Systematic Review of TinyML at the Edge: Optimization, Applications, and Hardware Ecosystem
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5541Keywords:
Edge AI, Hardware Ecosystems, On-Device Learning, TinyML Applications, TinyML Optimization, TinyMLOpsAbstract
The Internet of Things (IoT) is growing rapidly, making it even more crucial to deploy Machine Learning (ML) models directly on edge devices with limited resources. TinyML fixes this matter by giving microcontroller-class hardware the ability to think for itself. This makes it less reliant on the cloud and better for latency, energy efficiency, and data privacy. This study offers a comprehensive Systematic Literature Review (SLR) of TinyML research published between 2021 and 2025, in accordance with PRISMA principles. We identified 429 records, removed 326 duplicates, and added 83 studies to the final synthesis. The evaluation examines five research inquiries concerning optimization techniques, streamlined architectures, sophisticated learning frameworks, application sectors, and hardware ecosystems. The findings underscore four key themes: enhancing models, utilizing specialized tools and technology, and adapting strategies. Some of the challenges that keep recurring are broken ecosystems, different benchmarking approaches, and on-device learning that isn't compelling when ideas shift. This research presents an open-access taxonomy that categorizes optimization techniques, application trends, and hardware constraints, thereby laying the foundation for a TinyML research agenda within the informatics community. Future directions highlight the importance of adaptive TinyMLOps pipelines, federated learning, LLM-assisted model design, and NVM‑based computing to support scalable and sustainable edge intelligence. The results underscore the relevance of TinyML for advancing informatics and computer science, particularly in enabling secure, efficient, and environmentally aligned IoT systems that support SDG 9 and SDG 12.
Downloads
References
G. Wu, S. Tarkoma, and R. Morabito, “Consolidating TinyML Lifecycle With Large Language Models: Reality, Illusion, or Opportunity?,” IEEE Internet of Things Magazine, vol. 8, no. 5, pp. 88–96, 2025, doi: 10.1109/MIOT.2025.3575927.
V. Tsoukas, A. Gkogkidis, E. Boumpa, and A. Kakarountas, “A Review on the emerging technology of TinyML,” SN Comput Sci, 2024.
M. Altayeb, M. Zennaro, and E. Pietrosemoli, “TinyML Gamma Radiation Classifier,” Nuclear Engineering and Technology, vol. 55, no. 2, pp. 443–451, Feb. 2023, doi: 10.1016/j.net.2022.09.032.
M. A. Hasanpour, M. Kirkegaard, and X. Fafoutis, “EdgeMark: An automation and benchmarking system for embedded artificial intelligence tools,” Journal of Systems Architecture, vol. 167, Oct. 2025, doi: 10.1016/j.sysarc.2025.103488.
Y. I. Alatoom and O. Smadi, “Embedded framework for low-cost pavement condition evaluation using microcontroller and single-board computer platforms,” Autom Constr, vol. 178, Oct. 2025, doi: 10.1016/j.autcon.2025.106442.
R. Kallimani, K. Pai, P. Raghuwanshi, S. Iyer, and O. L. A. López, “TinyML: Tools, applications, challenges, and future research directions,” Multimed Tools Appl, vol. 83, no. 10, pp. 29015–29045, Mar. 2024, doi: 10.1007/s11042-023-16740-9.
T. Malche, S. Budhani, P. K. Soni, and G. M. Upadhyay, “Voice-activated home automation system for IoT edge devices using TinyML,” Discover Internet of Things, vol. 5, no. 1, Dec. 2025, doi: 10.1007/s43926-025-00165-x.
E. Tabanelli, G. Tagliavini, and L. Benini, “DNN Is Not All You Need: Parallelizing Non-neural ML Algorithms on Ultra-low-power IoT Processors,” ACM Transactions on Embedded Computing Systems, vol. 22, no. 3, Apr. 2023, doi: 10.1145/3571133.
R. Sanchez-Iborra, A. Zoubir, A. Hamdouchi, A. Idri, and A. Skarmeta, “Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing,” Informatica (Netherlands), vol. 34, no. 1, pp. 147–168, Jan. 2023, doi: 10.15388/22-INFOR505.
A. Karami and M. Karami, “Edge computing in big data: challenges and benefits,” Nov. 01, 2025, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s41060-025-00855-3.
H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, “Edge Computing with Artificial Intelligence: A Machine Learning Perspective,” ACM Comput Surv, vol. 55, no. 9, Sep. 2023, doi: 10.1145/3555802.
G. Kadve, A. Chowdhury, V. K. Singh, and A. Pal, “Engineering a multi model fallback system for edge devices,” Results in Engineering, vol. 26, 2025, doi: 10.1016/j.rineng.2025.105165.
X. Luo et al., “Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision,” ACM Transactions on Embedded Computing Systems, vol. 24, no. 1, Dec. 2024, doi: 10.1145/3701728.
M. Lin, “Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis,” Internet Technology Letters, vol. 8, no. 4, Jul. 2025, doi: 10.1002/itl2.70054.
B. Arratia, E. Rosas, J. Prades, S. Peña-Haro, J. M. Cecilia, and P. Manzoni, “Towards efficient stream monitoring: A systematic approach for model selection and continuous improvement in Tiny Machine Learning applications,” Eng Appl Artif Intell, vol. 162, Dec. 2025, doi: 10.1016/j.engappai.2025.112415.
N. Alajlan and F. Alotaibi, “Tiny Machine Learning: A Survey of Techniques and Applications,” Micromachines (Basel), vol. 13, no. 11, p. 1789, 2022, doi: 10.3390/mi13111789.
S. Khan, K. Perumal, H. Alsolai, and A. Aljohani, “FedTinyMed: Federated learning enabled tiny multi task machine learning model for smart healthcare monitoring for IoMT,” Computers and Electrical Engineering, vol. 128, Dec. 2025, doi: 10.1016/j.compeleceng.2025.110761.
M. Antonini, M. Pincheira, M. Vecchio, and F. Antonelli, “An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments †,” Sensors, vol. 23, no. 4, Feb. 2023, doi: 10.3390/s23042344.
