Low-cost air, noise, and light pollution measuring station with wireless communication and tinyML

Botero-Valencia, J.S.; Barrantes-Toro, C.; Marquez-Viloria, D.; Pearce, Joshua M.

doi:10.1016/j.ohx.2023.e00477

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The impact of TinyML extends to many areas, including wearable technology [12][13][14][15][16], smart cities [17][18][19][20][21][22], smart homes [23][24][25], smart agriculture [26][27][28][29][30][31], climatic change, environment protection, green AI sustainable applications [32][33][34][35][36][37][38], and automobiles [39,40]. Overcoming TinyML's challenges, especially in hardware, is key and can be advanced through creating an open-source community.…”

Section: Introductionmentioning

confidence: 99%

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

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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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.

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Section: Introductionmentioning

confidence: 99%

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

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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Exploring Local and Cloud-Based Training Use Cases for Embedded Machine Learning

Andalib,

Selimi

2024

2024 13th Mediterranean Conference on Embedded Computing (MECO)

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Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation

Sladojevic,

Arsenovic,

Nikolic

et al. 2024

Journal of Sensors

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The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

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Low-cost air, noise, and light pollution measuring station with wireless communication and tinyML

Cited by 4 publications

References 28 publications

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

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

Exploring Local and Cloud-Based Training Use Cases for Embedded Machine Learning

Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation

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