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Edge Artificial Intelligence in Future Sustainable Computing Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 17309

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Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
Interests: service computing; IoT security and reliability analysis
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College of Engineering, Virginia Commonwealth University, Richmond, VA E4249, USA
Interests: wireless communication and networking; artificial intelligence; cyber security
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Computing and Mathematic Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: machine learning; deep learning; image processing; information fusion; data analysis; MRI sensors; CT sensors
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Department of Computer Science, University of Missouri–St. Louis, Saint Louis (MO 63121), United States
Interests: data science and cyber security

Special Issue Information

Dear Colleagues,

Living in the era of machines and automated systems, the impact of Artificial Intelligence (AI) in people lives cannot be ignored. In a regular day of a human being starting from morning until night, the role of edge/smart devices play in computing, communicating, entertainment, work, and various other aspects of our lives is enormous. It is not an understatement to say AI has become a part of our life, and we greatly rely on its services. Edge AI is an emerging computing model which allows IoT data management and service supply to be moved from cloud to the local edge devices (IoT-connected devices at the edge) which might grow exponentially into billions of connected devices.

Similarly, sustainable computing has been extended to become a key research area covering the fields of computer science and engineering, electrical engineering, and other engineering disciplines. Recently, we have been witnessing more works being published on sustainable computing that include bio-energy efficiency, natural resources preservation, and emphasize the role of AI in achieving system design and operation objectives. The sustainable bio-energy impact/design of more efficient edge infrastructure is a key challenge for organizations to realize new intelligent computing paradigms. Thus, the uses of edge AI techniques for intelligent decision support being exploited to create effective computing systems.

The data being generated are increasing exponentially every year. This has pushed the capability of machine learning systems to largely extract and learn information from the underlying data. The current expansion of Edge AI demands new computing and networking infrastructure in sustainable environments in industrial systems. Hence, it is becoming challenging for Edge computing to deal with these emerging IoT environments.

Various businesses have started incorporating AI systems in their work. All these together have opened doors to boundless opportunities for innovative research and ideas in the field of Edge computing and AI with sustainable computing. The aim of this Special Issue is to cover all the latest developments and progress made in the field of AI and edge Computing, ranging over a variety of topics of sustainable computing and other related domains. Each contribution should describe in detail the use of artificial intelligence/smart computing/evolutionary computing in the field of edge industrial with sustainable computing and Internet of Things (IoT) application areas.

Prof. Dr. Arun Kumar Sangaiah
Dr. Xi Zheng
Dr. Changqing Luo
Dr. Shuihua Wang
Dr. Ankit Chaudhary
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Edge Computing
  • Artificial Intelligence
  • Sustainable Computing
  • Internet of Things
  • Big Data

Published Papers (5 papers)