R. Kallimani, K. Pai, P. Raghuwanshi, S. Iyer, and O. L. A. López, “A Machine-Learning-Oriented Survey on Tiny Machine Learning,” Multimed Tools Appl, vol. 83, pp. 29015–29045, 2024, doi: 10.1007/s11042-024-15678-9.
G. Wu, S. Tarkoma, and R. Morabito, “Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?,” IEEE Internet of Things Magazine, Sep. 2025, doi: 10.1109/MIOT.2025.3575927.
M. Zawish, S. Davy, and L. Abraham, “Complexity-Driven Model Compression for Resource-Constrained Deep Learning on Edge,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 8, pp. 3886–3901, 2024, doi: 10.1109/TAI.2024.3353157.
I. Lamaakal, C. Yahyati, Y. Maleh, K. El Makkaoui, I. Ouahbi, and D. Niyato, “An Explainable Tiny-Fast Kolmogorov–Arnold Network for Gesture-Based Air Handwriting Recognition of Tifinagh Letters in Resource-Constrained IoT Device,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3625087.
E. Njor, M. A. Hasanpour, J. Madsen, and X. Fafoutis, “A Holistic Review of the TinyML Stack for Predictive Maintenance,” 2024, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2024.3512860.
A. Abu-Samah et al., “Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable,” Electronics (Switzerland), vol. 14, no. 4, Feb. 2025, doi: 10.3390/electronics14040687.
S. Sahnoun, M. Mnif, B. Ghoul, M. Jemal, A. Fakhfakh, and O. Kanoun, “Hybrid Solution Through Systematic Electrical Impedance Tomography Data Reduction and CNN Compression for Efficient Hand Gesture Recognition on Resource-Constrained IoT Devices,” Future Internet, vol. 17, no. 2, Feb. 2025, doi: 10.3390/fi17020089.
S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,” IEEE Internet Things J, vol. 7, no. 8, pp. 7457–7469, 2020.
S. El Haddouti and W. Lazraq, “TinyML strategies for privacy-preserving and cyber threat multi-classification in edge-IoT networks,” Computing, vol. 107, no. 8, Aug. 2025, doi: 10.1007/s00607-025-01522-y.
L. Dutta and S. Bharali, “TinyML meets IoT: A comprehensive survey,” Internet of Things, vol. 16, p. 100461, 2021, doi: 10.1016/j.iot.2021.100461.
Z. Huang, K. Zandberg, K. Schleiser, and E. Baccelli, “RIOT-ML: toolkit for over-the-air secure updates and performance evaluation of TinyML models,” Annals of Telecommunications, vol. 80, no. 3, pp. 283–297, 2025, doi: 10.1007/s12243-024-01041-5.
S. A. R. Zaidi, A. M. Hayajneh, M. Hafeez, and Q. Z. Ahmed, “Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML),” IEEE Access, vol. 10, pp. 100867–100877, 2022, doi: 10.1109/ACCESS.2022.3207200.
S. S. Yadav, R. Agarwal, K. Bharath, S. Rao, and C. S. Thakur, “tinyRadar for Fitness: A Contactless Framework for Edge Computing,” IEEE Trans Biomed Circuits Syst, vol. 17, no. 2, pp. 192–201, Apr. 2023, doi: 10.1109/TBCAS.2023.3244240.
O. Abualghanam, H. Alazzam, and W. Almobaideen, “Hierarchical lightweight intrusion detection system using deep learning in the context of IoT,” Cluster Comput, vol. 28, no. 12, Nov. 2025, doi: 10.1007/s10586-025-05364-3.
I. Analytics, “Number of Connected IoT Devices Worldwide (2025),” 2025. [Online]. Available: https://iot-analytics.com/number-connected-iot-devices/?#C1
M. M. H. Shuvo, S. K. Islam, J. Cheng, and B. I. Morshed, “Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review,” Jan. 01, 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/JPROC.2022.3226481.
Y. Harbi, Z. Aliouat, A. Refoufi, and S. Harous, “Recent security trends in internet of things: A comprehensive survey,” 2021, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2021.3103725.
A. Karami and M. Karami, “Edge computing in big data: challenges and benefits,” Nov. 01, 2025, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s41060-025-00855-3.
Y. Abadade, A. Temouden, H. Bamoumen, N. Benamar, Y. Chtouki, and A. S. Hafid, “A Comprehensive Survey on TinyML,” IEEE Access, vol. 11, pp. 96892–96922, 2023, doi: 10.1109/ACCESS.2023.3294111.
S. Somvanshi et al., “From Tiny Machine Learning to Tiny Deep Learning: A Survey,” ACM Comput Surv, Nov. 2025, doi: 10.1145/3776588.
S. Heydari and Q. H. Mahmoud, “Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions,” May 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s25103191.
C. El Zeinaty, W. Hamidouche, G. Herrou, D. Menard, and M. Debbah, “Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?,” Apr. 2025, [Online]. Available: http://arxiv.org/abs/2504.09685
S. Hymel et al., “Edge Impulse: An MLOps Platform for Tiny Machine Learning,” 2023.
I. Lamaakal et al., “A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis,” IEEE Internet Things J, vol. 12, no. 16, pp. 32419–32443, 2025, doi: 10.1109/JIOT.2025.3565688.
L. Capogrosso, F. Cunico, D. S. Cheng, F. Fummi, and M. Cristani, “A Machine Learning-Oriented Survey on Tiny Machine Learning,” IEEE Access, vol. 12, pp. 23406–23426, 2024, doi: 10.1109/ACCESS.2024.3365349.
H. Han and J. Siebert, “TinyML: A Systematic Review and Synthesis of Existing Research,” in 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 269–274. doi: 10.1109/ICAIIC54071.2022.9722636.
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” BMJ, vol. 339, p. b2535, 2009, doi: 10.1136/bmj.b2535.
A. M. Rahmani, A. Haider, P. Khoshvaght, K. Moghaddasi, S. Rajabi, and M. Hosseinzadeh, “Optimizing task offloading with metaheuristic algorithms across cloud, fog, and edge computing networks: A comprehensive survey and state-of-the-art schemes,” Sustainable Computing: Informatics and Systems, vol. 45, p. 101080, Jan. 2025, doi: 10.1016/j.suscom.2024.101080.
