Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions

Deep-Learning-Based SDN Model for Internet of Things: An Incremental Tensor Train Approach

Singh, A., Aujla, G. S., Garg, S., Kaddoum, G., & Singh, G. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2953537 · Cited by 44

Fog and edge computing: concepts, tools and focus areas

Hurbungs, V., Bassoo, V., & Fowdur, T. P. (2021) · International Journal of Information Technology · doi:10.1007/s41870-020-00588-5 · Cited by 44

Cognitive multi-agent empowering mobile edge computing for resource caching and collaboration

Wang, R., Li, M., Peng, L., Hu, Y., Hassan, M. M., & Alelaiwi, A. A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.001 · Cited by 42

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Wang, F., Fan, X., Wang, F., & Liu, J. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2842733 · Cited by 41

Fog computing and Internet of Things in one building block: a survey and an overview of interacting technologies

Fersi, G. (2021) · Cluster Computing · doi:10.1007/s10586-021-03286-4 · Cited by 41

Data Driven Chiller Sequencing for Reducing HVAC Electricity Consumption in Commercial Buildings

Zheng, Z., Chen, Q., Fan, C., Guan, N., Vishwanath, A., Wang, D., & Liu, F. (2018) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3208903.3208913 · Cited by 41

Edge Computing: Applications, State-of-the-Art and Challenges

Wang, S. (2019) · Advances in Networks · doi:10.11648/j.net.20190701.12 · Cited by 40

Neural Networks Architectures Design, and Applications: A Review

Sadeeq, M. A. M., & Abdulazeez, A. M. (2020) · International Conference on Advanced Science and Engineering (ICOASE) · doi:10.1109/icoase51841.2020.9436582 · Cited by 40

Cloud-based Real-time Network Intrusion Detection Using Deep Learning

Parampottupadam, S., & Moldovan, A.-N. (2018) · International Conference on Cyber Security and Protection of Digital Services (Cyber Security) · doi:10.1109/cybersecpods.2018.8560674 · Cited by 40

Resource Allocation in Combined Fog-Cloud Scenarios by Using Artificial Intelligence

Abedi, M., & Pourkiani, M. (2020) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec49853.2020.9144693 · Cited by 39

Fog Computing Based on Machine Learning: A Review

Samann, F. E. F., Abdulazeez, A. M., & Askar, S. K. (2021) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v15i12.21313 · Cited by 38

Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges

Gill, S. S., & Buyya, R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-818146-1.00001-5 · Cited by 38

Energy-Efficient Resource Allocation and Data Transmission of Cell-Free Internet of Things

Xiu, Z., Qi, H., Zhang, X., & Han, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030675 · Cited by 35

Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments

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Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus

Hallett, N., Yi, K., Dick, J., Hodge, C., Sutton, G. L., Wang, Y. G., & You, J. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206694 · Cited by 35

Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification

Tian, H., Chen, S.-C., & Shyu, M.-L. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10023-6 · Cited by 34

Self-Deployable Indoor Localization With Acoustic-Enabled IoT Devices Exploiting Participatory Sensing

Cai, C., Hu, M., Cao, D., Ma, X., Li, Q., & Liu, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2900524 · Cited by 33

Challenges Facing the Industrial Implementation of Fog Computing

Bouzarkouna, I., Sahnoun, M., Sghaier, N., Baudry, D., & Gout, C. (2018) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2018.00056 · Cited by 33

Comparison of Alternative Architectures in Fog Computing

Karagiannis, V., & Schulte, S. (2020) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec50348.2020.00010 · Cited by 33

An Intrusion Detection Model Based on Multi-scale CNN

Yong, L., & Zhang, B. (2019) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec.2019.8729261 · Cited by 33

Proposal for a Comprehensive (Crypto) Asset Taxonomy

Ankenbrand, T., Bieri, D., Cortivo, R., Hoehener, J., & T, T. H. M. I. (2020) · Crypto Valley Conference on Blockchain Technology (CVCBT) · doi:10.1109/cvcbt50464.2020.00006 · Cited by 33

