ReviewSensors (Basel, Switzerland)2020
Edge Machine Learning for AI-Enabled IoT Devices: A Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.
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
58 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects.Sensors (Basel, Switzerland) · 2023Pooled it
- A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques.Sensors (Basel, Switzerland) · 2022Pooled it
- Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- A Dual-Camera Edge Sensing Framework with Zone-Aware Multi-Object Tracking for Sensorless Smart Vending Cabinets.Sensors (Basel, Switzerland) · 2026Article
- Human Pose Estimation for Real-World Deployment: A Review of Methods, Systems, and Applications.Sensors (Basel, Switzerland) · 2026Review
- Safe, high-performance, moisture-activated batteries for powering next-generation Internet-of-Things devices.Science advances · 2026Article
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- A privacy preserving optimized intelligent security framework for smart homes using zero trust architecture and explainability.Scientific reports · 2026Article
- Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Lightweight convolutional neural network for real-time earthquake P-wave detection on edge devices in New Zealand.Scientific reports · 2026Article
- A lightweight RPB-YOLO11-based detector improves mobile phenotyping of rice panicle blast.Frontiers in plant science · 2026Article
- AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems.Microsystems & nanoengineering · 2025Review
- A Review of Three-Dimensional Electric Field Sensors.Micromachines · 2025Review
- A refined Greylag Goose optimization method for effective IoT service allocation in edge computing systems.Scientific reports · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Real-Time Classification of Ochratoxin a Contamination in Grapes Using AI-Enhanced IoT.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Survey of Deep Learning Approaches in Image Processing.Sensors (Basel, Switzerland) · 2025Review
- Tuning into urban birdsong: enhancing nature connectedness with an AI-powered wearable.Scientific reports · 2025Article
- Hardware-Assisted Low-Latency NPU Virtualization Method for Multi-Sensor AI Systems.Sensors (Basel, Switzerland) · 2024Article
- The analysis of rural tourism image optimization under the internet of things and deep learning.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors' data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning "Hello World".
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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.