Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey
Abstract
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.
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Monitoring ML models is challenging but is required in order to detect performance degradations and build confidence in the models.
“Monitoring ML models is challenging [6,28,41], but is required in order to detect performance degradations and build confidence in the models [7,10].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [41]
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Relevance-Aware Anomalous Users Detection in Social Network via Graph Neural Network
Li, Y., Ji, Y., Li, S., He, S., Cao, Y., Liu, Y., Liu, H., Li, X., Shi, J., & Yang, Y. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534136 · Cited by 30 · see citing works
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Su, J., Wen, Z., Lin, T., & Guan, Y. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3517252 · Cited by 30 · see citing works
Self-citationOsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things
Qian, B., Wen, Z., Tang, J., Yuan, Y., Zomaya, A. Y., & Ranjan, R. K. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3193630 · Cited by 30 · see citing works
Self-citationBisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection
Li, T.-T., Hong, Z., Cai, Q., Yu, L., Wen, Z.-Y., & Yang, R.-Y. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3118727 · Cited by 27 · see citing works
Self-citationHTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data
Demirbaga, Ü. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06046-y · Cited by 23 · see citing works
An efficient parallel machine learning-based blockchain framework
Tsai, C., Chen, Y.-P., Tang, T.-C., & Luo, Y. (2021) · ICT Express · doi:10.1016/j.icte.2021.08.014 · Cited by 23 · see citing works
Discriminative learning of imaginary data for few-shot classification
Zhang, X., Zhang, Y., Zhang, Z., & Liu, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.070 · Cited by 22 · see citing works
Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning
Usman, M., Sarfraz, M. S., Habib, U., Aftab, M. U., & Javed, S. (2023) · Sensors · doi:10.3390/s23083931 · Cited by 21 · see citing works
Lopez, M. M., Shao, S., Hariri, S., & Salehi, S. (2023) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3583781.3590271 · Cited by 21 · see citing works
ANN-based predictive mimicker for the constitutive model of engineered cementitious composites (ECC)
Malik, U. J., Khokhar, S. A., Hammad, M., Khushnood, R. A., Najam, F. A., Ali, F. A., & Shahid, M. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.135530 · Cited by 20 · see citing works
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Janus: Latency-Aware Traffic Scheduling for IoT Data Streaming in Edge Environments
Wen, Z., Yang, R., Qian, B., Xuan, Y., Lu, L., Wang, Z., Peng, H., Xu, J., Zomaya, A. Y., & Ranjan, R. K. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3312131 · Cited by 19 · see citing works
Self-citationToward Distributed, Global, Deep Learning Using IoT Devices
Sudharsan, B., Patel, P., Breslin, J. G., Ali, M. I., Mitra, K., Dustdar, S., RANA, O. F., Jayaraman, P. P., & Ranjan, R. K. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3053711 · Cited by 19 · see citing works
Self-citationCapsule networks for segmentation of small intravascular ultrasound image datasets
Bargsten, L., Raschka, S., & Schlaefer, A. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02417-x · Cited by 18 · see citing works
Determining crystallographic orientation via hybrid convolutional neural network
Ding, Z., Zhu, C., & Graef, M. D. (2021) · Materials Characterization · doi:10.1016/j.matchar.2021.111213 · Cited by 17 · see citing works
Gopalakrishna, N. K., Anandayuvaraj, D., Detti, A., Bland, F. L., Rahaman, S., & Davis, J. C. (2022) · doi:10.1145/3528227.3528565 · Cited by 17 · see citing works
Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework
Alwakeel, A. M., & Alnaim, A. K. (2024) · Sensors · doi:10.3390/s24134308 · Cited by 16 · see citing works
Asset logging in the energy sector: a scalable blockchain-based data platform
Djamali, A., Dossow, P., Hinterstocker, M., Schellinger, B., Sedlmeir, J., Völter, F., & Willburger, L. (2021) · Energy Informatics · doi:10.1186/s42162-021-00183-3 · Cited by 16 · see citing works
A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems
Panda, P., Mishra, A. K., & Puthal, D. (2022) · Future Internet · doi:10.3390/fi14080241 · Cited by 16 · see citing works
Self-citationPandey, M., & Kwon, Y. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636033 · Cited by 15 · see citing works
Exploiting predicted answer in label aggregation to make better use of the crowd wisdom
Liu, J., Tang, F., Chen, L., & Zhu, Y. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.060 · Cited by 15 · see citing works
A Scalable Hierarchically Distributed Architecture for Next-Generation Applications
Ravuri, H. K., Vega, M. T., van der Hooft, J., Wauters, T., & Turck, F. D. (2021) · Journal of Network and Systems Management · doi:10.1007/s10922-021-09618-4 · Cited by 15 · see citing works
Generative AI in Agile Software Development: A Comprehensive Survey
Malladi, N. V., & Reddy, K. S. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280486 · Cited by 14 · see citing works
ARTS: A Framework for AI-Rooted IoT System Design Automation
Chakraborty, P., Dizon-Paradis, R. N., & Bhunia, S. (2022) · IEEE Embedded Systems Letters · doi:10.1109/les.2022.3158565 · Cited by 14 · see citing works
An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces
Sharma, S., Gupta, K., Gupta, D., Rani, S., & Dhiman, G. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.029997 · Cited by 15 · see citing works
ZHANG, S.-F., Zhu, D., & Chen, X. (2021) · Chinese Journal of Analytical Chemistry · doi:10.1016/j.cjac.2021.11.008 · Cited by 14 · see citing works
Persistent Service Provisioning Framework for IoMT Based Emergency Mobile Healthcare Units
Ghosh, A., Saha, R., & Misra, S. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172624 · Cited by 14 · see citing works
The Impact of Adversarial Attacks on Interpretable Semantic Segmentation in Cyber–Physical Systems
Gipiškis, R., Chiaro, D., Preziosi, M., Prezioso, E., & Piccialli, F. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3281079 · Cited by 16 · see citing works
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Monteiro, E. S., Righi, R. D. R., Kunst, R., da Costa, C. A., & Singh, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68449-5_31 · Cited by 13 · see citing works
A systematic review on privacy-preserving distributed data mining
Sun, C., Ippel, L., Dekker, A. L. A. J., Dumontier, M., & van Soest, J. (2021) · Data Science · doi:10.3233/ds-210036 · Cited by 12 · see citing works
Blockchain breeding grounds: Asia's advance over the USA and Europe
Denter, N. M. (2021) · World Patent Information · doi:10.1016/j.wpi.2021.102082 · Cited by 13 · see citing works
MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
Chen, R., Wu, T., Zheng, Y., & Ling, M. (2021) · Applied Sciences · doi:10.3390/app12010089 · Cited by 13 · see citing works
A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment
Jha, D. N., Chen, Z., Liu, S., Wu, M., Zhang, J., Morgan, G., Ranjan, R. K., & Li, X. (2022) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2022.3209086 · Cited by 13 · see citing works
Self-citationGroup intrusion detection in the Internet of Things using a hybrid recurrent neural network
Belhadi, A., Djenouri, Y., Djenouri, D., Srivastava, G., & Lin, J. C. (2022) · Cluster Computing · doi:10.1007/s10586-022-03779-w · Cited by 13 · see citing works
Recurrent neural network with density-based clustering for group pattern detection in energy systems
Djenouri, Y., Belhadi, A., & Lin, J. C. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102308 · Cited by 12 · see citing works
High Storable Power Density of Triboelectric Nanogenerator within Centimeter Size
Shang, Y., Li, C., Gao, Y., Yang, Y. O., Zhao, W., & Tang, W. (2023) · Materials · doi:10.3390/ma16134669 · Cited by 12 · see citing works
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Security-Aware computation offloading for Mobile edge computing-Enabled smart city
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Dealing with Multi-Dimensional Data and the Burden of Annotation
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Blessing of dimensionality at the edge and geometry of few-shot learning
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A Fully Open-Source Approach to Intelligent Edge Computing: AGILE’s Lesson
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Caching Techniques in Edge Computing and Challenges
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SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting
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MLOps in Data Science Projects: A Review
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A federated learning attack method based on edge collaboration via cloud
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ROA: A Rapid Learning Scheme for In-Situ Memristor Networks
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Toward Smart Healthcare: Challenges and Opportunities in IoT and ML
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A Survey of Few-Shot Learning for Radio Frequency Fingerprint Identification
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Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage
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Self-citation
- Authors
- Qian, B., Su, J., Wen, Z., Jha, D. N., Li, Y., Guan, Y., Puthal, D., James, P., Yang, R., Zomaya, A. Y., Rana, O., Wang, L., Koutny, M., & Ranjan, R.
- Affiliations
- Newcastle University (GB)
- University of Leeds (GB)
- The University of Sydney (AU)
- Cardiff University (GB)
- China University of Geosciences (CN)
- ORCID
- Venue
- ACM Computing Surveys
- Published
- 2020-08-03
- Volume
- 53
- Issue
- 4
- Pages
- 1-47
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3040838366
- Cited by
- 132
- References
- 167
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 15 · 2023: 12 · 2022: 19 · 2021: 68 · 2020: 6
- FWCI
- 24.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/R013411/1, EP/P016782/1)
- Natural Environment Research Council (NE/P017134/1)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- machine learning, Internet of Things, development life cycle, IoT applications, model training, model deployment, system integration, sensor data
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