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Special Issue "Data Engineering in the Internet of Things"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 20 May 2023 | Viewed by 7140
Special Issue Editors
Interests: multimedia networking; data mining; machine learning; internet of things; computer security
Special Issues, Collections and Topics in MDPI journals
Interests: multimedia communications; multimedia security; embedded systems
Interests: Internet of Thing; mobile application design; artificial intelligence; web technology
Special Issue Information
Recent years have seen explosive and exciting advances in the field of the Internet of Things (IoT) that have enjoyed tremendous success in varieties of applications such as digital health, smart city, environmental monitoring, and predictive maintenance. Real-world applications require sensor data to be timely, reliable and suitable for decision-making. The bounding condition in the IoT system is not going to be the deployment of sensors but rather the data engineering with management and analysis of the data coming off those sensors. With the proliferation of the different forms of data in IoT applications, the need for data engineering techniques can result in-depth processing, analysis, indexing, learning, mining, searching, management, and retrieval of data.
This Special Issue will highlight data engineering techniques that are applied in the design, development and assessment of IoT systems to prepare, transform, publish, or otherwise make available data for different IoT applications. We are receptive to a range of papers suitable to some aspect of IoT data engineering. For sharing and exchanging research and results to problems encountered in today's IoT data engineering practitioners and researchers, we especially encourage submissions that make efforts to
(1) the most recent research results in IoT data engineering;
(2) the most recent practice problems that arise in IoT data engineering;
(3) the exchange of experiences in IoT data engineering technologies;
(4) the new issues and directions for future research and development in IoT data engineering.
Prof. Dr. Ray-I Chang
Prof. Dr. Chia-Hui Wang
Prof. Dr. Yu-Hsin Hung
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. Sensors 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.
- data engineering
- data warehouse and database
- privacy and security
- data processing
- data analysis
- data mining
- data searching
- data management
- data retrieval
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Smart-watch Sensor with Deep Learning to Predict the Purchase Intention of Online Shopper
Authors: Ray-I Chang
Affiliation: Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan