Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

Baccarelli, Enzo; Scarpiniti, Michele; Momenzadeh, Alireza; Ahrabi, Sima Sarv

doi:10.1109/access.2021.3058021

Cited by 32 publications

(14 citation statements)

References 44 publications

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“…A second line of future research can be addressed towards to use of Generative Adversarial Networks (GANs) for generating additional examples in the case of new variants of COVID-19, in order to be fast in the automatic discrimination of these scans without awaiting the construction of sufficiently copious dataset. Finally, a third research hint can be focused on the implementation of the proposed methodology in a distributed Cloud/Fog networked technological platforms ( Baccarelli et al, 2021 , Baccarelli et al, 2017 ), in order to produce in fast and reliable clinical responses by exploiting the low-delay and (possibly, adaptive Baccarelli & Cusani, 1996 and/or smart-antenna empowered Baccarelli and Biagi, 2003 , Baccarelli et al, 2007 ) capability of virtualized Fog computing infrastructures in wireless-oriented application environments.…”

Section: Discussionmentioning

confidence: 99%

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Scarpiniti

Ahrabi

Baccarelli

et al. 2022

Expert Systems with Applications

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Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

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Section: Discussionmentioning

confidence: 99%

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Scarpiniti

Ahrabi

Baccarelli

et al. 2022

Expert Systems with Applications

Self Cite

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“…2) is an added layer in modern day storage networks having its tight coupling with cloud computing and IoT. According to the facts mentioned in [41], fog computing is a layered extension to the cloud computing environment combining the traditional coordination and efficiency features of cloud with additional security, reliability, and scalability in communication and data storage. As specified in a famous blog [42], fog computing solves the problem of what data to be processed at the local edge and what data to be sent to the cloud for storage.…”

Section: Fog Computing and Smart Metering Based Architecturesmentioning

confidence: 99%

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

et al. 2021

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Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the "Smart Grid". This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings.INDEX TERMS Energy Management, Smart Buildings and Homes.

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“…A third hint of future research can be addressed towards the use of Varational Autoencoeders (VAEs) and Generative Adversarial Networks (GANs) for generating additional examples in the case of new variants of COVID-19, in order to be fast in the automatic discrimination of these scans without awaiting the construction of sufficiently copious dataset. Finally, a fourth line of future research can be focused on the implementation of the proposed methodology atop distributed Cloud/Fog Computing technological platforms [116,117], in order to produce fast and reliable clinical responses by exploiting the low-delay (and, possibly, multi-antenna empowered [118,119]) capability of the supporting broadband wireless access networks [120].…”

Section: Conclusion and Hints For Future Researchmentioning

confidence: 99%

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

et al. 2021

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The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.

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Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

Cited by 32 publications

References 44 publications

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Energy Management in Smart Buildings and Homes: Current Approaches, a Hypothetical Solution, and Open Issues and Challenges

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

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