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Research

38 pages, 8939 KiB  
Article
A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction
by Stefan Tsokov, Milena Lazarova and Adelina Aleksieva-Petrova
Sustainability 2022, 14(9), 5104; https://doi.org/10.3390/su14095104 - 23 Apr 2022
Cited by 28 | Viewed by 2795
Abstract
Nowadays, air pollution is an important problem with negative impacts on human health and on the environment. The air pollution forecast can provide important information to all affected sides, and allows appropriate measures to be taken. In order to address the problems of [...] Read more.
Nowadays, air pollution is an important problem with negative impacts on human health and on the environment. The air pollution forecast can provide important information to all affected sides, and allows appropriate measures to be taken. In order to address the problems of filling in the missing values in the time series used for air pollution forecasts, the automation of the allocation of optimal subset of input variables, the dependency of the air quality at a particular location on the conditions of the surrounding environment, as well as automation of the model’s optimization, this paper proposes a deep spatiotemporal model based on a 2D convolutional neural network and a long short-term memory network for predicting air pollution. The model utilizes the automatic selection of input variables and the optimization of hyperparameters by a genetic algorithm. A hybrid strategy for missing value imputation is used based on a combination of linear interpolation and a strategy of using the average between the previous value and the average value for the same time in other years. In order to determine the best architecture of the spatiotemporal model, the architecture hyperparameters are optimized by a genetic algorithm with a modified crossover operator for solutions with variable lengths. Additionally, the trained models are included in various ensembles in order to further improve the prediction performance—these include ensembles of models with the same architecture comprising the best architecture obtained by the evolutionary optimization, and ensembles of diverse models comprising the k best models of the evolutionary optimization. The experimental results for the Beijing Multi-Site Air-Quality Data Set show that the proposed spatiotemporal model for air pollution forecasting provides good and consistent prediction results. The comparison of the suggested model with other deep NN models shows satisfactory results, with the best performance according to MAE, based on the experimental results for the station at Wanliu (16.753 ± 0.384). Most of the model architectures obtained by the optimization of the model hyperparameters using the genetic algorithm have one convolutional layer with a small number of kernels and a small kernel size; the convolutional layers are followed by a max-pooling layer, and one or two LSTM layers are utilized with dropout regularization applied to the LSTM layer using small values of p (0.1, 0.2 and 0.3). The utilization of ensembles from the k best trained models further improves the prediction results and surpasses other deep learning models, according to MAE and RMSE metrics. The used hybrid strategy for missing value imputation enhances the results, especially for data with clear seasonality, and produces better MAE compared to the strategy using average values for the same hour of the same day and month in other years. The experimental results also reveal that random searching is a simple and effective strategy for selecting the input variables. Furthermore, the inclusion of spatial information in the model’s input data, based on the local neighborhood data, significantly improves the predictive results obtained with the model. The results obtained demonstrate the benefits of including spatial information from as many surrounding stations as possible, as well as using as much historical information as possible. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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15 pages, 3627 KiB  
Article
Deep Learning-Based Defect Detection for Sustainable Smart Manufacturing
by Sang-Hyun Park, Kang-Hee Lee, Ji-Su Park and Youn-Soon Shin
Sustainability 2022, 14(5), 2697; https://doi.org/10.3390/su14052697 - 25 Feb 2022
Cited by 9 | Viewed by 3788
Abstract
In manufacturing a product, product defects occur at several stages. This study makes the case that one can build a smart factory by introducing it into the manufacturing process of small-scale scarce products, which mainly solves the defect problem through visual inspection. By [...] Read more.
In manufacturing a product, product defects occur at several stages. This study makes the case that one can build a smart factory by introducing it into the manufacturing process of small-scale scarce products, which mainly solves the defect problem through visual inspection. By introducing an intelligent manufacturing process, defects can be minimized, and human costs can be lowered to enable sustainable growth. In this paper, in order to easily detect defects occurring in the manufacturing process, we studied a deep learning-based automatic defect detection model that can train product characteristics and determine defects using open sources. To verify the performance of this model, it was applied to the disposable gas lighter manufacturing process to detect the liquefied gas volume defect of the lighter, and it was confirmed that the detection accuracy and processing time were sufficient to apply to the manufacturing process. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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15 pages, 4329 KiB  
Article
Performance and Efficiency Evaluation of ASR Inference on the Edge
by Santosh Gondi and Vineel Pratap