A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features

Rozenwald, M., Galitsyna, A. A., Sapunov, G. V., Khrameeva, E. E., & Gelfand, M. S. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.307 · Cited by 32

On the Decision Boundary of Deep Neural Networks

Li, Y., Ding, L., & Gao, X. (2018) · arXiv · doi:10.48550/arxiv.1808.05385 · Cited by 31

Sample Subset Optimization for Classifying Imbalanced Biological Data

Yang, P., Zhang, Z., Zhou, B. B., & Zomaya, A. Y. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20847-8_28 · Cited by 31

Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Venčkauskas, A., Štuikys, V., Damaševičius, R., & Jusas, N. (2016) · Security and Communication Networks · doi:10.1002/sec.1537 · Cited by 30

Integration of Color and Texture Features in CBIR System

Atlam, H. F., Attiya, G., & El-Fishawy, N. A. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017913600 · Cited by 30

Learning AWS IoT: Effectively manage connected devices on the AWS cloud using services such as AWS Greengrass, AWS button, predictive analytics and machine learning

Kurniawan, A. (2018) · Cited by 30

Efficient classification model of web news documents using machine learning algorithms for accurate information

Mulahuwaish, A., Gyorick, K., Ghafoor, K. Z., Maghdid, H. S., & Rawat, D. B. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102006 · Cited by 30

2015 National Aerospace and Electronics Conference (NAECON)

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MTFCT: A task offloading approach for fog computing and cloud computing

Jindal, R., Kumar, N., & Nirwan, H. (2020) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence47617.2020.9058209 · Cited by 29

A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings

Vishwanath, A., Chandan, V., Mendoza, C., & Blake, C. (2017) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3077839.3077847 · Cited by 29

A manifesto for modern fog and edge computing: vision, new paradigms, opportunities, and future directions

Gill, S. S. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-74402-1_13 · Cited by 28

Boosting Convolutional Neural Networks Performance Based on FPGA Accelerator

Al-Shamma, O., Fadhel, M. A., Hameed, R. A., Alzubaidi, L., & Zhang, J. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-16657-1_47 · Cited by 27

Research on Intrusion Detection Based on Improved DBN-ELM

Dai, L., & Pan, P. (2019) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce.2019.00115 · Cited by 27

2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)

(2021) · doi:10.1109/icabcd51485.2021 · Cited by 25

Internet of Things: Applications, Adoptions and Components - A Conceptual Overview

Yunana, K., Alfa, A. A., Misra, S., Damaševičius, R., Maskeliūnas, R., & Oluranti, J. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73050-5_50 · Cited by 25

Edge Computing Technology: Development and Countermeasures

Hong, X., & Wang, Y. (2018) · Strategic Study of CAE · doi:10.15302/j-sscae-2018.02.004 · Cited by 24

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts

Carvelli, L., Olesen, A. N., Brink-Kjær, A., Leary, E. B., Peppard, P. E., Mignot, E., Sørensen, H. B. D., & Jennum, P. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2019.12.032 · Cited by 24

Extending the Cloud with Fog: Security Challenges & Opportunities

Shropshire, J. (2014) · Americas Conference on Information Systems · Cited by 24

The 6th International Conference on Advanced Communication Technology

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Integrating machine learning and open data into social Chatbot for filtering information rumor

Hsu, I., & Chang, C.-C. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02119-3 · Cited by 23

Developing a Network Attack Detection System Using Deep Learning

Alsughayyir, B., Qamar, A. M., & Khan, R. (2019) · doi:10.1109/iccisci.2019.8716389 · Cited by 23

Computational Intelligence in Cloud Computing

Labati, R. D., Genovese, A., Piuri, V., Scotti, F., & Vishwakarma, S. (2019) · Topics in intelligent engineering and informatics · doi:10.1007/978-3-030-14350-3_6 · Cited by 23

HLMCC: A Hybrid Learning Anomaly Detection Model for Unlabeled Data in Internet of Things

