Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

Cecilia, José M.; Cano, Juan-Carlos; Morales-García, Juan; Llanes, Antonio; Imbernón, Baldomero

doi:10.3390/s20216335

Cited by 12 publications

(8 citation statements)

References 44 publications

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“…Power consumption during ML forecasts on edge is a critical issue due the limited power availability for IoT nodes. Therefore, power consumption optimization on the edge using ML is investigated in [27][28][29]. Also, in [30] ML is applied on time-series data from IoT sensors in order to predict failure in a slitting machine.…”

Section: Platforms Used On the Edgementioning

confidence: 99%

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Daher

Ferrari²,

Muselli

et al. 2022

Computing

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Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit.

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Section: Platforms Used On the Edgementioning

confidence: 99%

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Daher

Ferrari²,

Muselli

et al. 2022

Computing

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“…From the perspective of mastering the initiative of online public opinion, this paper puts forward that if we want to grasp the law correctly, we need to collect and analyze the online public opinion scientifically to master the initiative of online public opinion, so as to guide the online public opinion correctly. It is believed that the main body of online public opinion is the Internet users, and the correct grasp of the main body characteristics of Internet users can better understand the transmission law, so the study of the main body characteristics of Internet public opinion is completed by analyzing the concept and connotation of Internet users [19,20].…”

Section: Related Workmentioning

confidence: 99%

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Zhu

2021

Wireless Communications and Mobile Computing

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In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

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“…For example, authors in [ 13 ] propose and edge computing framework for collaboration among nodes with the aim to improve resources management and achieve optimal offloading directed towards healthcare systems. Also, energy consumption on the edge and the used of ML to improve its performance is addressed in [ 14 , 15 , 16 ], since energy consumption is essential during ML forecasts due the limited power supplies available for light-weight IoT devices. Furthermore, authors in [ 17 ] apply ML algorithms for an indoor classification applications which uses features collected from radio frequency measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge

Daher

Rizik

Muselli

et al. 2021

Sensors

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Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.

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Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

Cited by 12 publications

References 44 publications

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge

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