Edge Devices In Natural Resource Management For Real-time Tracking – Numerical analysis of an unstable ternary electrolyte hybrid nanofluid with chemical reaction and activation energy on parallel plates

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Edge Devices In Natural Resource Management For Real-time Tracking

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By Norah N. Alajlan Norah N. Alajlan Scilit Preprints.org Google Scholar 1 and Dina M. Ibrahim Dina M. Ibrahim Scilit Preprints.org Google Scholar 1, 2, *

Received: April 27, 2022 / Revised: May 26, 2022 / Accepted: May 27, 2022 / Published: May 29, 2022

Recently, the Internet of Things (IoT) has received a lot of attention, as IoT devices are located in various fields. Many of these devices rely on machine learning (ML) models to make them intelligent and capable of making decisions. IoT devices typically have limited resources, which limits the execution of complex ML models such as deep learning (DL) on them. In addition, connecting IoT devices to the cloud to transmit and process raw data causes system responses to be delayed, exposes private data, and increases communication costs. Therefore, to deal with these problems, there is a new technology called Tiny Machine Learning (TinyML), which has opened the way to deal with the challenges of IoT devices. This technology allows data to be processed locally on the device without having to send it to the cloud. Additionally, TinyML enables the inference of ML models with respect to DL models on a device such as a microcontroller with limited resources. The purpose of this article is to provide an overview of the TinyML revolution and a review of tinyML studies, where the main contribution is to provide an analysis of the ML models used in tinyML studies; it also presents details of datasets and device types and features to clarify the state of the art and visualize development requirements.

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In the past decade, we have seen the development of machine learning algorithms along with the development of the Internet of Things (IoT) in areas such as microelectronics, communication and information technology. The concept of IoT has been widely used in various aspects of our life with applications and technologies [1, 2], including smart cities, smart environments, smart homes, etc. There are billions of IoT devices connected to the Internet; With a large number of IoT devices comes the mass production of IoT platforms [1, 3]. The role of these devices is to detect the physical functions of the deployment environments – twenty-four hours a day, seven days a week. This leads to an increase in the amount of data generated, which requires high computer performance and storage space. Recently, the integration of Machine Learning (ML) algorithms with IoT devices has aimed to process large amounts of data and make the devices intelligent for decision making [3].

Deep neural networks (DNN) or deep learning (DL) are a subset of ML and account for the most advanced data processing algorithms. In recent years, DL has seen the rapid development of successful applications in various fields, e.g. image classification, object detection and speech recognition. At the same time, DL enables many applications in IoT edge devices, such as mobile phones, which become smart microcontrollers equipped with DL, e.g. Apple Siri to enable human-computer interactions [4]. Efforts have been made to deploy DL in IoT devices due to the benefits of reducing response time delay, broadband connectivity, power consumption, security, and privacy [5]. IoT edge devices can collect data using the device itself, take action and make decisions based on the data capture. Local processing can be performed when a continuously trained DL model capable of inference is added to the device. In addition, some data is sent to micro cloud computing, e.g. perform fog calculation processing before returning the results to the device. However, the inference of DL in edge devices is still not sufficiently prepared to be fully realized [3, 4, 6].

Many challenges of integrating DL devices with devices are listed here. (i) Training DL models is the most difficult challenge for integrating edge devices with DL, where the training DL models consist of dense parameters with high weight for high accuracy. It is computationally expensive and consumes enormous CPU and GPU resources, power, memory and time. However, edge devices have not yet become cost-effective enough to train DL models due to limited resources [7, 8, 9]. For example, in [10], DL training is shown to be the biggest barrier to integrating DL with the industrial internet of things (IIoT), due to the complications of DL models that take time during the training phase. (ii) DL inference in edge devices is a significant challenge; Based on complicated DL models, training inference on DL models takes a long time and can cause delays in response times.

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Currently, DL is partially deployed on the edge devices and the rest of the data is transferred to the cloud for processing, or DL models are deployed on the cloud to process the raw data received from the edge devices, which results in latency delay [9]. For example, Ref. [11] edge devices are used to detect water data and then transmit the data to the cloud for analysis; data can also be predicted using DL models. (iii) Energy and memory consumption are current challenges; Heavy DL models consume a lot of memory and power. Memory size and power capacity are limited in peripheral devices, as these devices have small memory and short power life. Thus, the performance of DL models will be greatly affected compared to servers in data centers or devices with large resources such as energy and memory [12]. (iv) Security poses a challenge to integrate with DL devices. The proliferation of IoT edge devices leads to the collection of sensitive social data, where transferring data to the cloud can expose it to hacking and eavesdropping [13].

A new concept has been created with a meeting and intersection between machine learning (deep learning) and an edge device called TinyML. TinyML makes it possible to deploy small DL models on a small edge device with hard resource constraints, e.g. limited computing (clock speed around tens of megahertz), small memory and few milliwatts (mW) of power. TinyML enables local analysis and interpretation of data on devices and takes action in real time [14]. Furthermore, it is now possible to deploy pre-trained DL models on small edge devices after performing some DL model compression and inference optimization techniques. For example, using quantization techniques, i.e. floating-point numbers are conversion techniques to minimize precision numbers, to reduce the size of the DL model with minimal degradation of accuracy. Pruning techniques allow the removal of redundant network structure and parameters [15, 16]. Figure 1 TinyML allows processing data from different IoT devices locally to small edge devices (such as a microcontroller) without having to connect to the cloud for data processing. But TinyML has many advantages that result in cost savings, energy savings, and better privacy protection. Details about TinyML will be mentioned in the next section.

The main contribution of this paper is to review the emerging topic of TinyML and their techniques to support researchers in this field. The contributions are listed as follows:

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TinyML studies are reviewed in two aspects; The first studies that developed the DL model and applied it to IoT applications. Second, learning how to design frameworks and libraries for TinyML.

Analyzes and results from previous studies are provided for the three main objects (model, dataset, and devices) used in TinyML.

The most important limitation of TinyML is clarified, which will provide guidance for future research.

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The rest of the paper is organized as follows. Section 2 presents an overview of TinyML with highlights of TinyML’s advantages.

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