Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Sun, Yongliang; Zhang, Xuzhao; Wang, Xiaocheng; Zhang, Xinggan

doi:10.1155/2018/4201367

Cited by 28 publications

(16 citation statements)

References 33 publications

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“…In [ 21 ] the authors consider the notion of structural complexity from a geometrical point of view and argue that it can be characterized using general metrics computed on three-dimensional sealed structural models. Similar approaches are described in references [ 22 , 23 , 24 , 25 , 26 , 27 ]. All these studies refer to localization and improving the discrimination between different radio-detected targets in the vicinity of the detection device(s).…”

Section: Related Workmentioning

confidence: 99%

Developing a Solution for Mobility and Distribution Analysis Based on Bluetooth and Artificial Intelligence

Minea

Dumitrescu

Costea

et al. 2020

Sensors

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The purpose of this research was to develop a simple, cost-effective, but enough efficient solution for locating, tracking and distribution analysis of people and/or vehicle flowing, based on non-intrusive Bluetooth sensing and selective filtering algorithms employing artificial intelligence components. The solution provides a tool for analyzing density of targets in a specific area, useful when checking contact proximities of a target along a route. The principle consists of the detection of mobile devices that use active Bluetooth connections, such as personal notebooks, smartphones, smartwatches, Bluetooth headphones, etc. to locate and track their movement in the dedicated area. For this purpose, a specific configuration of three BT sensors is used and RSSI levels compared, based on a combination of differential location estimates. The solution may also be suited for indoor localization where GPS signals are usually weak or missing; for example, in public places such as subway stations or trains, hospitals, airport terminals and so on. The applicability of this solution is estimated to be vast, ranging from travel and transport information services, route guidance, passenger flows tracking, and path recovery for persons suspected to have SARS-COV2 or other contagious viruses, serving epidemiologic enquiries. The specific configuration of Bluetooth detectors may be installed either in a fixed location, or in a public transport vehicle. A set of filters and algorithms for triangulation-based location of detected targets and movement tracking, based on artificial intelligence is employed. When applied in the public transport field, this setup can be also developed to extract additional information on traffic, such as private traffic flowing, or passenger movement patterns along the vehicle route, improved location in absence of GPS signals, etc. Field tests have been carried out for determining different aspects concerning indoor location accuracy, reliability, selection of targets and filtering. Results and possible applications are also presented in the final section of the paper.

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Section: Related Workmentioning

confidence: 99%

Developing a Solution for Mobility and Distribution Analysis Based on Bluetooth and Artificial Intelligence

Minea

Dumitrescu

Costea

et al. 2020

Sensors

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“…So far, people have developed various indoor localization methods using infrared, ultrasound, radio frequency identifi-cation (RFID), ZigBee, Bluetooth, ultra wideband (UWB), and Wi-Fi [9][10][11][12]. Among them, the indoor localization method using Wi-Fi has become a research hotspot because of its low cost, widely deployed infrastructure, and so on.…”

Section: Introductionmentioning

confidence: 99%

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Bai

Sun

Meng

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

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“…In particular, a received signal strength indicator (RSSI) can also be used to measure signal variations between sensor nodes. The presence of a human can be detected when the person goes into the monitoring area because the movement will reflect, scatter and absorb the RSSI signal [10]. Therefore, using this method, it is possible to infer a person's location by analyzing its influence on the wireless signals.…”

Section: Introductionmentioning

confidence: 99%

RSSI-Based for Device-Free Localization Using Deep Learning Technique

et al. 2020

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Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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Device‐Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks

Cited by 28 publications

References 33 publications

Developing a Solution for Mobility and Distribution Analysis Based on Bluetooth and Artificial Intelligence

Developing a Solution for Mobility and Distribution Analysis Based on Bluetooth and Artificial Intelligence

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

RSSI-Based for Device-Free Localization Using Deep Learning Technique

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