A ZigBee Position Technique for Indoor Localization based on Proximity Learning

被引：0

作者：

Ou, Chung-Wei ^{[1
]}

Chao, Chin-Jung ^{[1
]}

Chang, Fa-Shian ^{[2
]}

Wang, Shun-Min ^{[2
]}

Liu, Guan-Xun ^{[2
]}

Wu, Min-Ren ^{[2
]}

Cho, Kai-Yi ^{[2
]}

Hwang, Lih-Tyng ^{[3
]}

Huan, Yi-Ying ^{[3
]}

机构：

[1] Chung Yuan Christian Univ, Dept Ind & Syst Engn, Chungli 320, Taiwan

[2] Cheng Shiu Univ, Dept Elect, Bird Pine Area, Kaohsiung 804, Taiwan

[3] Natl Sun Yat Sen Univ, Inst Commun Engn, Kaohsiung 804, Taiwan

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) | 2017年

关键词：

Wireless Sensor Network (WSM); ZigBee; Indoor Localization; Reference Node (RN); Blind Node (BN); Positioning Algorithm (PA);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new method of position along with node teaching and placement is proposed. Different from the traditional triangulation position, the proposed method employs a faster computation technique with special proximity learning and placement, which leads to reduced computational time. In position computation, the received signal strength indication (RSSI) values from ZigBee CC2431 modules (by Texas Instruments) were processed. The simple and efficiency position technique can be extended to many applications; for example, the handover mechanism for reference node, position using WSN protocols like Wi-Fi and Bluetooth, and basic indoor navigation.

引用

页码：875 / 880

页数：6

共 50 条

[21] RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes
Bianchi, Valentina
Ciampolini, Paolo
De Munari, Ilaria
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2019, 68 (02) : 566 - 575
[22] ZigBee based wireless indoor localization with sensor placement optimization towards practical home sensing*
Shimosaka, Masamichi
Saisho, Osamu
Sunakawa, Takuya
Koyasu, Hidenori
Maeda, Keisuke
Kawajiri, Ryoma
ADVANCED ROBOTICS, 2016, 30 (05) : 315 - 325
[23] Indoor localization system using RSSI measurement of wireless sensor network based on ZigBee standard
Sugano, Masashi
Kawazoe, Tomonori
Ohta, Yoshikazu
Murata, Masayuki
PROCEEDINGS OF THE SIXTH IASTED INTERNATIONAL MULTI-CONFERENCE ON WIRELESS AND OPTICAL COMMUNICATIONS, 2006, : 503 - +
[24] CHANNEL LEARNING IN INDOOR LOCALIZATION
Mansour, Mohamed F.
2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 3886 - 3890
[25] Indoor Localization with Transfer Learning
Korkmaz, Ilter Onat
Ozates, Tuna
Koc, Enes
Aydin, Ege
Kor, Ege
Dilek, Dogac
Gungen, Murat Alp
Kose, Idil Gokalp
Akman, Caglar
2022 30TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU, 2022,
[26] Graph-Based Machine Learning for Practical Indoor Localization
Kim, Minseuk
IEEE SENSORS LETTERS, 2022, 6 (12)
[27] Fingerprinting-based Indoor Localization with Relation Learning Network
Zhang, Lingyan
Wang, Hongyu
2019 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2019,
[28] A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine
Khatab, Zahra Ezzati
Gazestani, Amirhosein Hajihoseini
Ghorashi, Seyed Ali
Ghavami, Mohammad
SIGNAL PROCESSING, 2021, 181
[29] An Indoor Localization Method of Image Matching Based on Deep Learning
Yang, Guihua
Liang, Yu
PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC, CONTROL AND AUTOMATION ENGINEERING (MECAE 2018), 2018, 149 : 88 - 93
[30] A Deep Learning based Scene Recognition Algorithm for Indoor Localization
Labinghisa, Boney
Lee, Dong Myung
3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (IEEE ICAIIC 2021), 2021, : 167 - 170

← 1 2 3 4 5 →