Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
IoT Data Processing and Analytics for Computational Sustainability
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

IoT Data Processing and Analytics for Computational Sustainability

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 5724

Special Issue Editors

Dr. Byungchul Tak

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
Interests: distributed systems; cloud computing; container security
Dr. Young-kyoon Suh

E-Mail Website
Guest Editor
Director of Data Engineering and Intelligence Lab (DEAL Lab), School of Computer Science & Engineering, Kyungpook National University, Daegu, Korea
Interests: big data; database performance
Dr. Liqiang Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
Interests: blockchain; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational sustainability is a popular emerging approach to solving various problems in the field of sustainability through state-of-the-art computer science technologies and data analytics. Recent advances in computer science big data processing capabilities allow us to gain clearer insights from various environmental data gathered via IoT devices and make intelligent decisions in real time. We are at the pivotal moment where we can advance our capability to conduct research on economic, environmental, and societal needs through integrating promising innovations from data science and artificial intelligence. It is imperative that we now endeavor to advance into more data-oriented approaches to address sustainability problems better.

This Special Issue covers research and applications related to advancing computational sustainability, ranging from IoT, fog/edge computing, big data analytics for IoT data, the application of deep learning to sustainability, cloud management for IoT data, and algorithmic aspects of efficient sustainability data processing. In addition, innovative H/W design for enabling advanced computational sustainability and computation modeling are within its scope.

All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. We solicit innovative ideas and solutions in all aspects of the IoT data processing and analytics for computational sustainability. Topics of interest include, but are not limited to:

  • IoT for computational sustainability;
  • Cloud infrastructure for computational sustainability;
  • Application of deep learning for supporting IoT data analysis;
  • New paradigm of sustainability data analytics;
  • Real-world sustainability data analysis;
  • Management of computing resources for computational sustainability;
  • Parallel computing in computational sustainability;
  • Monitoring and visualization methodologies and tools in sustainability;
  • Data security for sustainability;
  • Networking issues for collecting and distributing IoT data;
  • Theory and algorithm for computational sustainability.

Dr. Byungchul Tak
Dr. Young-kyoon Suh
Dr. Liqiang Wang
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

  • IoT
  • cloud computing
  • data analytics
  • computational sustainability

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 4493 KiB  
Article
Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data
by Fatima Abdullah, Limei Peng and Byungchul Tak
Sustainability 2020, 12(23), 10175; https://doi.org/10.3390/su122310175 - 5 Dec 2020
Cited by 4 | Viewed by 2318
Abstract
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to [...] Read more.
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption. Full article
(This article belongs to the Special Issue IoT Data Processing and Analytics for Computational Sustainability)
►▼ Show Figures

Figure 1

21 pages, 2457 KiB  
Article
ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data
by Hawon Chu, Jaeseong Kim, Seounghyeon Kim, Young-Kyoon Suh, Ryong Lee, Rae-Young Jang and Minwoo Park
Sustainability 2020, 12(22), 9727; https://doi.org/10.3390/su12229727 - 21 Nov 2020
Cited by 1 | Viewed by 2763
Abstract
Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, [...] Read more.
Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven. Full article
(This article belongs to the Special Issue IoT Data Processing and Analytics for Computational Sustainability)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop