ReviewSensors (Basel, Switzerland)2025
Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- Global, Interdisciplinary Research to Guide a Secure and Sustainable Expansion of Mineral Supply Chains.Environmental science & technology letters · 2026Review
- Restoring CFAR Validity for Single-Channel IoT Sensor Streams: A Monte Carlo Comparison of CFAR-Family and Sequential Detectors Under Cortex-M0+ Constraints.Sensors (Basel, Switzerland) · 2026Article
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- Design and Evaluation of a Compact CNN for EMG-Based Wearable Systems Under Embedded Constraints.Sensors (Basel, Switzerland) · 2026Article
- Intrusion Detection in the Internet of Things: A Comprehensive Review of Techniques, Architectures, Datasets, and Emerging Trends.Sensors (Basel, Switzerland) · 2026Review
- From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.Bioengineering (Basel, Switzerland) · 2026Review
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- A lightweight DRR-YOLOv11s model for power equipment failure and personal protective equipment detection in hydropower stations.Scientific reports · 2026Article
- TinyML in Industrial IoT: A Systematic Review of Applications, System Components, and Methodologies.Sensors (Basel, Switzerland) · 2026Review
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- Dynamic community detection using class preserving time series generation with Fourier Markov diffusion.Scientific reports · 2026Article
- A Survey on the Computing Continuum and Meta-Operating Systems: Perspectives, Architectures, Outcomes, and Open Challenges.Sensors (Basel, Switzerland) · 2026Review
- Low-Power Embedded Sensor Node for Real-Time Environmental Monitoring with On-Board Machine-Learning Inference.Sensors (Basel, Switzerland) · 2026Article
- Compact waste image classification with multi-student CNNs and edge-oriented model selection.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- An Advanced CNN-Based Framework for the Automated Detection of Uncovered PET in Recycling Streams.Polymers · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous response times. This review examines Tiny Machine Learning, also known as TinyML, as an alternative to cloud-based inference. The review focuses on applications where transmission delays make traditional Internet of Things (IoT) approaches impractical, thus necessitating a solution that uses TinyML and on-device inference. This study, which follows the PRISMA guidelines, covers TinyML's use cases for real-world applications by analyzing experimental studies and synthesizing current research on the characteristics of TinyML experiments, such as machine learning techniques and the hardware used for experiments. This review identifies existing gaps in research as well as the means to address these gaps. The review findings suggest that TinyML has a strong record of real-world usability and offers advantages over cloud-based inference, particularly in environments with bandwidth constraints and use cases that require rapid response times. This review discusses the implications of TinyML's experimental performance for future research on TinyML applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.