Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Hayajneh, Ali; Aldalahmeh, Sami; Zaidi, Syed Ali Raza; McLernon, Des; Obeidollah, Haitham; Alsakarnah, Rashed

doi:10.1109/menacomm57252.2022.9998267

Cited by 5 publications

(5 citation statements)

References 83 publications

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“…In this scenario, unidirectional LSTM is particularly appealing for deployment on edge devices due to its lower complexity and compatibility with TensorFlow Lite Micro [7]. This renders it an attractive option for practical applications requiring on-device processing and local decision-making capabilities, particularly in remote locations with limited connectivity or where immediate responses are crucial [8]. By evaluating the performance of unidirectional LSTM models for solar power forecasting and edge inference using TinyML, we aim to identify a robust, efficient, and computationally viable solution that can be deployed on resource-constrained devices.…”

Section: A Motivationmentioning

confidence: 99%

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh,

Alasali,

Salama

et al. 2024

IEEE Access

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The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting.INDEX TERMS Solar power forecasting, Time series forecasting, Internet of things, Deep neural networks.

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Section: A Motivationmentioning

confidence: 99%

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh,

Alasali,

Salama

et al. 2024

IEEE Access

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“…This highlights the effectiveness of TL in maintaining high performance with fewer epochs and parameters. Also, it is important to emphasise that the model with the most minor complexity (i.e., Conv1D (20), Conv1D (10), LSTM (12), LSTM (6), and Dense(4)) has the lowest scores in both the initial and TL training phases. This again reinforces the notion that a certain level of model complexity is necessary for optimal performance.…”

Section: B Ml-tl Model Evaluation 1) Tl With Data Augmentationmentioning

confidence: 99%

“…With these developments, a crucial area of interest has formed around the requirement for reliable tools to implement ML models on edge IoT devices. To further optimise the edge-based HAR implementations, light-weight ML solutions are the key and are referred to as Tiny Machine Learning (TinyML) approaches [5], [6]. TinyML specifically addresses space and computational constraints in low-end smart devices and presents a framework of implementing ML solutions for operation on the edge.…”

Section: Introductionmentioning

confidence: 99%

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TinyML Empowered Transfer Learning on the Edge

Hayajneh,

Hafeez,

Zaidi

et al. 2024

IEEE Open J. Commun. Soc.

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Tiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) on resource-limited and low-power internet of things (IoT) edge devices. However, designing efficient TinyML models for these devices remains challenging due to computational resource constraints and the need for customisation to unique use cases. To address this, we propose a novel approach that utilises transfer learning (TL) techniques on edge micro-controller units (MCUs) to accelerate TinyML development. Our strategy involves pre-training generalised ML models on large-scale and varied datasets and fine-tuning them on-device for specific applications using TL. We demonstrate the effectiveness of our approach for HAR by experimenting with a convolutional neural network, long short-term memory TL (CNN-LSTM-TL) model architecture and visualisation techniques like t-distributed stochastic neighbour embedding (t-SNE) for dimensionality reduction. To further validate our model's proficiency and adaptability, we conducted extensive testing using two distinct datasets: MotionSense and UCI. This dual-dataset approach allowed us to assess the robustness of our model across different data domains, showcasing its versatility and effectiveness in various HAR scenarios. Our results show significant model accuracy and reduced training time while maintaining high inference rates and low MCU memory footprint. We also provide insights into best practices for implementing TL on edge MCUs and evaluate classification performance metrics such as accuracy, precision, recall, F1 score, and categorical cross-entropy loss. Our work lays a solid foundation for faster and more efficient TinyML deployments through TL framework across different application domains and types of edge IoT devices.

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“…While the combination of IoT and cloud computing capabilities [7,8] has enabled a plethora of applications (from industrial automation to healthcare), smart agriculture applications are particularly challenging. In particular, energy efficiency for end devices, low latency for actuator control, high data rate for machine vision and privacy for commercially sensitive operations are all key factors [3,9].…”

Section: Introductionmentioning

confidence: 99%

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

Hayajneh,

Aldalahmeh,

Alasali

et al. 2023

IET Smart Cities

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Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assisted smart farming applications. The practical deployment of such a framework on the UAV and bespoke internet of things (IoT) sensors which measure soil moisture and ambient environmental conditions is demonstrated. The key objective of this framework is to harness TinyML for implementing transfer learning (TL) using deep neural networks (DNNs) and long short‐term memory (LSTM) ML models. As a case study, this framework is employed to predict soil moisture content for smart agriculture applications, guiding optimal water utilisation for crops through time‐series forecasting models. To the best of authors’ knowledge, a framework which leverages UAV‐assisted TL for the edge internet of things using TinyML has not been investigated previously. The TL‐based framework employs a pre‐trained data model on different but similar applications and data domains. Not only do the authors demonstrate the practical deployment of the proposed framework but they also quantify its performance through real‐world deployment. This is accomplished by designing a custom sensor board for soil and environmental sensing which uses an ESP32 microcontroller unit. The inference metrics (i.e. inference time and accuracy) are measured for different ML model architectures on edge devices as well as other performance metrics (i.e. mean square error and coefficient of determination [R2]), while emphasising the need for balancing accuracy and processing complexity. In summary, the results show the practical feasibility of using drones to deliver TL for DNN and LSTM models to ultra‐low performance edge IoT devices for soil humidity prediction. But in general, this work also lays the foundation for further research into other applications of TinyML usage in many different aspects of smart farming.

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Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Cited by 5 publications

References 83 publications

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

TinyML Empowered Transfer Learning on the Edge

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

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