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Special Issue "Selected Papers from TENCON 2018"

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 6707

Special Issue Editors

Prof. Dr. Kukjin Chun
E-Mail Website
Guest Editor
Seoul National University, Korea
Interests: sensor; MEMS
Prof. Dr. Changhyun Kim
E-Mail Website
Guest Editor
Research Professor, Dept. of Creative IT Engineering, POSTECH, Korea
Interests: passive/active neural probe design and fabrication for bio-medical applications; high speed memory design; architecture and process technology; new emerging memory development; product planning of memory; electronic components and connectivity modules
Prof. Dr. Jong Chang Yi
E-Mail Website
Guest Editor
Hongik University, Korea
Interests: quantum and optical electronics
Prof. Dr. Cheon Won Choi
E-Mail Website
Guest Editor
Dankook University, Korea
Interests: ad hoc sensor networks; energy harvesting sensor networks; medium access control; queueing theory; game theory

Special Issue Information

Dear Colleagues,

TENCON is a premier conference of IEEE Region 10. IEEE TENCON 2018 will be held at Jeju Island, Korea, from 28–31 October, 2018 (http://tencon2018.org/). Traditionally, TENCONs have encompassed all areas in electrical and electronics engineering, as well as information technologies. Recently, advances in sensor technologies have had a remarkable influence on TENCON; sensors and their applications now stand out as notable topics in every area of TENCON, which includes materials, devices, communications, signal processing, and computing. While following the tradition of previous TENCONs, TENCON 2018 will provide a place to deeply discuss the infrastructure that leads to “smartness everywhere”. In such a place, we expect that highly valuable research works will be presented on sensors, sensor networks, and their applications.

This Special Issue selects excellent papers from IEEE TENCON 2018 and covers sensors, sensor networks and their applications, e.g., IoT. Authors of outstanding papers at TENCON 2018, which are related to sensors, sensor networks and their applications, will be invited to submit extended versions of their works to this Special Issue for publication.

Potential topics include, but are not limited to

  • IoT
  • IoT based smart cities
  • Ad hoc sensor networks
  • Energy harvesting sensor networks
  • Sensor device structure
  • Advanced sensor materials

Prof. Dr. Kukjin Chun
Prof. Dr. Changhyun Kim
Prof. Dr. Jong Chang Yi
Prof. Dr. Cheon Won Choi
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. 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.

Published Papers (4 papers)

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Research

Article
Self-Adaptive Framework Based on MAPE Loop for Internet of Things
Sensors 2019, 19(13), 2996; https://doi.org/10.3390/s19132996 - 07 Jul 2019
Cited by 9 | Viewed by 1631
Abstract
The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its [...] Read more.
The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework. Full article
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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Article
Radio Resource Allocation with The Fairness Metric for Low Density Signature OFDM in Underlay Cognitive Radio Networks
Sensors 2019, 19(8), 1921; https://doi.org/10.3390/s19081921 - 23 Apr 2019
Cited by 5 | Viewed by 1459
Abstract
Low density signature orthogonal frequency division multiplexing (LDS-OFDM), one type of non-orthogonal multiple access (NOMA), is a special case of multi-carrier code division multiple access (MC-CDMA). In LDS-OFDM, each user is allowed to spread its symbols in a small set of subcarriers, and [...] Read more.
Low density signature orthogonal frequency division multiplexing (LDS-OFDM), one type of non-orthogonal multiple access (NOMA), is a special case of multi-carrier code division multiple access (MC-CDMA). In LDS-OFDM, each user is allowed to spread its symbols in a small set of subcarriers, and there is only a small group of users that are permitted to share the same subcarrier. In this paper, we study the resource allocation for LDS-OFDM as the multiple access model in cognitive radio networks. In our scheme, SUs are allocated to certain d v subcarriers based on minimum interference or higher SINR in each subcarrier. To overcome the problem where SUs were allocated less than the d v subcarriers, we propose interference limit-based resource allocation with the fairness metric (ILRA-FM). Simulation results show that, compared to the ILRA algorithm, the ILRA-FM algorithm has a lower outage probability and higher fairness metric value and also a higher throughput fairness index. Full article
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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Article
Basic MAC Scheme for RF Energy Harvesting Wireless Sensor Networks: Throughput Analysis and Optimization
Sensors 2019, 19(8), 1822; https://doi.org/10.3390/s19081822 - 16 Apr 2019
Cited by 2 | Viewed by 1407
Abstract
Traditionally, how to reduce energy consumption has been an issue of utmost importance in wireless sensor networks. Recently, radio frequency (RF) energy harvesting technologies, which scavenge the ambient RF waves, provided us with a new paradigm for such networks. Without replacement or recharge [...] Read more.
Traditionally, how to reduce energy consumption has been an issue of utmost importance in wireless sensor networks. Recently, radio frequency (RF) energy harvesting technologies, which scavenge the ambient RF waves, provided us with a new paradigm for such networks. Without replacement or recharge of batteries, an RF energy harvesting wireless sensor network may live an eternal life. Against theoretical expectations, however, energy is scarce in practice and, consequently, structural naiveté has to be within a MAC scheme that supports a sensor node to deliver its data to a sink node. Our practical choice for the MAC scheme is a basic one, rooted in ALOHA, in which a sensor node simply repeats harvesting energy, backing off for a while and transmitting a packet. The basic medium access control (MAC) scheme is not able to perfectly prevent a collision of packets, which in turn deteriorates the throughput. Thus, we derive an exact expression of the throughput that the basic MAC scheme can attain. In various case studies, we then look for a way to enhance the throughput. Using the throughput formula, we reveal that an optimal back-off time, which maximizes the total throughput, is not characterized by the distribution but only by the mean value when the harvest times are deterministic. Also, we confirm that taking proper back-off times is able to improve the throughput even when the harvest times are random. Furthermore, we show that shaping the back-off time so that its variance is increased while its mean remains unchanged can help ameliorate the throughput that the basic MAC scheme is able to achieve. Full article
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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Article
A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization
Sensors 2019, 19(8), 1790; https://doi.org/10.3390/s19081790 - 14 Apr 2019
Cited by 15 | Viewed by 1781
Abstract
The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate [...] Read more.
The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date, there is not adequate feature extraction method that can significantly mitigate the influence of the receiver signal strength indicator (RSSI) variation that degrades the performance of the ANN-based indoor fingerprinting algorithm. In this work, a wavelet scattering transform is used to extract reliable features that are stable to small deformation and rotation invariant. The extracted features are used by a deep neural network (DNN) model to predict the location. The zeroth and the first layer of decomposition coefficients were used as features data by concatenating different scattering path coefficients. The proposed algorithm has been validated on real measurements and has achieved good performance. The experimentation results demonstrate that the proposed feature extraction method is stable to the RSSI variation. Full article
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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