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

[11] Sequential learning for fingerprint based indoor localization
Nhan Vo Than Ngo
Kim, Jeong Geun
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2017, 71 : 105 - 109
[12] Survey of Indoor Localization Based on Deep Learning
Kordi, Khaldon Azzam
Roslee, Mardeni
Alias, Mohamad Yusoff
Alhammadi, Abdulraqeb
Waseem, Athar
Osman, Anwar Faizd
CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 79 (02): : 3261 - 3298
[13] Study of Received Signal Strength Indication in ZigBee Location Cluster for Indoor Localization
Kaemarungsi, Kamol
Ranron, Rachasak
Pongsoon, Prasit
2013 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON), 2013,
[14] A new deep learning-based distance and position estimation model for range-based indoor localization systems
Guidara, Amir
Fersi, Ghofrane
Jemaa, Maher Ben
Derbel, Faouzi
AD HOC NETWORKS, 2021, 114
[15] An Experimental Comparison of RSSI-Based Indoor Localization Techniques Using ZigBee Technology
Fahama, Hassoon Salman
Ansari-Asl, Karim
Kavian, Yousef Seifi
Soorki, Mehdi Naderi
IEEE ACCESS, 2023, 11 : 87985 - 87996
[16] A Measurement Study of Zigbee-based Indoor Localization Systems Under RF Interference
Lau, Seng-Yong
Lin, Tsung-Han
Huang, Te-Yuan
Ng, I-Hei
Huang, Polly
4TH ACM INTERNATIONAL WORKSHOP ON WIRELESS NETWORK TESTBEDS, EXPERIMENTAL EVALUATION AND CHARACTERIZATION-WINTECH 2009, 2009, : 35 - 42
[17] An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services
Elbes, Mohammed
Almaita, Eyad
Alrawashdeh, Thamer
Kanan, Tarek
AlZu'bi, Shadi
Hawashin, Bilal
2019 IEEE JORDAN INTERNATIONAL JOINT CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY (JEEIT), 2019, : 437 - 441
[18] WiFi Based Indoor Localization with Multiple Kernel Learning
Fan, Heng
Chen, Zhongmin
PROCEEDINGS OF 2016 8TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS (ICCSN 2016), 2016, : 474 - 477
[19] Development of a Localization System Based on VLC Technique for an Indoor Environment
Yi, Keon Young
Kim, Dae Young
Yi, Kwang Moo
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2015, 10 (01) : 436 - 442
[20] Self-Adaptive Filtering Approach for Improved Indoor Localization of a Mobile Node with Zigbee-Based RSSI and Odometry
Loganathan, Anbalagan
Ahmad, Nur Syazreen
Goh, Patrick
SENSORS, 2019, 19 (21)

← 1 2 3 4 5 →