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Special Issue "Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Prof. Dr. Giovanni Pau
Website
Guest Editor
Prof. Dr. Hsing-Chung Chen
Website
Guest Editor
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
Interests: blockchain, IoT security, network security, applied cryptography
Special Issues and Collections in MDPI journals
Prof. Dr. Fang-Yie Leu
Website
Guest Editor
Department of Computer Science, Tunghai University, Taichung 40799, Taiwan
Interests: 5G security; IoT security; authentication and authorization
Prof. Dr. Ilsun You
Website1 Website2
Guest Editor
Department of Information Security Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si 31538, Choongchungnam-do, Korea
Interests: 5G security, IoT security, authentication and authorization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The International Symposium on the Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) will be held on 17–19 October 2019 in Taichung, Taiwan (http://isyou.info/conf/mobisec19/). The symposium will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of the advancement of smart applications based on future ICT and its relative security. The symposium will publish high-quality papers that are strictly related to the various theories and practical applications in the area of advanced smart applications based on future ICT and its related communications and networks. Furthermore, we expect that the symposium and its publications will be a trigger for further related research and technology improvements in this subject matter. The conference papers falling in the scope of Sensors at this symposium are invited to submit the extended versions to this Special Issue for publication. Moreover, new papers strictly related to the conference themes are also welcome.

Topics include, but are not limited to, the following:

  • AI IoT (artificial intelligence and Internet of things) and its advanced or future applications
  • Expert systems and their advanced or future applications
  • Knowledge-based systems and their advanced or future applications
  • Smart computing and its advanced or future applications
  • Smart manufacturing and its advanced or future applications
  • Smart e-business and its advanced or future applications
  • Smart healthcare and its advance or future applications
  • Smart living and its advanced or future applications
  • Smart campus and its advanced or future applications
  • Smart city and its advanced or future applications
  • Smart agriculture and its advanced or future applications
  • Smart home and its advanced or future applications
  • Smart education systems and their advanced or future applications
  • Cognitive and biologically inspired solutions for security and cryptography big data and their advanced or future applications
  • Context-aware scheme and its advanced or future applications
  • Smart spaces and intelligent environments and their advanced or future applications
  • Embedded systems and wearable computing and their advanced or future applications
  • Middleware and agent technologies and their advanced or future applications
  • Pervasive and ubiquitous computing and its advanced or future applications
  • Mobile communications and wireless communications and their advance or future technologies
  • Wireless ad-hoc networks and wireless sensor networks and their advanced or future technologies
  • Mobile Internet, mobility management, and their advanced or future applications
  • Wireless sensors, ad-hoc, mesh networks, and their advanced or future applications
  • Networks and interconnection networks for green computing
  • Parallel computing, distributed computing reliability, and fault tolerance
  • Ubiquitous computing applications and their advanced or future applications
  • Security threats, security policies and secure managements, security schemes, secure protocols, cryptography, and their advanced or future applications, e.g., AI applications, big data, blockchain-based technologies and applications
  • Digital forensic, privacy, and trust, and their advanced or future applications, e.g., AI applications, big data, and blockchain-based technologies and their applications
  • Others in advanced or future ICT areas

Prof. Dr. Giovanni Pau
Prof. Dr. Hsing-Chung Chen
Prof. Dr. Fang-Yie Leu
Assoc. Prof. Dr. Ilsun You
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 papers will be 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 2000 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.

Published Papers (6 papers)

