Power management for supply of IoT Systems

Hnatiuc, Bogdan; Paun, Maria‐Alexandra; Sintea, Sorin; Hnatiuc, Mihaela

doi:10.1109/cscc55931.2022.00044

Cited by 2 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the NN classification, we use the target data obtained from the field regarding the degree of attack on the leaves collected in the vegetation period. In the future, we will analyze the data of an IoT sensor system built in the laboratory [ 22 , 27 ]. This system gives more information about the environmental parameters like soil nutrients, wind direction, leaf colors, and so on.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Grapevine Disease Detection Using IoT Sensor Network

Hnatiuc,

Ghita,

Alpetri

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine’s health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The data from the sensors were recorded on an 8 GB external memory card. Two solar panels of 10 W and one solar panel of 20 W were used for powering sensors and charging the energy sources, as the producer proposed [22]. Data were tr mitted to the cloud using MicroGateway RAK7258.…”

Section: Data Acquisition Using Iot Technologymentioning

confidence: 99%

Intelligent Grapevine Disease Detection Using IoT Sensor Network

Hnatiuc,

Ghita,

Alpetri

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Power management also plays a crucial role in IoT systems, particularly those reliant on compact devices and smart networks. This often includes the adoption of low-power communication protocols and the integration of autonomous power systems, which are frequently powered by renewable energy sources [87]. Furthermore, AI-based analytics, processed in the cloud, are increasingly being utilized for healthcare-related data management, such as systems designed for managing diabetic patient data [88].…”

Section: Data Management Techniques Utilizing Tinyml In Iot Systemsmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

View full text Add to dashboard Cite

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

show abstract

Power management for supply of IoT Systems

Cited by 2 publications

References 21 publications

Intelligent Grapevine Disease Detection Using IoT Sensor Network

Intelligent Grapevine Disease Detection Using IoT Sensor Network

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Contact Info

Product

Resources

About