R. Ajax, R. Wasiu, and S. Daniel, “Deep Reinforcement Learning for Resource Allocation in Edge-Cloud Environments,” Preprint, 2025, [Online]. Available: https://www.researchgate.net/publication/390009791
A. Mujtaba, W. K. Lee, B. C. Ko, H. J. Chang, and S. O. Hwang, “AuGQ: Augmented quantization granularity to overcome accuracy degradation for sub-byte quantized deep neural networks,” Applied Intelligence, vol. 55, no. 7, May 2025, doi: 10.1007/s10489-025-06495-1.
F. Sakr et al., “CBin-NN: An Inference Engine for Binarized Neural Networks,” Electronics (Switzerland), vol. 13, no. 9, May 2024, doi: 10.3390/electronics13091624.
A. Khatoon, W. Wang, A. Ullah, L. Li, and M. Wang, “Optimized Binary Neural Networks for Road Anomaly Detection: A TinyML Approach on Edge Devices,” Computers, Materials and Continua, vol. 80, no. 1, pp. 527–546, 2024, doi: 10.32604/cmc.2024.051147.
A. Dequino, L. Bompani, L. Benini, and F. Conti, “Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs,” Journal of Low Power Electronics and Applications, vol. 15, no. 1, Mar. 2025, doi: 10.3390/jlpea15010008.
H. Lokhande and S. R. Ganorkar, “Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 1, pp. 357–365, Feb. 2025, doi: 10.11591/eei.v14i1.8131.
A. De Simone, L. Barbisan, G. Turvani, and F. Riente, “Advancing Beekeeping: IoT and TinyML for Queen Bee Monitoring Using Audio Signals,” IEEE Trans Instrum Meas, vol. 73, 2024, doi: 10.1109/TIM.2024.3449981.
A. Krishna et al., “RAMAN: A Reconfigurable and Sparse tinyML Accelerator for Inference on Edge,” IEEE Internet Things J, vol. 11, no. 14, pp. 24831–24845, 2024, doi: 10.1109/JIOT.2024.3386832.
A. Rehman, K. Mahmood, M. Ul Hassan, M. W. Javed, K. Aurangzeb, and M. S. Anwar, “LEMS: Optimized Large Model Framework for Edge-AI in Consumer Internet of Things Devices,” IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 5683–5690, 2025, doi: 10.1109/TCE.2025.3552533.
R. de la Fuente, L. Radrigan, and A. S. Morales, “Enhancing Predictive Maintenance in Mining Mobile Machinery Through a Hierarchical Inference Network,” IEEE Access, vol. 13, pp. 59480–59504, 2025, doi: 10.1109/ACCESS.2025.3557405.
I. Lamaakal, C. Yahyati, Y. Maleh, K. El Makkaoui, I. Ouahbi, and D. Niyato, “An Explainable Tiny-Fast Kolmogorov–Arnold Network for Gesture-Based Air Handwriting Recognition of Tifinagh Letters in Resource-Constrained IoT Device,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3625087.
N. Gaud, M. Rathore, and U. Suman, “MHCNLS-HAR: Multiheaded CNN-LSTM-Based Human Activity Recognition Leveraging a Novel Wearable Edge Device for Elderly Health Care,” IEEE Sens J, vol. 24, no. 21, pp. 35394–35405, 2024, doi: 10.1109/JSEN.2024.3450499.
A. P. Behera et al., “Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical,” IEEE Internet Things J, vol. 12, no. 18, pp. 37456–37470, 2025, doi: 10.1109/JIOT.2025.3583477.
S. Sadiq, J. Hare, S. Craske, P. Maji, and G. Merrett, “Enabling ImageNet-Scale Deep Learning on MCUs for Accurate and Efficient Inference,” IEEE Internet Things J, vol. 11, no. 7, pp. 11471–11479, Apr. 2024, doi: 10.1109/JIOT.2023.3331654.
H. Ren, D. Anicic, X. Li, and T. Runkler, “On-device Online Learning and Semantic Management of TinyML Systems,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 4, Jun. 2024, doi: 10.1145/3665278.
H. Cai et al., “Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications,” ACM Transact Des Autom Electron Syst, vol. 27, no. 3, May 2022, doi: 10.1145/3486618.
A. M. Hayajneh, S. Batayneh, E. Alzoubi, and M. Alwedyan, “TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices,” AgriEngineering, vol. 5, no. 4, pp. 2266–2283, Dec. 2023, doi: 10.3390/agriengineering5040139.
I. Lamaakal et al., “Tiny Deep Learning Models with Hybrid Compression Techniques for Gesture-Based Air Handwriting Recognition of English Alphabets on Edge Device,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3624283.
T. H. Lin, C. T. Chang, and A. Putranto, “Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection,” Eng Appl Artif Intell, vol. 133, Jul. 2024, doi: 10.1016/j.engappai.2024.108618.
R. Kallimani, K. Pai, P. Raghuwanshi, S. Iyer, and O. L. A. López, “TinyML: Tools, applications, challenges, and future research directions,” Multimed Tools Appl, vol. 83, no. 10, pp. 29015–29045, Mar. 2024, doi: 10.1007/s11042-023-16740-9.
A. Pai H, K. K. Mishra, M. T. R, J. V. M. L. Jeyan, and A. Sayal, “Enhanced household energy consumption forecasting using multivariate long short-term memory (LSTM) networks with weather data integration,” Results in Engineering, vol. 27, Sep. 2025, doi: 10.1016/j.rineng.2025.106512.
R. D. A. Fernandes, C. Tavares Da Costa, R. C. S. Gomes, and N. Luniere Vilaça, “SmartLVEnergy: An AIoT Framework for Energy Management Through Distributed Processing and Sensor-Actuator Integration in Legacy Low-Voltage Systems,” IEEE Sens J, vol. 24, no. 13, pp. 20726–20741, Jul. 2024, doi: 10.1109/JSEN.2024.3403484.
S. Iqbal et al., “FusionGCNN: An IoT-Based Novel Spatiotemporal Graph Convolutional Network for ECG Arrhythmia Detection,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3560344.
M. J. C. S. Reis, “Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks,” Sensors, vol. 25, no. 21, Nov. 2025, doi: 10.3390/s25216629.
M. Lin, “Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis,” Internet Technology Letters, vol. 8, no. 4, Jul. 2025, doi: 10.1002/itl2.70054.
