Device-Free Localization: A Review of Non-RF Techniques for Unobtrusive Indoor Positioning

Alam, Fakhrul; Faulkner, Nathaniel; Parr, Baden

doi:10.1109/jiot.2020.3030174

Cited by 62 publications

(27 citation statements)

References 163 publications

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“…3) DL can learn effective features from complex raw data without the participation of the inefficient handcrafted feature specification. Motivated by this, the research community decided to take advantage of DL models and the associated powerful learning capabilities to develop more efficient and effective approaches for different human-centered Indoor IoT applications, i.e., indoor localization [8], fall detection [9], Activity monitoring [10], energy control [11], and robotic control [14]. Therefore, this study emphasizes studying the contribution of a different kind of DL to improving the efficiency of IoT applications, focusing on indoor located ones.…”

Section: Deep Learning Approaches For Human-centered Iot Applicationsmentioning

confidence: 99%

“…They also discuss the main issues of designing intelligent localization in the real world and accordingly discuss the possible improvements and future solutions. Alam [8] Presented an extensive overview of the non-RF-based approaches for device-independent indoor positioning in the IoT environment. The authors consider light-based studies, infrared-based studies, physical excitation-based studies, and electric field sensing-based studies, then discuss the main limitations of each kind of those studies and the promising research directions.…”

Section: B Surveys On Device-independent Approachesmentioning

confidence: 99%

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Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

et al. 2021

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The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.

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Section: Deep Learning Approaches For Human-centered Iot Applicationsmentioning

confidence: 99%

Section: B Surveys On Device-independent Approachesmentioning

confidence: 99%

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

et al. 2021

View full text Add to dashboard Cite

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“…to fall detection and remote monitoring of the elderly, occupancy detection for energy-efficient heating, ventilation, and air conditioning (HVAC), and lighting [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Attack Defense for Device-Free Localization

Han

Lin

Wang

et al. 2022

Mobile Information Systems

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Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances.

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“…As an individual walks on a floor, each footstep becomes the source of a physical excitation. This has been leveraged to identify and localize subjects and detect activities [ 11 , 12 , 13 , 14 , 15 , 16 ]. One of the benefits of such floor-based human sensing techniques is the potential to capture gait information.…”

Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

Self Cite

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In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

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Device-Free Localization: A Review of Non-RF Techniques for Unobtrusive Indoor Positioning

Cited by 62 publications

References 163 publications

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

CNN-Based Attack Defense for Device-Free Localization

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

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