Realising the Power of Edge Intelligence: Addressing the Challenges in AI and tinyML Applications for Edge Computing

Gibbs, Michael; Kanjo, Eiman

doi:10.1109/edge60047.2023.00056

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…However, deploying TinyML on microcontrollers presents certain challenges. These include the selection of appropriate programming languages, limited support for various development boards, often overlooked preprocessing steps, the choice of suitable sensors, and a scarcity of labeled data for training purposes [42]. Overcoming these obstacles is essential for the development of TinyML systems that are both efficient and effective.…”

Section: Introductionmentioning

confidence: 99%

“…Tiny Machine Learning (TinyML) offers significant progress in machine learning, focusing on low-resource embedded devices [43]. Despite its extensive potential for enabling machine learning in compact formats, TinyML encounters several challenges, including the absence of standardized benchmarks, limited development board support, programming language restrictions, preprocessing oversights, sensor selection issues, and a lack of sufficient labeled data [42,44]. In regions like Africa, where the adoption of AI and embedded systems is still in its infancy, TinyML could address issues related to connectivity, energy, and costs [45].…”

Section: Introductionmentioning

confidence: 99%

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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%