W. Villegas-Ch, R. Gutierrez, A. Maldonado Navarro, and A. Mera-Navarrete, “Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks,” IEEE Access, vol. 12, pp. 174354–174370, 2024, doi: 10.1109/ACCESS.2024.3503516.
H. Ren and (et al.), “Unified representation of various knowledge about TinyML models and devices...,” ACM Trans. Embedd. Comput. Syst., vol. 23, no. 4, Jun. 2024.
H. Ren and (et al.), “TinyML Knowledge Graph (ML-KG),” ACM Trans. Embedd. Comput. Syst., vol. 23, no. 4, Jun. 2024, doi: 10.1145/3665278.
M. Hayajneh and (et al.), “Tiny machine learning on the edge: A framework for transfer learning empowered,” IET Smart Cities, vol. 2, no. 1, 2023, doi: 10.1049/smc2.12072.
H. Ren and (et al.), “Unified representation of various knowledge about TinyML models and devices...”.
W. Villegas-Ch, R. Gutierrez, A. Maldonado Navarro, and A. Mera-Navarrete, “Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks,” IEEE Access, vol. 12, pp. 174354–174370, 2024, doi: 10.1109/ACCESS.2024.3503516.
A. Ahmed and O. Hassan, “Gesture Recognition with Jetson Nano using TensorRT Optimization,” Journal of Machine Learning Applications, vol. 18, no. 2, pp. 210–222, 2025.
W. Villegas-Ch, R. Gutierrez, A. Maldonado Navarro, and A. Mera-Navarrete, “Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks,” IEEE Access, vol. 12, pp. 174354–174370, 2024, doi: 10.1109/ACCESS.2024.3503516.
E. A. Anowr, M. Nashaat, M. I. Ismail, M. A. Mohamed, M. M. Fouda, and H. M. Abdel-Atty, “F-IRAN: Performance Analysis of 6G Fog Intelligent Radio Access Network,” IEEE Open Journal of the Communications Society, vol. 6, pp. 7091–7108, 2025, doi: 10.1109/OJCOMS.2025.3604282.
H. Ren, D. Anicic, X. Li, and T. Runkler, “On-device Online Learning and Semantic Management of TinyML Systems,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 4, 2024, doi: 10.1145/3665278.
H. Wang, B. Zhao, X. Liu, R. Pan, S. Pang, and J. Song, “An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks,” Mathematics, vol. 12, no. 21, Nov. 2024, doi: 10.3390/math12213371.
H. Cai et al., “Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications,” ACM Transact Des Autom Electron Syst, vol. 27, no. 3, May 2022, doi: 10.1145/3486618.
A. M. Hayajneh, M. Hafeez, S. A. R. Zaidi, and D. McLernon, “TinyML Empowered Transfer Learning on the Edge,” IEEE Open Journal of the Communications Society, vol. 5, pp. 1656–1672, 2024, doi: 10.1109/OJCOMS.2024.3373177.
S. A. R. Zaidi, A. M. Hayajneh, M. Hafeez, and Q. Z. Ahmed, “Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML),” IEEE Access, vol. 10, pp. 100867–100877, 2022, doi: 10.1109/ACCESS.2022.3207200.
E. Tabanelli, G. Tagliavini, and L. Benini, “Optimizing Random Forest-Based Inference on RISC-V MCUs at the Extreme Edge,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 11, pp. 4516–4526, Nov. 2022, doi: 10.1109/TCAD.2022.3199903.
J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, and S. Han, “Tiny machine learning: Progress and futures [Feature],” IEEE Circuits and Systems Magazine, vol. 23, no. 3, pp. 8–34, 2023.
S. A. R. Zaidi, A. M. Hayajneh, M. Hafeez, and Q. Z. Ahmed, “Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML),” IEEE Access, vol. 10, pp. 100867–100877, 2022, doi: 10.1109/ACCESS.2022.3207200.
X. Ren, W. Li, and Q. Zhang, “Online Learning and Preprocessing Strategies for Edge AI Systems,” Future Generation Computer Systems, vol. 152, pp. 112–125, 2024, doi: 10.1016/j.future.2024.03.012.
H. Ren, D. Anicic, X. Li, and T. Runkler, “On-device Online Learning and Semantic Management of TinyML Systems,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 4, 2024, doi: 10.1145/3665278.
R. Berta, A. Dabbous, L. Lazzaroni, D. Pietro Pau, and F. Bellotti, “Developing a TinyML Image Classifier in an Hour,” IEEE Open Journal of the Industrial Electronics Society, vol. 5, no. August, pp. 946–960, 2024, doi: 10.1109/OJIES.2024.3451959.
R. Kallimani, K. Pai, P. Raghuwanshi, S. Iyer, and O. L. A. López, “TinyML: Tools, applications, challenges, and future research directions,” Multimed Tools Appl, vol. 83, no. 10, pp. 29015–29045, 2024, doi: 10.1007/s11042-023-16740-9.
S. El Haddouti and W. Lazraq, “TinyML strategies for privacy-preserving and cyber threat multi-classification in edge-IoT networks,” Computing, vol. 107, no. 8, Aug. 2025, doi: 10.1007/s00607-025-01522-y.
P. Andrade, I. Silva, M. Diniz, T. Flores, D. G. Costa, and E. Soares, “Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 3, May 2024, doi: 10.1145/3591356.
A. Khatoon, W. Wang, M. Wang, L. Li, and A. Ullah, “TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing,” Sci Rep, vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-01981-5.
P. Andrade, M. Silva, M. Medeiros, D. G. Costa, and I. Silva, “TEDA-RLS: A TinyML Incremental Learning Approach for Outlier Detection and Correction,” IEEE Sens J, vol. 24, no. 22, pp. 38165–38173, 2024, doi: 10.1109/JSEN.2024.3458917.
S. Leroux and P. Simoens, “Sparse random neural networks for online anomaly detection on sensor nodes,” Future Generation Computer Systems, vol. 144, pp. 327–343, Jul. 2023, doi: 10.1016/j.future.2022.12.028.
M. J. C. S. Reis, “Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks,” Sensors, vol. 25, no. 21, Nov. 2025, doi: 10.3390/s25216629.