Alghanmi, N., Alotaibi, R., & Buhari, S. M. (2019) · IEEE Access · doi:10.1109/access.2019.2959739 · Cited by 22

GML learning, a generic machine learning model for network measurements analysis

Casas, P., Vanerio, J., & Fukuda, K. (2017) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm.2017.8255998 · Cited by 22

Review Article on Deep Learning Approaches

Modi, A. S. (2018) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iccons.2018.8663057 · Cited by 22

Do We Really Need Cloud? Estimating the Fog Computing Capacities in the City of Barcelona

García, J., Mezquita, E. S., Masip‐Bruin, X., Marín‐Tordera, E., & Sánchez-López, S. (2018) · IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion) · doi:10.1109/ucc-companion.2018.00070 · Cited by 22

FOGG: A Fog Computing Based Gateway to Integrate Sensor Networks to Internet

Adhatarao, S. S., Arumaithurai, M., & Fu, X. (2017) · International Teletraffic Congress · doi:10.23919/itc.2017.8065709 · Cited by 21

An Edge Based Data-Driven Chiller Sequencing Framework for HVAC Electricity Consumption Reduction in Commercial Buildings

Zheng, Z., Chen, Q., Fan, C., Guan, N., Vishwanath, A., Wang, D., & Liu, F. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2019.2932045 · Cited by 21

Energy and latency reductions at the fog gateway using a machine learning classifier

Suryadevara, N. K. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100582 · Cited by 21

Enhancing Security of Cloud Platform with Cloud Access Security Broker

Ahmad, S., Mehfuz, S., & Beg, J. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0882-7_27 · Cited by 20

Multi-task and multi-view training for end-to-end relation extraction

Zhang, J., Zhang, Y., Ji, D., & Liu, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.087 · Cited by 19

A Time Self-Decay Trust Management Algorithm for P2P Computing Security

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A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

Feng, W., Wu, Y., & Fan, Y. (2018) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-06-2017-0066 · Cited by 18

Deep learning in the fog

Sobecki, A., Szymański, J., Gil, D., & Mora, H. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719867072 · Cited by 18

Fog Computing: Comprehensive Approach for Security Data Theft Attack Using Elliptic Curve Cryptography and Decoy Technology

Dong, M. T., & Zhou, X. (2016) · OALib · doi:10.4236/oalib.1102802 · Cited by 16

2015 International Conference on Platform Technology and Service

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Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

Liu, W., Chen, J., Wang, Y., Gao, P., Lei, Z., & Ma, X. (2020) · Complexity · doi:10.1155/2020/8216874 · Cited by 14

Deep Learning Based Resources Allocation for Internet-of-Things Deployment Underlaying Cellular Networks

ElHalawany, B. M., Wu, K., & Zaky, A. B. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01566-8 · Cited by 14

Predicting the behavior of reinforced concrete columns confined by fiber reinforced polymers using data mining techniques

Ghanem, S. Y., & Elgazzar, H. (2021) · SN Applied Sciences · doi:10.1007/s42452-020-04136-5 · Cited by 14

Anti-steganalysis for image on convolutional neural networks

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Improving threat detection in networks using deep learning

Schuartz, F. C., Fonseca, M., & Munaretto, A. (2020) · Annals of Telecommunications · doi:10.1007/s12243-019-00743-5 · Cited by 13

Point-by-point feature extraction of artificial intelligence images based on the Internet of Things

Mo, C., & Sun, W. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.05.015 · Cited by 13

Digital evidence in fog computing systems

Hegarty, R., & Taylor, M. J. (2021) · Computer law & security review · doi:10.1016/j.clsr.2021.105576 · Cited by 13

Fog Computing, Edge Computing and a return to privacy and personal autonomy

Losavio, M. M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.188 · Cited by 12

Role of Machine Learning in Resource Allocation of Fog Computing

Mehta, S., Singh, A., & Singh, K. K. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377095 · Cited by 12

