An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services

Elbes, Mohammed; Almaita, Eyad; Alrawashdeh, Thamer A.; Kanan, Tarek; AlZurbi, Shadi; Hawashin, Bilal

doi:10.1109/jeeit.2019.8717509

Cited by 30 publications

(9 citation statements)

References 9 publications

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“…Jaafar and Saab [ 32 ] realized point estimation using a MLP regression model after initial room classification. Using the data collected during walking along a predefined path, Sahar and Han [ 33 ] as well as Xu et al [ 34 ], Elbes et al [ 35 ], and Chen et al [ 15 ] utilized LSTM with a regression output layer to predict the exact position. Ibrahim et al [ 36 ] utilized a convolutional neural network (CNN) on RSS time-series data to estimate the coordinate on the lowest layer of their hierarchical prediction model (building and floor on higher levels).…”

Section: Related Workmentioning

confidence: 99%

“…It has been demonstrated that deep learning is a valuable tool for fingerprinting-based indoor localization. While the RSS of WLAN access points (AP) is predominantly used to establish fingerprints [ 33 , 35 , 41 ], alternative approaches have been presented. CSI of WLAN APs is increasingly used, since it contains richer multipath information [ 53 ].…”

Section: Related Workmentioning

confidence: 99%

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DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Laska

Blankenbach

2021

Sensors

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Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Laska

Blankenbach

2021

Sensors

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“…The results obtained showed that 95% of the position estimated errors were less than 1m. In [22], the authors suggested a novel indoor localization approach based on the fingerprints of RSSI measurements. They used Wi-Fi and machine learning techniques based on long short-term memory neural networks to estimate location.…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

et al. 2020

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The number of older adults with Alzheimer's disease is increasing every year. The associated memory problems cause many difficulties for Alzheimer's patients and their caretakers; patients may even become lost in familiar surroundings. In this paper, a proposed localization system based on a wireless sensor network (WSN) and backpropagation based artificial neural network (BP-ANN) was practically implemented to detect and determine the position of an Alzheimer's patient in an indoor environment. The proposed system consisted of four ZigBee-based XBee S2C anchor nodes and one mobile node carried by the Alzheimer's patient. The received signal strength indicator (RSSI) of the anchor nodes was collected by the mobile node using a laptop supported by X-CTU software. The obtained RSSI values were used as input for training, testing, and validation processes of the BP-ANN, while two-dimension (2D) locations (x and y) were used as the output of the ANN. The results showed that the obtained mean localization errors were 0.964 and 0.921m for validation and testing phases, respectively, after applying the ANN. Based on a comparison with state-ofthe-art technology, we deduced that the proposed ANN method outperformed other techniques in previous studies in terms of mean localization error.

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“…[29] present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which contains both an off-line phase for training and an on-line phase for localization. Reference [30] propose a novel indoor localization approach based on fingerprints of Received Signal Strength Indicator (RSSI) measurements. The approach utilized machine learning techniques using LSTM Neural Networks for location estimation.…”

Section: B Deep Learning In a Localization Systemmentioning

confidence: 99%

DeepTAL: Deep Learning for TDOA-Based Asynchronous Localization Security With Measurement Error and Missing Data

Yuan

Wang

et al. 2019

IEEE Access

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The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements are not yet complete. Like any other process, localization also has security requirements. The use of ultra-wideband (UWB) indoor localization systems have recently grown quickly in industries, with a reliable, fast, and have high accuracy performances. In particular, time difference-of-arrival (TDOA) is one of the widely used localization models. However, as TDOA measurement errors increase, the accuracy of the localization decreases. The accuracy of the TDOA measurement is influenced the accuracy of the localization and affected by multiple factors such as time synchronization, errors in sensor positions, missing data is caused by network attack. To reduce the influence of a sensor measurement error in localization, this paper proposes an improved localization algorithm for source localization using deep learning to address a TDOA measurements error or missing data in an asynchronous localization called DeepTAL. Unlike the conventional algorithm, DeepTAL can obtain highly accurate localization data in the presence of TDOA measurement errors or missing data. The algorithm starts with TDOA measurements without time synchronization. A network based on Long Short-Term Memory (LSTM) is then applied to achieve a stronger learning and better representation of the determined target state and TDOA prediction. The network can express features more abstractly at higher levels and increase recognition accuracy. After that, the target node is accurately located by TDOAs. We implement the DeepTAL algorithm on an asynchronous localization system with UWB signals. The experiments show that the proposed DeepTAL algorithm is efficient in improving the localization precision as measurement errors or missing data.

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An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services

Cited by 30 publications

References 9 publications

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment

DeepTAL: Deep Learning for TDOA-Based Asynchronous Localization Security With Measurement Error and Missing Data

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