Sustainability 2021, 13(22), 12392; https://doi.org/10.3390/su132212392 - 10 Nov 2021
Cited by 5 | Viewed by 2509
Abstract
Automatic speech recognition, a process of converting speech signals to text, has improved a great deal in the past decade thanks to the deep learning based systems. With the latest transformer based models, the recognition accuracy measured as word-error-rate (WER), is even below [...] Read more.
Automatic speech recognition, a process of converting speech signals to text, has improved a great deal in the past decade thanks to the deep learning based systems. With the latest transformer based models, the recognition accuracy measured as word-error-rate (WER), is even below the human annotator error (4%). However, most of these advanced models run on big servers with large amounts of memory, CPU/GPU resources and have huge carbon footprint. This server based architecture of ASR is not viable in the long run given the inherent lack of privacy for user data, reliability and latency issues of the network connection. On the other hand, on-device ASR (meaning, speech to text conversion on the edge device itself) solutions will fix deep-rooted privacy issues while at same time being more reliable and performant by avoiding network connectivity to the back-end server. On-device ASR can also lead to a more sustainable solution by considering the energy vs. accuracy trade-off and choosing right model for specific use cases/applications of the product. Hence, in this paper we evaluate energy-accuracy trade-off of ASR with a typical transformer based speech recognition model on an edge device. We have run evaluations on Raspberry Pi with an off-the-shelf USB meter for measuring energy consumption. We conclude that, in the case of CPU based ASR inference, the energy consumption grows exponentially as the word error rate improves linearly. Additionally, based on our experiment we deduce that, with PyTorch mobile optimization and quantization, the typical transformer based ASR on edge performs reasonably well in terms of accuracy and latency and comes close to the accuracy of server based inference. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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12 pages, 541 KiB  
Article
Interoperable Permissioned-Blockchain with Sustainable Performance
by Swathi Punathumkandi, Venkatesan Meenakshi Sundaram and Prabhavathy Panneer
Sustainability 2021, 13(20), 11132; https://doi.org/10.3390/su132011132 - 9 Oct 2021
Cited by 9 | Viewed by 3500
Abstract
Bitcoin is an innovative and path-breaking technology that has influenced numerous industries across the globe. It is a form of digital currency (cryptocurrency) that can be used for trading and has the potential to replace fiat money, where the underlying infrastructure is called [...] Read more.
Bitcoin is an innovative and path-breaking technology that has influenced numerous industries across the globe. It is a form of digital currency (cryptocurrency) that can be used for trading and has the potential to replace fiat money, where the underlying infrastructure is called Blockchain. The Blockchain is an open ledger that provides decentralization, transparency, immutability, and confidentiality. Blockchain can be used in enormous applications, such as healthcare, logistics, supply chain management, the IoT, and so forth. Most of the industrial applications rely on the permissioned blockchain. However, the permissioned blockchain fails in some aspects, such as interoperability among different platforms. This paper suggests a sustainable system to solve the interoperability issue of the permissioned blockchain by designing a new infrastructure. This work has been tested in ethereum and hyperledger frameworks, which obtained a success rate of 100 percent. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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15 pages, 2641 KiB  
Article
A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications
by Li-Wei Chou, Jun-Hong Shen, Hui-Ting Lin, Yi-Tung Yang and Wen-Pin Hu
Sustainability 2021, 13(5), 2934; https://doi.org/10.3390/su13052934 - 8 Mar 2021
Cited by 3 | Viewed by 2783
Abstract
The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, [...] Read more.
The past decade has seen the emergence of numerous new wearable devices, including many that have been widely adopted by both physicians and consumers. In this paper, we discuss the design and application of smart insoles to measure gait and plantar pressure. Herein, we investigate the potential applications of insoles with fewer sensing spots and the consequent reduction in the amount of data acquired from both feet. The main purpose is to discuss the influence of the layout of these pressure sensing points of the insole design on the center of pressure (COP) calculation. The insole used in this study has 89 pressure sensing spots, and we used data from 36, 29, 20, and 11 sensing points in simplified calculation types. Among these four simplified calculation types, Type 1 exhibited the best accuracy of the COP calculation, and Type 4 obtained the worst results. Type 2 and Type 3 exhibited inferior accuracy of the COP calculation, but they still sufficed for applications that did not require high accuracy. Aside from the factor of the number of sensing spots used in the calculation, we also demonstrated that the location of selected sensors could influence the accuracy of COP calculation in the analyses by using the different combinations of metatarsal areas and other areas (heel, central, lateral toes, and hallux). The results of this research could be a reference for making a simplified form of pressure sensing Internet-of-Health Things (IoHT) insole with a reduced product cost. Full article
(This article belongs to the Special Issue Edge Artificial Intelligence in Future Sustainable Computing Systems)
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