Overview of received signal strength based fingerprinting localization in indoor wireless LAN environments

Ding, Genming; Zhang, Jinbao; Zhang, Lingwen; Zhang, Tan

doi:10.1109/mape.2013.6689973

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…The positional fingerprint method [6] is probably the best solution for localization inside buildings. The method consists of two phases.…”

Section: Fingerprinting Methodsmentioning

confidence: 99%

“…Aside from this, the measurement by RFID technology was in progress at the time, so even this could have affected the accuracy of the results. The final factor, which is more a characteristic of the environment, is the imaginary triangle created by the localization points (rectangular with an arrow) (5), (6), and (8). There were no classic doors leading into zone 2; the triangle shape can therefore be considered as one area, which explains the results.…”

Section: The First Test Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure

Hrad

Vojtěch

Cihlar

et al. 2020

Sensors

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This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms.

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“…The positional fingerprint method [6] is probably the best solution for localization inside buildings. The method consists of two phases.…”

Section: Fingerprinting Methodsmentioning

confidence: 99%

Section: The First Test Environmentmentioning

confidence: 99%

Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure

Hrad

Vojtěch

Cihlar

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…This is due to improved localization and reduced computational complexity, as concluded by Amr et al [ 19 ]. A detailed comparison of technologies and algorithms implementing the fingerprint technique for IoT indoor positioning has been carried out by [ 15 , 21 , 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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“…Chen et al [29] proposed the application of K-means to cluster fingerprint samples. Ding et al [35] proposed a fingerprint clustering method based on APC to avoid an initial starting point selection. Saha and Sadhukhan [36] proposed a hierarchical clustering strategy combined with KNN to locate the object.…”

Section: Related Studiesmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Wang

Fan

et al. 2020

Mobile Information Systems

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Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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Overview of received signal strength based fingerprinting localization in indoor wireless LAN environments

Cited by 30 publications

References 21 publications

Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure

Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

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