R. de la Fuente, L. Radrigan, and A. S. Morales, “Enhancing Predictive Maintenance in Mining Mobile Machinery Through a Hierarchical Inference Network,” IEEE Access, vol. 13, pp. 59480–59504, 2025, doi: 10.1109/ACCESS.2025.3557405.
S. Arciniegas, D. Rivero, J. Piñan, E. Diaz, and F. Rivas, “IoT device for detecting abnormal vibrations in motors using TinyML,” Discover Internet of Things, vol. 5, no. 1, 2025, doi: 10.1007/s43926-025-00142-4.
S. El Haddouti and W. Lazraq, “TinyML strategies for privacy-preserving and cyber threat multi-classification in edge-IoT networks,” Computing, vol. 107, no. 8, 2025, doi: 10.1007/s00607-025-01522-y.
A. M. Hayajneh, S. Batayneh, E. Alzoubi, and M. Alwedyan, “TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices,” AgriEngineering, vol. 5, no. 4, pp. 2266–2283, Dec. 2023, doi: 10.3390/agriengineering5040139.
H. Lokhande and S. R. Ganorkar, “Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 1, pp. 357–365, Feb. 2025, doi: 10.11591/eei.v14i1.8131.
F. Sakr et al., “CBin-NN: An Inference Engine for Binarized Neural Networks,” Electronics (Switzerland), vol. 13, no. 9, May 2024, doi: 10.3390/electronics13091624.
I. Lamaakal et al., “Tiny Deep Learning Models with Hybrid Compression Techniques for Gesture-Based Air Handwriting Recognition of English Alphabets on Edge Device,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3624283.
M. Hizem, L. Bousbia, Y. Ben Dhiab, M. O. E. Aoueileyine, and R. Bouallegue, “Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications,” Sensors, vol. 25, no. 8, Apr. 2025, doi: 10.3390/s25082496.
A. Abu-Samah et al., “Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable,” Electronics (Switzerland), vol. 14, no. 4, Feb. 2025, doi: 10.3390/electronics14040687.
S. Dvsr, C. Badachi, C. Nagawaram, P. C. Kondoju, C. Dhanamjayulu, and I. Kamwa, “State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach,” IEEE Access, vol. 13, pp. 106703–106723, 2025, doi: 10.1109/ACCESS.2025.3580552.
H. T. Nguyen, N. D. Mai, B. G. Lee, and W. Y. Chung, “Behind-the-Ear EEG-Based Wearable Driver Drowsiness Detection System Using Embedded Tiny Neural Networks,” IEEE Sens J, vol. 23, no. 19, pp. 23875–23892, Oct. 2023, doi: 10.1109/JSEN.2023.3307766.
R. Srinivasagan, M. Mohammed, and A. Alzahrani, “TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits,” Sensors, vol. 23, no. 16, 2023, doi: 10.3390/s23167081.
S. Zhou, T. Guo, X. Luan, and Y. Li, “Multidimensional Edge Perception Model for Rail Vehicle Operational States Based on Artificial Intelligence of Things,” IEEE Internet Things J, vol. 11, no. 18, pp. 29728–29741, 2024, doi: 10.1109/JIOT.2024.3405356.
E. Tabanelli, G. Tagliavini, and L. Benini, “Optimizing Random Forest-Based Inference on RISC-V MCUs at the Extreme Edge,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 11, pp. 4516–4526, Nov. 2022, doi: 10.1109/TCAD.2022.3199903.
R. H. Jhaveri, H. R. Chi, and H. Wu, “TinyML for Empowering Low-Power IoT Edge Consumer Devices,” IEEE Transactions on Consumer Electronics, vol. 70, no. 4, pp. 7318–7321, 2024, doi: 10.1109/TCE.2024.3482353.
M. Alselek, J. M. Alcaraz-Calero, and Q. Wang, “Dynamic AI-IoT: Enabling Updatable AI Models in Ultralow-Power 5G IoT Devices,” IEEE Internet Things J, vol. 11, no. 8, pp. 14192–14205, Apr. 2024, doi: 10.1109/JIOT.2023.3340858.
R. Berta, A. Dabbous, L. Lazzaroni, D. Pietro Pau, and F. Bellotti, “Developing a TinyML Image Classifier in an Hour,” IEEE Open Journal of the Industrial Electronics Society, vol. 5, pp. 946–960, 2024, doi: 10.1109/OJIES.2024.3451959.
V. E. Baciu, A. Braeken, L. Segers, and B. da Silva, “Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning,” Future Internet, vol. 17, no. 2, Feb. 2025, doi: 10.3390/fi17020085.
S. Sadiq, J. Hare, S. Craske, P. Maji, and G. Merrett, “Enabling ImageNet-Scale Deep Learning on MCUs for Accurate and Efficient Inference,” IEEE Internet Things J, vol. 10, no. 1, pp. 11526–11547, 2023, doi: 10.1109/JIOT.2023.3274567.
M. Antonini, M. Pincheira, M. Vecchio, and F. Antonelli, “An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments †,” Sensors, vol. 23, no. 4, Feb. 2023, doi: 10.3390/s23042344.
E. Tabanelli, G. Tagliavini, and L. Benini, “Optimizing Random Forest-Based Inference on RISC-V MCUs at the Extreme Edge,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 11, pp. 4516–4526, Nov. 2022, doi: 10.1109/TCAD.2022.3199903.
S. Arciniegas, D. Rivero, J. Piñan, E. Diaz, and F. Rivas, “IoT device for detecting abnormal vibrations in motors using TinyML,” Discover Internet of Things, vol. 5, no. 1, Dec. 2025, doi: 10.1007/s43926-025-00142-4.
H. Zhou, X. Zhang, Y. Feng, T. Zhang, and L. Xiong, “Efficient human activity recognition on edge devices using DeepConv LSTM architectures,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-98571-2.
X. Wang, M. Hersche, M. Magno, and L. Benini, “MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain-Machine Interfaces With EEG Channel Selection,” IEEE Sens J, vol. 24, no. 6, pp. 8835–8847, Mar. 2024, doi: 10.1109/JSEN.2024.3353146.
