing architecture, access networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
