SmartFinder: Cloud-based Self Organizing Localization for Mobile Smart Devices in Large-scale Indoor Facility

被引：0

作者：

Kitanouma, Takamasa ^{[1
]}

Nii, Eiji ^{[1
]}

Adachi, Naotoshi ^{[2
]}

Takizawa, Yasuhisa ^{[2
]}

机构：

[1] Kansai Univ, Grad Sch Sci & Engn, Osaka, Japan

[2] Kansai Univ, Fac Environm & Urban Engn, Osaka, Japan

来源：

2017 GLOBAL INTERNET OF THINGS SUMMIT (GIOTS 2017) | 2017年

关键词：

Tracking; Localization; Smart Devices; Self-Organizing Maps;

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 large-scale indoor facilities such as airports, train stations, factories, and hospitals, the locations of mobile smart devices provide important information for grasping the activity state of people and the utilization state of things. Consequently, many indoor localization systems for people and things have been proposed. However, such systems require a large amount of localization equipment or advanced measurements of environmental physical characteristics, and thus they strongly depend on the infrastructure for localization. In this paper, we propose SmartFinder as a solution with extremely low dependence on the infrastructure. This localization system applies our previously proposed Cloud-based Self-Organizing Localization to mobile smart devices, such as smartphones carried by people and BLE devices attached to things. SmartFinder can track the location of many mobile smart devices with only three anchor nodes and in real time.

引用

页码：201 / 206

页数：6

共 50 条

[41] Large-Scale Deep Learning Framework on FPGA for Fingerprint-Based Indoor Localization
Liu, Chang
Wang, Chun
Luo, Juan
IEEE ACCESS, 2020, 8 : 65609 - 65617
[42] Automated Execution of Large-Scale Daylighting and Glare Simulations in a Cloud-Based Parallel Computing Environment
Labib, Rania
Baltazar, Juan-Carlos
PROCEEDINGS OF BUILDING SIMULATION 2019: 16TH CONFERENCE OF IBPSA, 2020, : 1545 - 1551
[43] Realization Limits of Impulse-Based Localization System for Large-Scale Indoor Applications
Zwirello, Lukasz
Schipper, Tom
Jalilvand, Malyhe
Zwick, Thomas
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2015, 64 (01) : 39 - 51
[44] RSSI-Based LoRa Localization Systems for Large-Scale Indoor and Outdoor Environments
Lam, Ka-Ho
Cheung, Chi-Chung
Lee, Wah-Ching
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (12) : 11778 - 11791
[45] Secure and Reliable Indoor Localization Based on Multitask Collaborative Learning for Large-Scale Buildings
Wang, Chun
Luo, Juan
Liu, Xuan
He, Xiangjian
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (22) : 22291 - 22303
[46] Efficient Distributed Threshold-Based Offloading for Large-Scale Mobile Cloud Computing
Qin, Xudong
Li, Bin
Ying, Lei
IEEE-ACM TRANSACTIONS ON NETWORKING, 2023, 31 (01) : 308 - 321
[47] VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data
Bayat, Arash
Szul, Piotr
O'Brien, Aidan R.
Dunne, Robert
Hosking, Brendan
Jain, Yatish
Hosking, Cameron
Luo, Oscar J.
Twine, Natalie
Bauer, Denis C.
GIGASCIENCE, 2020, 9 (08):
[48] A Computational Framework for Large-Scale Analysis of TCRβ Immune Repertoire Sequencing Data on Cloud-Based Infrastructure
Lin, L.
Looney, T.
Lowman, G. M.
Linch, E. A.
Topacio-Hall, D. S.
Miller, L.
Zheng, J.
Pankov, A.
Au-Young, J. K.
Manivannan, M.
Kamat, A.
Andersen, M. R.
Hyland, F. C.
JOURNAL OF MOLECULAR DIAGNOSTICS, 2017, 19 (06): : 992 - 993
[49] Cloud-based Data-intensive Framework towards Fault Diagnosis in Large-scale Petrochemical Plants
Huo, Zhiqiang
Mukherjee, Mithun
Shu, Lei
Chen, Yuanfang
Zhou, Zhangbing
2016 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), 2016, : 1080 - 1085
[50] HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data
Li, Wen
Zhang, Zhining
Xie, Bo
He, Yunlin
He, Kangming
Qiu, Hong
Lu, Zhiwei
Jiang, Chunlan
Pan, Xuanyu
He, Yuxiao
Hu, Wenyu
Liu, Wenjian
Que, Tengcheng
Hu, Yanling
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 659 - 668

← 1 2 3 4 5 →