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Research

Open AccessArticle
Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
Sensors 2020, 20(10), 2952; https://doi.org/10.3390/s20102952 - 22 May 2020
Abstract
NAND flash memory-based storage devices are vulnerable to errors induced by NAND flash memory cells. Error-correction codes (ECCs) are integrated into the flash memory controller to correct errors in flash memory. However, since ECCs show inherent limits in checking the excessive increase in [...] Read more.
NAND flash memory-based storage devices are vulnerable to errors induced by NAND flash memory cells. Error-correction codes (ECCs) are integrated into the flash memory controller to correct errors in flash memory. However, since ECCs show inherent limits in checking the excessive increase in errors, a complementary method should be considered for the reliability of flash storage devices. In this paper, we propose a scheme based on lossless data compression that enhances the error recovery ability of flash storage devices, which applies to improve recovery capability both of inside and outside the page. Within a page, ECC encoding is realized on compressed data by the adaptive ECC module, which results in a reduced code rate. From the perspective of outside the page, the compressed data are not placed at the beginning of the page, but rather is placed at a specific location within the page, which makes it possible to skip certain pages during the recovery phase. As a result, the proposed scheme improves the uncorrectable bit error rate (UBER) of the legacy system. Full article
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Open AccessArticle
An Efficient Data-Hiding Scheme Based on Multidimensional Mini-SuDoKu
Sensors 2020, 20(9), 2739; https://doi.org/10.3390/s20092739 - 11 May 2020
Abstract
The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major requirements in scaling up the IoT. In [...] Read more.
The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major requirements in scaling up the IoT. In this research, we propose an efficient data-hiding scheme to deal with the security problems and power saving issues of multimedia communication among IoT devises. Data hiding is the practice of hiding secret data into cover images in order to conceal and prevent secret data from being intercepted by malicious attackers. One of the established research streams of data-hiding methods is based on reference matrices (RM). In this study, we propose an efficient data-hiding scheme based on multidimensional mini-SuDoKu RM. The proposed RM possesses high complexity and can effectively improve the security of data hiding. In addition, this study also defines a range locator function which can significantly improve the embedding efficiency of multidimensional RM. Experimental results show that our data-hiding scheme can not only obtain better image quality, but also achieve higher embedding capacity than other related schemes. Full article
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Open AccessArticle
Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection
Sensors 2020, 20(9), 2537; https://doi.org/10.3390/s20092537 - 29 Apr 2020
Abstract
One of the common methods for measuring distance is to use a camera and image processing algorithm, such as an eye and brain. Mechanical stereo vision uses two cameras to shoot the same object and analyzes the disparity of the stereo vision. One [...] Read more.
One of the common methods for measuring distance is to use a camera and image processing algorithm, such as an eye and brain. Mechanical stereo vision uses two cameras to shoot the same object and analyzes the disparity of the stereo vision. One of the most robust methods to calculate disparity is the well-known census transform, which has the problem of conversion window selection. In this paper, three methods are proposed to improve the performance of the census transform. The first one uses a low-pass band of the wavelet to reduce the computation loading and a high-pass band of the wavelet to modify the disparity. The main idea of the second method is the adaptive size selection of the conversion window by edge information. The third proposed method is to apply the adaptive window size to the previous sparse census transform. In the experiments, two indexes, percentage of bad matching pixels (PoBMP) and root mean squared (RMS), are used to evaluate the performance with the known ground truth data. According to the results, the computation required can be reduced by the multiresolution feature of the wavelet transform. The accuracy is also improved with the modified disparity processing. Compared with previous methods, the number of operation points is reduced by the proposed adaptive window size method. Full article
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Open AccessArticle
PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression
Sensors 2020, 20(8), 2423; https://doi.org/10.3390/s20082423 - 24 Apr 2020
Abstract
Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring [...] Read more.
Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper. Full article
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Open AccessArticle
A Durable Hybrid RAM Disk with a Rapid Resilience for Sustainable IoT Devices
Sensors 2020, 20(8), 2159; https://doi.org/10.3390/s20082159 - 11 Apr 2020
Abstract
Flash-based storage is considered to be a de facto storage module for sustainable Internet of things (IoT) platforms under a harsh environment due to its relatively fast speed and operational stability compared to disk storage. Although their performance is considerably faster than disk-based [...] Read more.
Flash-based storage is considered to be a de facto storage module for sustainable Internet of things (IoT) platforms under a harsh environment due to its relatively fast speed and operational stability compared to disk storage. Although their performance is considerably faster than disk-based mechanical storage devices, the read and write latency still could not catch up with that of Random-access memory (RAM). Therefore, RAM could be used as storage devices or systems for time-critical IoT applications. Despite such advantages of RAM, a RAM-based storage system has limitations in its use for sustainable IoT devices due to its nature of volatile storage. As a remedy to this problem, this paper presents a durable hybrid RAM disk enhanced with a new read interface. The proposed durable hybrid RAM disk is designed for sustainable IoT devices that require not only high read/write performance but also data durability. It includes two performance improvement schemes: rapid resilience with a fast initialization and direct byte read (DBR). The rapid resilience with a fast initialization shortens the long booting time required to initialize the durable hybrid RAM disk. The new read interface, DBR, enables the durable hybrid RAM disk to bypass the disk cache, which is an overhead in RAM-based storages. DBR performs byte–range I/O, whereas direct I/O requires block-range I/O; therefore, it provides a more efficient interface than direct I/O. The presented schemes and device were implemented in the Linux kernel. Experimental evaluations were performed using various benchmarks at the block level till the file level. In workloads where reads and writes were mixed, the durable hybrid RAM disk showed 15 times better performance than that of Solid-state drive (SSD) itself. Full article
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Open AccessArticle
The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment
Sensors 2020, 20(5), 1353; https://doi.org/10.3390/s20051353 - 01 Mar 2020
Abstract
Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and [...] Read more.
Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method. Full article
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