N. D. Mai, Y. A. Nando, and W. Y. Chung, “Wearable Ear-Centric Physiological Sensing with On-Edge Physics-Informed Neural Networks for Negative Mental State Detection,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3609250.
E. Tsakanika, V. Tsoukas, A. Kakarountas, and V. Kokkinos, “High Accuracy of Epileptic Seizure Detection Using Tiny Machine Learning Technology for Implantable Closed-Loop Neurostimulation Systems,” BioMedInformatics, vol. 5, no. 1, Mar. 2025, doi: 10.3390/biomedinformatics5010014.
L. Wulfert et al., “AIfES: A Next-Generation Edge AI Framework,” IEEE Trans Pattern Anal Mach Intell, vol. 46, no. 6, pp. 4519–4533, Jun. 2024, doi: 10.1109/TPAMI.2024.3355495.
U. H. Khan, A. Qamar, R. Khan, F. Alturise, A. R. Alshaabani, and S. Alkhalaf, “Secure edge-based IoMT framework for ICU monitoring with TinyML and post-quantum cryptography,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-20017-6.
A. Albanese, M. Nardello, G. Fiacco, and D. Brunelli, “Tiny Machine Learning for High Accuracy Product Quality Inspection,” IEEE Sens J, vol. 23, no. 2, pp. 1575–1583, Jan. 2023, doi: 10.1109/JSEN.2022.3225227.
M. Piechocki, M. Kraft, T. Pajchrowski, P. Aszkowski, and D. Pieczynski, “Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware,” IEEE Access, vol. 10, pp. 124835–124847, 2022, doi: 10.1109/ACCESS.2022.3225233.
A. Daas, B. Sari, F. Semchedine, and M. Amad, “Optimizing waste management with integrated AIoT, edge computing, and LoRaWAN communication technologies,” Internet of Things (The Netherlands), vol. 31, May 2025, doi: 10.1016/j.iot.2025.101546.
R. Sanchez-Iborra, A. Zoubir, A. Hamdouchi, A. Idri, and A. Skarmeta, “Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing,” Informatica (Netherlands), vol. 34, no. 1, pp. 147–168, Jan. 2023, doi: 10.15388/22-INFOR505.
X. Weng et al., “OdorNet: A lightweight odor recognition method for TinyML in handheld electronic noses using spatiotemporal pseudo-images,” Sens Actuators B Chem, vol. 444, Dec. 2025, doi: 10.1016/j.snb.2025.138393.
R. Srinivasagan, M. Mohammed, and A. Alzahrani, “TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits,” Sensors, vol. 23, no. 16, Aug. 2023, doi: 10.3390/s23167081.
M. Altayeb, M. Zennaro, and E. Pietrosemoli, “TinyML Gamma Radiation Classifier,” Nuclear Engineering and Technology, vol. 55, no. 2, pp. 443–451, Feb. 2023, doi: 10.1016/j.net.2022.09.032.
P. Andrade, I. Silva, M. Diniz, T. Flores, D. G. Costa, and E. Soares, “Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 3, May 2024, doi: 10.1145/3591356.
P. Andrade, M. Silva, M. Medeiros, D. G. Costa, and I. Silva, “TEDA-RLS: A TinyML Incremental Learning Approach for Outlier Detection and Correction,” IEEE Sens J, vol. 24, no. 22, pp. 38165–38173, 2024, doi: 10.1109/JSEN.2024.3458917.
G. Kadve, A. Chowdhury, V. K. Singh, and A. Pal, “Engineering a multi model fallback system for edge devices,” Results in Engineering, vol. 26, Jun. 2025, doi: 10.1016/j.rineng.2025.105165.
B. Sun and Y. Zhao, “TinyNIDS: CNN-Based Network Intrusion Detection System on TinyML Models in 6G Environments,” Internet Technology Letters, vol. 8, no. 6, Nov. 2025, doi: 10.1002/itl2.629.
E. A. Anowr, M. Nashaat, M. I. Ismail, M. A. Mohamed, M. M. Fouda, and H. M. Abdel-Atty, “F-IRAN: Performance Analysis of 6G Fog Intelligent Radio Access Network,” IEEE Open Journal of the Communications Society, vol. 6, pp. 7091–7108, 2025, doi: 10.1109/OJCOMS.2025.3604282.
M. Ficco, A. Guerriero, E. Milite, F. Palmieri, R. Pietrantuono, and S. Russo, “Federated learning for IoT devices: enhancing TinyML with on-board training,” Information Fusion, vol. 104, p. 102189, 2024, doi: 10.1016/j.inffus.2024.102189.
S. Bebortta, S. Sekhar Tripathy, S. Bhatia Khan, M. M. Al Dabel, A. Almusharraf, and A. Kashif Bashir, “TinyDeepUAV: A Tiny Deep Reinforcement Learning Framework for UAV Task Offloading in Edge-Based Consumer Electronics,” IEEE Transactions on Consumer Electronics, vol. 70, no. 4, pp. 7357–7364, 2024, doi: 10.1109/TCE.2024.3445290.
J. Douch and (et al.), “An Ultra-Low Power TinyML System for Real-Time Visual Processing at Edge,” IEEE Transactions on (tidak diketahui), 2023.
B. Arratia, E. Rosas, J. Prades, S. Peña-Haro, J. M. Cecilia, and P. Manzoni, “Towards efficient stream monitoring: A systematic approach for model selection and continuous improvement in Tiny Machine Learning applications,” Eng Appl Artif Intell, vol. 162, p. 112415, Dec. 2025, doi: 10.1016/j.engappai.2025.112415.
D. L. Dutta, S. Bharali, L. Dutta, S. Bharali, D. L. Dutta, and S. Bharali, “TinyML meets IoT: A comprehensive survey,” Internet of Things, vol. 16, p. 100461, Dec. 2021, doi: 10.1016/j.iot.2021.100461.
H. Ren, D. Anicic, X. Li, and T. Runkler, “On-device Online Learning and Semantic Management of TinyML Systems,” vol. 23, no. 4, Jun. 2024, doi: 10.1145/3665278.
M. Sharma and T. Maity, “Smart and Fault-Tolerant Multisensor Fusion Model for UCM Methane Hazard Monitoring Based on Belief Divergence Backed DS Filter and Hybrid CNN-LSTM Classifier,” IEEE Internet Things J, vol. 11, no. 2, pp. 3264–3273, Jan. 2024, doi: 10.1109/JIOT.2023.3295823.
