1,324 research outputs found
A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments
Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters
A comparison analysis of ble-based algorithms for localization in industrial environments
Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters
Indoor positioning with deep learning for mobile IoT systems
2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification
Copresence verification based on context can improve usability and strengthen
security of many authentication and access control systems. By sensing and
comparing their surroundings, two or more devices can tell whether they are
copresent and use this information to make access control decisions. To the
best of our knowledge, all context-based copresence verification mechanisms to
date are susceptible to context-manipulation attacks. In such attacks, a
distributed adversary replicates the same context at the (different) locations
of the victim devices, and induces them to believe that they are copresent. In
this paper we propose DoubleEcho, a context-based copresence verification
technique that leverages acoustic Room Impulse Response (RIR) to mitigate
context-manipulation attacks. In DoubleEcho, one device emits a wide-band
audible chirp and all participating devices record reflections of the chirp
from the surrounding environment. Since RIR is, by its very nature, dependent
on the physical surroundings, it constitutes a unique location signature that
is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR
data with various mobile devices and in a range of different locations. We show
that DoubleEcho mitigates context-manipulation attacks whereas all other
approaches to date are entirely vulnerable to such attacks. DoubleEcho detects
copresence (or lack thereof) in roughly 2 seconds and works on commodity
devices
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
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