國立虎尾科技大學 |

Deep Learning-Based Indoor Localization for IoT Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep Learning-Based Indoor Localization for IoT Applications./
作者:	Ye, Qianwen.
面頁冊數:	1 online resource (174 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
標題:	Global positioning systems--GPS. -
電子資源:	click for full text (PQDT)
ISBN:	9798381034790

Deep Learning-Based Indoor Localization for IoT Applications.
Ye, Qianwen.

Deep Learning-Based Indoor Localization for IoT Applications. - 1 online resource (174 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

Thesis (Ph.D.)--University of Technology Sydney (Australia), 2023.

Includes bibliographical references

In recent years, localization-based Internet of Things (IoT) applications have been developed and deployed, such as interactive and personalized routing, car localization in underground parking systems and patient emergency localization. However, in indoor environment, Global Positioning System signal is not available because it is very sensitive to occlusion. Many researchers have been focusing on utilizing other technologies such as Wi-Fi (Wireless Fidelity), Radio Frequency Identifcation, Bluetooth and so on for localization services. Among these technologies, Wi-Fi has been most widely utilized for indoor localization due to its low cost and wide availability. There are various signal measurements for the Wi-Fi-based indoor localization such as Received Signal Strength (RSS), Time of Arrival, Time Diference of Arrival, Round Trip Time, Angle of Arrival, and Channel-State Information. However, RSS remains the most popular signal measurement used in Wi-Fi-based localization solution compared to other measurements, especially for localization of low-cost IoT devices with limited computing and storage resources.However, RSS-based indoor localization possesses many challenges due to multipath efects and noise, environment dynamics, device heterogeneity, limited highquality data, and security. To overcome these challenges, in this thesis, RSS fngerprintingbased indoor localization methods are developed using machine learning methods and deep learning methods. For RSS time-series data, the system of Kalman-DNN exploits the temporal dependency of these data by integrating the Kalman flter with deep neural networks, and experiment results validate efectiveness of the KalmanDNN system. However, for single RSS readings vector, a system called CapsLoc is proposed, which is an RSS fngerprinting-based indoor localization system based on CapsNet (Capsule Network). The experimental results show that CapsLoc can achieve accurate indoor localization, which outperforms some traditional machine learning methods and existing deep learning methods. Especially for heterogeneous IoT devices, RSS can be afected by superimposed challenges, i.e., device heterogeneity, database problem and energy efciency. In order to improve localization speed, EdgeLoc is proposed based on CapsNet and edge computing technology. Experiment results show that EdgeLoc outperforms state-of-the-art deep learning methods in performance of the localization accuracy and average positioning speed.Considering security issues in localization where malicious attacks at APs (Access Points) exist, a solution of SE-Loc is proposed for RSS fngerprinting-based indoor localization utilizing the deep learning methods. Extensive experiments show that SE-Locdemonstrates superior performance on secure indoor localization over the baseline methods. To address challenges including the multipath efects and noise, the environment dynamics, the device heterogeneity, data limitation, database problem and even malicious AP attacks, deep learning-based indoor localization methods are proposed. In the future, it is necessary to develop security-enhanced deep learning techniques when facing other various security problems such as AP hijacking, jamming, and man-in-the-middle attack.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381034790Subjects--Topical Terms:

1372831
Global positioning systems--GPS.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning-Based Indoor Localization for IoT Applications.
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504 $a Includes bibliographical references
520 $a In recent years, localization-based Internet of Things (IoT) applications have been developed and deployed, such as interactive and personalized routing, car localization in underground parking systems and patient emergency localization. However, in indoor environment, Global Positioning System signal is not available because it is very sensitive to occlusion. Many researchers have been focusing on utilizing other technologies such as Wi-Fi (Wireless Fidelity), Radio Frequency Identifcation, Bluetooth and so on for localization services. Among these technologies, Wi-Fi has been most widely utilized for indoor localization due to its low cost and wide availability. There are various signal measurements for the Wi-Fi-based indoor localization such as Received Signal Strength (RSS), Time of Arrival, Time Diference of Arrival, Round Trip Time, Angle of Arrival, and Channel-State Information. However, RSS remains the most popular signal measurement used in Wi-Fi-based localization solution compared to other measurements, especially for localization of low-cost IoT devices with limited computing and storage resources.However, RSS-based indoor localization possesses many challenges due to multipath efects and noise, environment dynamics, device heterogeneity, limited highquality data, and security. To overcome these challenges, in this thesis, RSS fngerprintingbased indoor localization methods are developed using machine learning methods and deep learning methods. For RSS time-series data, the system of Kalman-DNN exploits the temporal dependency of these data by integrating the Kalman flter with deep neural networks, and experiment results validate efectiveness of the KalmanDNN system. However, for single RSS readings vector, a system called CapsLoc is proposed, which is an RSS fngerprinting-based indoor localization system based on CapsNet (Capsule Network). The experimental results show that CapsLoc can achieve accurate indoor localization, which outperforms some traditional machine learning methods and existing deep learning methods. Especially for heterogeneous IoT devices, RSS can be afected by superimposed challenges, i.e., device heterogeneity, database problem and energy efciency. In order to improve localization speed, EdgeLoc is proposed based on CapsNet and edge computing technology. Experiment results show that EdgeLoc outperforms state-of-the-art deep learning methods in performance of the localization accuracy and average positioning speed.Considering security issues in localization where malicious attacks at APs (Access Points) exist, a solution of SE-Loc is proposed for RSS fngerprinting-based indoor localization utilizing the deep learning methods. Extensive experiments show that SE-Locdemonstrates superior performance on secure indoor localization over the baseline methods. To address challenges including the multipath efects and noise, the environment dynamics, the device heterogeneity, data limitation, database problem and even malicious AP attacks, deep learning-based indoor localization methods are proposed. In the future, it is necessary to develop security-enhanced deep learning techniques when facing other various security problems such as AP hijacking, jamming, and man-in-the-middle attack.
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