Y. Zhang, H. Liu, X. Chen, and J. Wang, “Low-Power Physiological Fatigue Monitoring via TinyML-Enabled Wearable Devices,” Internet Technology Letters, vol. 8, no. 1, 2025, doi: 10.1002/itl2.1234.
M. Zawish, S. Davy, and L. Abraham, “Complexity-Driven Model Compression for Resource-Constrained Deep Learning on Edge,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 8, pp. 3886–3901, Aug. 2024, doi: 10.1109/TAI.2024.3353157.
E. J. Husom et al., “Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency,” ACM Transactions on Internet of Things, vol. 6, no. 4, p. 3767742, 2025, doi: 10.1145/3767742.
C. El Zeinaty, W. Hamidouche, G. Herrou, and D. Menard, “Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions,” ACM Comput Surv, vol. 58, no. 2, doi: 10.1145/3744339.
M. M. H. Shuvo, S. K. Islam, J. Cheng, and B. I. Morshed, “Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review,” Jan. 01, 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/JPROC.2022.3226481.
M. A. Hasanpour, M. Kirkegaard, and X. Fafoutis, “EdgeMark: An automation and benchmarking system for embedded artificial intelligence tools,” Journal of Systems Architecture, vol. 167, Oct. 2025, doi: 10.1016/j.sysarc.2025.103488.
O. L. A. Arratia, R. Kallimani, K. Pai, P. Raghuwanshi, and S. Iyer, “TinyML Deployment on Microcontrollers: Tools, Applications, and Challenges,” Multimed Tools Appl, vol. 84, pp. 29015–29045, 2025, doi: 10.1007/s11042-024-16740-9.
Y. Abadade, A. Temouden, H. Bamoumen, N. Benamar, Y. Chtouki, and A. S. Hafid, “A Comprehensive Survey on TinyML,” IEEE Access, vol. 11, pp. 96892–96922, 2023, doi: 10.1109/ACCESS.2023.3294111.
A. J. Aparcana-Tasayco, X. Deng, and J. H. Park, “A systematic review of anomaly detection in IoT security: towards quantum machine learning approach,” Dec. 01, 2025, Springer Science and Business Media Deutschland GmbH. doi: 10.1140/epjqt/s40507-025-00414-6.
A. N. M. Rafee, J. Clear, and J. Noor, “Composite human activity recognition utilizing knowledge distillation and sensor fusion focusing on resource constrained microcontrollers,” Expert Syst Appl, vol. 298, Mar. 2026, doi: 10.1016/j.eswa.2025.129652.
V. Tsoukas, A. Gkogkidis, E. Boumpa, and A. Kakarountas, “A Review on the emerging technology of TinyML,” ACM Comput Surv, vol. 56, no. 10, Jun. 2024, doi: 10.1145/3661820.
V. Tsoukas, A. Gkogkidis, E. Boumpa, and A. Kakarountas, “A Review on the emerging technology of TinyML,” ACM Comput Surv, vol. 56, no. 10, Jun. 2024, doi: 10.1145/3661820.
N. Alajlan and F. Alotaibi, “Tiny Machine Learning: A Survey of Techniques and Applications,” Micromachines (Basel), vol. 13, no. 11, p. 1789, 2022, doi: 10.3390/mi13111789.
S. A. R. Zaidi, A. M. Hayajneh, M. Hafeez, Q. Z. Ahmed, Q. Z. Zeeshan Ahmed, and Q. Z. Ahmed, “Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML),” IEEE Access, vol. 10, pp. 100867–100877, 2022, doi: 10.1109/ACCESS.2022.3207200.
J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, and S. Han, “Tiny machine learning: Progress and futures [Feature],” IEEE Circuits and Systems Magazine, vol. 23, no. 3, pp. 8–34, 2023.
X. Luo et al., “Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision,” ACM Transactions on Embedded Computing Systems, vol. 24, no. 1, p. Article 21, Dec. 2024, doi: 10.1145/3701728.
E. Njor, M. A. Hasanpour, J. Madsen, and X. Fafoutis, “A Holistic Review of the TinyML Stack for Predictive Maintenance,” 2024, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2024.3512860.
Z. Huang, K. Zandberg, K. Schleiser, and E. Baccelli, “RIOT-ML: toolkit for over-the-air secure updates and performance evaluation of TinyML models,” vol. 80, no. 3, pp. 283–297, Apr. 2025, doi: 10.1007/s12243-024-01041-5.
L. Wulfert et al., “AIfES: A Next-Generation Edge AI Framework,” IEEE Trans Pattern Anal Mach Intell, vol. 46, no. 6, pp. 4519–4533, Jun. 2024, doi: 10.1109/TPAMI.2024.3355495.
S. Khan, K. Perumal, H. Alsolai, and A. Aljohani, “FedTinyMed: Federated learning enabled tiny multi task machine learning model for smart healthcare monitoring for IoMT,” Computers and Electrical Engineering, vol. 128, p. 110761, Dec. 2025, doi: 10.1016/j.compeleceng.2025.110761.
W. Villegas-Ch, R. Gutierrez, A. Maldonado Navarro, and A. Mera-Navarrete, “Optimizing Federated Learning on TinyML Devices for Privacy Protection and Energy Efficiency in IoT Networks,” IEEE Access, vol. 12, pp. 174354–174370, 2024, doi: 10.1109/ACCESS.2024.3503516.
S. El Haddouti and W. Lazraq, “TinyML strategies for privacy-preserving and cyber threat multi-classification in edge-IoT networks,” Computing, vol. 107, Aug. 2025, doi: 10.1007/s00607-025-01522-y.
Y. Harbi, Z. Aliouat, A. Refoufi, and S. Harous, Recent security trends in internet of things: A comprehensive survey, vol. 9. Institute of Electrical and Electronics Engineers Inc., 2021, pp. 113292–113314. doi: 10.1109/ACCESS.2021.3103725.
V.-E. V. E. Baciu, A. Braeken, L. Segers, B. da Silva, B. D. da Silva, and B. da Silva, “Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning,” Future Internet, vol. 17, no. 2, Feb. 2025, doi: 10.3390/fi17020085.
