Smart City Gnosys

Smart city article details

Title	Emerging Trends And Strategic Opportunities In Tiny Machine Learning: A Comprehensive Thematic Analysis
ID_Doc	22806
Authors	Velasquez J.D.; Cadavid L.; Franco C.J.
Year	2025
Published	Neurocomputing, 648
DOI	http://dx.doi.org/10.1016/j.neucom.2025.130746
Abstract	This study comprehensively reviews 779 Scopus-indexed documents, critically analyzing the trends, challenges, and opportunities in Tiny Machine Learning (TinyML). Through thematic analysis, 21 key themes are identified, covering areas such as edge computing, deep learning, IoT integration, microcontroller efficiency, and energy utilization. Unlike previous reviews focusing on specific domains or applications, this study adopts a text mining-based thematic approach to identify cross-sector research patterns and uncover underexplored areas. This positions the review as a broad yet deep mapping of the TinyML landscape. By synthesizing insights from the literature, this research highlights strategic opportunities, future directions, and technological advancements necessary to expand the application of TinyML in resource-constrained environments, neural networks, and real-time systems. The review also identifies key challenges such as balancing accuracy and energy efficiency in low-power devices, optimizing on-device learning, and ensuring data privacy without cloud dependency. In doing so, it outlines actionable directions for future research, including scalable deployment in large-scale IoT systems and application expansion into areas such as UAV security and smart cities. The findings are crucial for advancing AI applications in low-power, embedded systems and contribute to the growing body of knowledge on TinyML. © 2025 The Authors
Author Keywords	Deep learning; Edge computing; Internet of Things (IoT); Microcontroller efficiency; Neural networks; Tiny Machine Learning (TinyML)

Similar Articles

Id	Similarity	Authors	Title	Published
2486	0.963	Capogrosso L.; Cunico F.; Cheng D.S.; Fummi F.; Cristani M.	A Machine Learning-Oriented Survey On Tiny Machine Learning	IEEE Access, 12 (2024)
57463	0.944	Alajlan N.N.; Ibrahim D.M.	Tinyml: Adopting Tiny Machine Learning In Smart Cities	Journal of Autonomous Intelligence, 7, 4 (2024)
6706	0.919	Pazmiño Ortiz L.A.; Maldonado Soliz I.F.; Guevara Balarezo V.K.	Advancing Tinyml In Iot: A Holistic System-Level Perspective For Resource-Constrained Ai	Future Internet, 17, 6 (2025)
57460	0.907	Saeed M.M.; Saeed R.A.; Ahmed Z.E.	Tinyml For 5G Networks	TinyML for Edge Intelligence in IoT and LPWAN Networks (2024)
26328	0.902	Ramadan M.N.A.; Ali M.A.H.; Khoo S.Y.; Alkhedher M.	Federated Learning And Tinyml On Iot Edge Devices: Challenges, Advances, And Future Directions	ICT Express (2025)
57461	0.898	Ahmed Z.E.; Hashim A.A.; Saeed R.A.; Saeed M.M.	Tinyml Network Applications For Smart Cities	TinyML for Edge Intelligence in IoT and LPWAN Networks (2024)
14842	0.888	Grzesik P.; Mrozek D.	Combining Machine Learning And Edge Computing: Opportunities, Challenges, Platforms, Frameworks, And Use Cases	Electronics (Switzerland), 13, 3 (2024)
36075	0.884	Bzai J.; Alam F.; Dhafer A.; Bojović M.; Altowaijri S.M.; Niazi I.K.; Mehmood R.	Machine Learning-Enabled Internet Of Things (Iot): Data, Applications, And Industry Perspective	Electronics (Switzerland), 11, 17 (2022)
6522	0.883	Dhanalakshmi M.; Nand Kishor Kumar G.; Himabindu G.; Gosavi V.R.; Sundar Ganesh C.S.; Premanand R.	Advanced Machine Learning Innovations In Embedded Systems And Narrowband Internet Of Things (Nb-Iot) Devices	Integrating Artificial Intelligence Into the Energy Sector (2025)
8526	0.875	Zacharia A.; Zacharia D.; Karras A.; Karras C.; Giannoukou I.; Giotopoulos K.C.; Sioutas S.	An Intelligent Microprocessor Integrating Tinyml In Smart Hotels For Rapid Accident Prevention	7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022 (2022)