Results 51 to 60 of about 7,108,814 (163)

IoT Enabled Smart Poultry Farming System With Deep Learning for Chicken Health Detection in Real‐Time

Journal of Sensors, Volume 2026, Issue 1, 2026.
Poultry farming is a critical sector for food security and rural livelihoods, yet traditional practices remain highly labor‐intensive and vulnerable to environmental stressors and disease outbreaks. This project presents the design and implementation of a Smart Poultry Farm system that integrates Internet of Things (IoT)‐based automation with deep ...
Shahbaj Ahmed Sakib, Md Mursalin Hossain Misat, Tanjum Rahi Akanto, Jahedul Islam, Fahmida Ahmed Antara, Deepali   +5 more
wiley   +1 more source

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Bioengineering
Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e ...
Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Jorge Ivan Cuevas-Chávez, Raquel Ochoa-Ornelas, Antonio Navarrete-Guzmán, Carlos Vidrios-Serrano, Daniel Alejandro Sánchez-Arias   +6 more
doaj   +1 more source

A Review of Lightweight Algorithm‐Driven Intelligent Sensing Technologies for Extreme Environments

Journal of Sensors, Volume 2026, Issue 1, 2026.
Extreme environments impose stringent demands on intelligent sensing systems in terms of real‐time performance, energy constraints, data quality, and robustness. Conventional sensing algorithms often fail to satisfy the practical requirements of these scenarios, particularly when resources are limited, under rapidly changing conditions, communication ...
Yu Wang, Shaoming Li, Zhengxu Xiao, Lu Tian, Chunxi Yang, Jian Song, Le Zheng, Lei Zhang, Charles H. Lawrie   +8 more
wiley   +1 more source

Bio‐Nano Sensor‐Centric Lightweight AI for Real‐Time Anomaly Detection Under 6G Edge Architectures

Journal of Sensors, Volume 2026, Issue 1, 2026.
By enabling the detection of molecular, biochemical, and physiological signals at micro‐ and nano‐scales, bio‐nano sensor networks (BNSNs) have emerged as a transformative sensing paradigm for environmental surveillance, biomedical monitoring, and healthcare.
Ayesha Siddika Mourin, Pramita Mishra
wiley   +1 more source

TinyML-Enabled IoT Systems: Recent Advancements in Tiny Machine Learning

The book TinyML-Enabled IoT Systems: Recent Advancement in Tiny Machine Learning presents a comprehensive exploration of Tiny Machine Learning (TinyML), an emerging field that enables machine learning models to run efficiently on ultra-low-power embedded and edge devices.
Haldar, Chandramouli, Biswas, Suparna, Biswas, Srabani, Jain, Dr. Rishi   +3 more
openaire   +2 more sources

A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems

Sci
The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation.
Margarita Terziyska, Iliana Ilieva, Zhelyazko Terziyski, Nikolay Komitov   +3 more
doaj   +1 more source

Energy‐Aware RF Fingerprinting for Device Identification in Ultra‐Low‐Power IoT Systems

Engineering Reports, Volume 7, Issue 7, July 2025.
We propose an energy‐aware RF fingerprinting method for ultra‐low‐power IoT using two deep learning models. RFNet achieves 97.48% accuracy while TinyRFNet reaches 93.19% with far fewer parameters. Dynamic model selection by battery level and confidence yields 94.54% accuracy and 17% energy savings.
Emmanuel Osei Owusu, Danlard Iddrisu, Griffith Selorm Klogo, Kwame Osei Boateng, Emmanuel Kofi Akowuah   +4 more
wiley   +1 more source

TinyFed: Lightweight Federated Learning for Constrained IoT Devices

Internet Technology Letters, Volume 8, Issue 4, July/August 2025.
ABSTRACT TinyML enables machine learning inference on microcontrollers with limited resources. Extending this to a collaborative setting led to Tiny Federated Learning (TinyFL). This article presents TinyFed, a lightweight framework that supports the full federated learning cycle—from local training to model aggregation and redistribution.
Claudio Jr. N. da Silva, Maycon L. M. Peixoto, Gustavo B. Figueiredo, Cassio V. S. Prazeres   +3 more
wiley   +1 more source

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Advanced Functional Materials, Volume 34, Issue 41, October 8, 2024.
This review explores bioelectronic implantable devices, which monitor physiological signals and detect diseases early by interfacing with bodily organs. It highlights their use in neuromodulation for precise therapeutic delivery and discusses closed‐loop systems that adapt treatments from real‐time feedback.
Saehyuck Oh, Janghwan Jekal, Jia Liu, Jeehwan Kim, Jang‐Ung Park, Taeyoon Lee, Kyung‐In Jang   +6 more
wiley   +1 more source

A Novel Active RFID and TinyML based system for livestock Localization in Pakistan

Sir Syed University Research Journal of Engineering and Technology
Localization of livestock is a vital component of good livestock management in Pakistan. This abstract describes a unique method for livestock localization in Pakistan that makes use of Active RFID technology and Tiny Machine Learning (TinyML ...
Syed Atir Raza Shirazi, Maham Fatima, Abdul Wahab, Sadaf Ali   +3 more
doaj   +1 more source