U. H. Khan, A. Qamar, R. Khan, F. Alturise, A. R. Alshaabani, and S. Alkhalaf, “Secure edge-based IoMT framework for ICU monitoring with TinyML and post-quantum cryptography,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-20017-6.
R. Sanchez-Iborra, A. Zoubir, A. Hamdouchi, A. Idri, and A. Skarmeta, “Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing,” Informatica (Netherlands), vol. 34, no. 1, pp. 147–168, Jan. 2023, doi: 10.15388/22-INFOR505.
Q. Zhou et al., “On-Device Learning Systems for Edge Intelligence: A Software and Hardware Synergy Perspective,” IEEE Internet Things J, vol. 8, no. 15, pp. 11916 – 11934, 2021, doi: 10.1109/JIOT.2021.3063147.
P. Andrade, M. Silva, M. Medeiros, D. G. Costa, and I. Silva, “TEDA-RLS: A TinyML Incremental Learning Approach for Outlier Detection and Correction,” IEEE Sens J, vol. 24, no. 22, pp. 38165–38173, 2024, doi: 10.1109/JSEN.2024.3458917.
S. Hymel et al., “Edge Impulse: An MLOps Platform for Tiny Machine Learning,” 2023.
M. Alselek, J. M. Alcaraz-Calero, and Q. Wang, “Dynamic AI-IoT: Enabling Updatable AI Models in Ultralow-Power 5G IoT Devices,” IEEE Internet Things J, vol. 11, no. 8, pp. 14192–14205, Apr. 2024, doi: 10.1109/JIOT.2023.3340858.
M. Lin, “Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis,” Internet Technology Letters, vol. 8, no. 4, Jul. 2025, doi: 10.1002/itl2.70054.
R. Srinivasagan, M. Mohammed, and A. Alzahrani, “TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits,” Sensors, vol. 23, no. 16, Aug. 2023, doi: 10.3390/s23167081.
S. Arciniegas et al., “IoT device for detecting abnormal vibrations in motors using TinyML,” Discover Internet of Things, vol. 5, no. 1, p. 41, Dec. 2025, doi: 10.1007/s43926-025-00142-4.
A. De Simone, L. Barbisan, G. Turvani, and F. Riente, “Advancing Beekeeping: IoT and TinyML for Queen Bee Monitoring Using Audio Signals,” IEEE Trans Instrum Meas, vol. 73, 2024, doi: 10.1109/TIM.2024.3449981.
A. Abu-Samah et al., “Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable,” Electronics (Switzerland), vol. 14, no. 4, Feb. 2025, doi: 10.3390/electronics14040687.
S. Sreeraj and D. Harikrishnan, “Performance Benchmarking of ML Models for Resource Constrained Devices,” in 2025 5th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/ICAECT63952.2025.10958884.
A. Dequino, L. Bompani, L. Benini, and F. Conti, “Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs,” Journal of Low Power Electronics and Applications, vol. 15, no. 1, Mar. 2025, doi: 10.3390/jlpea15010008.
G. Wu, S. Tarkoma, and R. Morabito, “Consolidating TinyML Lifecycle With Large Language Models: Reality, Illusion, or Opportunity,” IEEE Internet of Things Magazine, vol. 8, no. 5, pp. 88–96, Sep. 2025, doi: 10.1109/MIOT.2025.3575927.
M. Antonini, M. Pincheira, M. Vecchio, and F. Antonelli, “An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments †,” Sensors, vol. 23, no. 4, Feb. 2023, doi: 10.3390/s23042344.
H. Zhou, X. Zhang, Y. Feng, T. Zhang, and L. Xiong, “Efficient human activity recognition on edge devices using DeepConv LSTM architectures,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-98571-2.
M. Hizem, L. Bousbia, Y. Ben Dhiab, M. O. E. M. O.-E. Aoueileyine, and R. Bouallegue, “Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications,” Sensors, vol. 25, no. 8, Apr. 2025, doi: 10.3390/s25082496.
R. de la Fuente, L. Radrigan, and A. S. Morales, “Enhancing Predictive Maintenance in Mining Mobile Machinery Through a Hierarchical Inference Network,” IEEE Access, vol. 13, pp. 59480–59504, 2025, doi: 10.1109/ACCESS.2025.3557405.
N. Gaud, M. Rathore, and U. Suman, “MHCNLS-HAR: Multiheaded CNN-LSTM-Based Human Activity Recognition Leveraging a Novel Wearable Edge Device for Elderly Health Care,” IEEE Sens J, vol. 24, no. 21, pp. 35394–35405, 2024, doi: 10.1109/JSEN.2024.3450499.
S. Leroux and P. Simoens, “Sparse random neural networks for online anomaly detection on sensor nodes,” Future Generation Computer Systems, vol. 144, pp. 327–343, Jul. 2023, doi: 10.1016/j.future.2022.12.028.
E. Tabanelli, G. Tagliavini, and L. Benini, “Optimizing Random Forest-Based Inference on RISC-V MCUs at the Extreme Edge,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 11, pp. 4516–4526, Nov. 2022, doi: 10.1109/TCAD.2022.3199903.
A. Krishna et al., “RAMAN: A Reconfigurable and Sparse tinyML Accelerator for Inference on Edge,” IEEE Internet Things J, vol. 11, no. 14, pp. 24831–24845, 2024, doi: 10.1109/JIOT.2024.3386832.
I. Lamaakal et al., “An Explainable Tiny-Fast Kolmogorov–Arnold Network for Gesture-Based Air Handwriting Recognition of Tifinagh Letters in Resource-Constrained IoT Device,” IEEE Internet Things J, 2025, doi: 10.1109/JIOT.2025.3625087.
R. Kallimani, K. Pai, P. Raghuwanshi, S. Iyer, and O. L. A. López, “A Machine-Learning-Oriented Survey on Tiny Machine Learning,” Multimed Tools Appl, vol. 83, pp. 29015–29045, 2024, doi: 10.1007/s11042-024-15678-9.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Very Kurnia Bakti, Arif Setyanto, Alva Hendi Muhammad, Ferry Wahyu Wibowo
This work is licensed under a Creative Commons Attribution 4.0 International License.
