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Special Issue "Wireless Communication Systems for Localization"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 31 August 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Suk-Seung Hwang

Department of Electronic Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju, 501-759, Korea
E-Mail
Phone: +82-62-230-7741
Fax: +82 62 233 6896
Interests: adaptive signal processing; wireless communications; location detection technology; interference cancellation; channel estimation; GPS; RFID

Special Issue Information

Dear Colleagues,

Location detection technology (LDT), which has a variety of applications, including the energy consumption monitoring system, is one of core techniques in modern wireless communication systems. As wireless communication techniques are developed, we can find more and more applications related to LDT in industrial fields. Many researchers involved in this area are pushing to efficiently develop accurate mobile devices with high performance, low-costs, and reusability of components.

The goal of this Special Issue is to contribute to the development of various theories, applications, mathematical models, simulations, etc., related to localization techniques based on wireless communication systems.

Topics of interest include, but are not limited to:

  • Advanced Location Detection Technologies Related to Time of Arrival (ToA), Time Difference of Arrival (TDoA), Angle of Arrival (AoA), Cell ID, Fingerprinting Technique, Trilateration, etc.
  • Localization in Networks (IoT, Next Generation, Vehicle, ITS, etc.)
  • Indoor Localization and RFID Based Positioning Technique
  • Localization for Energy Saving and Energy Management
  • Signal Processing and Communication Theory for Localization
  • Statistical and Adaptive Signal Processing for Localization
  • Location-Based-Service Applications Including Emergency Service
  • Localization Technique for Disaster
  • Satellite Based Positioning Technique (Global Positioning System, Galileo, COMPASS, GLONASS, etc.)
  • Maritime and Underwater Localization
  • AOA Estimation and Ranging Techniques

Prof. Dr. Suk-Seung Hwang
Guest Editor

Manuscript Submission Information

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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. Energies 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 1800 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

  • localization
  • location detection technology
  • wireless communications
  • satellite based positioning
  • indoor positioning
  • networks
  • energy saving
  • energy management
  • location-based-service
  • signal processing
  • adaptive signal processing
  • angle of arrival estimation
  • ranging technique
  • underwater localization

Published Papers (4 papers)

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Research

Open AccessArticle
DNN-Assisted Cooperative Localization in Vehicular Networks
Energies 2019, 12(14), 2758; https://doi.org/10.3390/en12142758
Received: 23 May 2019 / Revised: 25 June 2019 / Accepted: 16 July 2019 / Published: 18 July 2019
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Abstract
This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization [...] Read more.
This work develops a deep-learning-based cooperative localization technique for high localization accuracy and real-time operation in vehicular networks. In cooperative localization, the noisy observation of the pairwise distance and the angle between vehicles causes nonlinear optimization problems. To handle such a nonlinear optimization task at each vehicle, a deep neural network (DNN) technique is to replace a cumbersome solution of nonlinear optimization along with the saving of the computational loads. Simulation results demonstrate that the proposed technique attains some performance gain in localization accuracy and computational complexity as compared to existing cooperative localization techniques. Full article
(This article belongs to the Special Issue Wireless Communication Systems for Localization)
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Graphical abstract

Open AccessArticle
Robust Localization for Robot and IoT Using RSSI
Energies 2019, 12(11), 2212; https://doi.org/10.3390/en12112212
Received: 24 April 2019 / Revised: 2 June 2019 / Accepted: 6 June 2019 / Published: 11 June 2019
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Abstract
Node-localization technology has been supported in the wireless sensor network (WSN) environment. Node localization is based on a few access-point (AP) nodes that comprises positioning information because they are fixed, and a beacon node that comprises unknown positioning information because it is moving. [...] Read more.
Node-localization technology has been supported in the wireless sensor network (WSN) environment. Node localization is based on a few access-point (AP) nodes that comprises positioning information because they are fixed, and a beacon node that comprises unknown positioning information because it is moving. To determine the position of the unknown node, it must use two or three APs that comprise certain positioning information. There are a number of representative range-based methods, including time of arrival (TOA), weighted centroid locating algorithm, received signal strength intensity (RSSI), and time difference of arrival (TDOA) signal, that are received by the receiver. The RSSI method has its advantages. A simple device structure means that the RSSI method is easy to use. Because the structures of previous wireless local area network (LAN) technologies make them compatible with RSSI information, the RSSI method is widely used in the related area of position tracking. In addition, this algorithm has a hardware system that cannot be increased, has the advantage of the miniaturization of the node, and can wear through obstacles. This paper proposes the application of a robust ranging method that can be applied in robots and Internet of Things (IoT) using RSSI, especially in the tracing location of each nursing home patient, where the RSSI method with trilateral technique is used. This paper shows the results of the measured point from the application of the trilateral technique, and it also represents the results of the error distance between the ideal point and the measured point using computer simulation. Finally, this paper presents an estimation of localization using a real experimental device with a BLE (Bluetooth low-energy) transmitter and receiver, and beacon gateway, by applying an RSSI algorithm with the trilateral technique. Full article
(This article belongs to the Special Issue Wireless Communication Systems for Localization)
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Open AccessArticle
Hybrid TOA Trilateration Algorithm Based on Line Intersection and Comparison Approach of Intersection Distances
Energies 2019, 12(9), 1668; https://doi.org/10.3390/en12091668
Received: 7 March 2019 / Revised: 18 April 2019 / Accepted: 28 April 2019 / Published: 1 May 2019
Cited by 1 | PDF Full-text (4245 KB) | HTML Full-text | XML Full-text
Abstract
The ever-growing mobile station (MS) localization technologies provide an increasingly important role in all aspects of the wireless cellular systems and Internet of Things (IoT). The accurate MS location information is the basis in connection of different devices in IoT. The MS localization [...] Read more.
The ever-growing mobile station (MS) localization technologies provide an increasingly important role in all aspects of the wireless cellular systems and Internet of Things (IoT). The accurate MS location information is the basis in connection of different devices in IoT. The MS localization techniques based on time of arrival (TOA) trilateration algorithm, which determines the location of MS using an intersection point of three circles based on distances between MS and base stations (BS) and coordinates of BSs, have been actively studied. In general, the distance between the MS and BS is calculated by counting the number of delay samples or measuring the power of the received signal. Since the estimated distance (radius of a circle) between MS and BS is commonly increased, three circles may not meet at a single point, resulting in the estimation error of MS localization. In order to improve this problem, in this paper, we propose the hybrid TOA trilateration algorithm based on the line intersection algorithm for the general case for intersection of three circles and the comparison approach of intersection distances for the specific case where a small circle is located inside the area of two large circles. The line intersection algorithm has an excellent location estimation performance in the general case, but it does not work in the specific case. The comparison approach of intersection distances has good performance only for the specific case. In addition, we propose the mode selection algorithm to efficiently select a proper mode between the general and specific cases. The representative computer simulation examples are provided to verify the localization performance of the proposed algorithm. Full article
(This article belongs to the Special Issue Wireless Communication Systems for Localization)
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Open AccessArticle
Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network
Energies 2018, 11(11), 2866; https://doi.org/10.3390/en11112866
Received: 5 October 2018 / Revised: 16 October 2018 / Accepted: 19 October 2018 / Published: 23 October 2018
Cited by 3 | PDF Full-text (7325 KB) | HTML Full-text | XML Full-text
Abstract
Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This [...] Read more.
Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption. Full article
(This article belongs to the Special Issue Wireless Communication Systems for Localization)
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