Intelligent Distributed Computing

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An NF V-powered emergency system for smart enhanced living environments

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A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction

Sharma, K., Alsadoon, A., Prasad, P. W. C., Al-Dala’in, T., Nguyen, T. Q. V., & Pham, D. T. H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105751 · Cited by 11

Operationalizing Multi-Cloud Environments

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Information and Communication Technology for Competitive Strategies (ICTCS 2020)

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Reliable Machine Learning for Networking: Key Issues and Approaches

Hammerschmidt, C., García, S., Verwer, S., & State, R. (2017) · Conference on Local Computer Networks · doi:10.1109/lcn.2017.74 · Cited by 11

Fuzzy TOPSIS-Based Cloud Model to Evaluate Cloud Computing Services

Ahmad, S., Mehfuz, S., & Beg, J. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-981-16-0942-8_4 · Cited by 11

A Phase II nonparametric adaptive exponentially weighted moving average control chart

Zheng, R., & Chakraborti, S. (2016) · Quality Engineering · doi:10.1080/08982112.2016.1183255 · Cited by 9

A Genetic Algorithm based Key Management Approach for Enhancing Data Security in Cloud Environment

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Suitability of Using Fog Computing Alongside Cloud Computing

Ema, R. R., Islam, T., & Ahmed, M. H. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944906 · Cited by 9

AUC-maximized Deep Convolutional Neural Fields for Sequence LabelingPreprint

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2013 Fourth International Conference on e-Learning "Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity"

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Design and Implementation of e-Laboratory for Information Security Training

Ameen, S. Y., & Ahmed, I. M. (2013) · International Conference on e-Learning (econf) · doi:10.1109/econf.2013.39 · Cited by 8

Fuzzy Cloud Access Security Broker for Requirements Negotiation and Prioritization

Ahmad, S., Mehfuz, S., & Beg, J. (2019) · 2019 International Conference on Power Electronics, Control and Automation (ICPECA) · doi:10.1109/icpeca47973.2019.8975620 · Cited by 8

AutoEncoder Based Feature Extraction for Multi-Malicious Traffic Classification

Yeom, S., Choi, C., & Kim, K. (2020) · doi:10.1145/3426020.3426093 · Cited by 7

IEEE Military Communications Conference

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Proceedings of the fifth ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies

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Recent Advances in Intelligent Engineering

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A Massive Multi-Modal Perception Data Classification Method Using Deep Learning Based on Internet of Things

Jiang, L., & Wu, C. (2019) · International Journal of Wireless Information Networks · doi:10.1007/s10776-019-00447-6 · Cited by 5

End-Edge-Cloud Collaborative Computing for Deep Learning: A Comprehensive Survey

Wang, Y., Yang, C., Lan, S., Zhu, L., & Zhang, Y. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3393230 · Cited by 269

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228

Mobile robot localization: Current challenges and future prospective

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Survey: federated learning data security and privacy-preserving in edge-Internet of Things

Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112

A Comprehensive Review of AI Techniques for Resource Management in Fog Computing: Trends, Challenges and Future Directions

Alsadie, D. (2024) · IEEE Access · doi:10.1109/access.2024.3447097 · Cited by 81

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Nancy, A. A., Ravindran, D., Vincent, P. M. D. R., Srinivasan, K., & Hu, Y. (2021) · Sensors · doi:10.3390/s22010196 · Cited by 177

Deep Learning at the Edge

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Foundational Paradigms of Deep Learning-Driven Edge and Cloud Intelligence in IoT Ecosystems

Bhatti, U. A., Ruslan, K., Dadaboev, U., Gulumovna, Y. G., Ruzieva, M., & Ali, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0615-3.ch006

Application of IOT deep learning in edge computing: a review

Li, H. (2021) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2021.040514 · Cited by 3

Scaling Machine Learning at the Edge-Cloud: A Distributed Computing Perspective

Marozzo, F., Orsino, A., Talia, D., & Paolo Trunfio, A. (2023) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot58021.2023.00119 · Cited by 4