1,590 research outputs found
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
Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System
The location determination in obstructed area can be very challenging
especially if Global Positioning System are blocked. Users will find it
difficult to navigate directly on-site in such condition, especially indoor car
park lot or obstructed environment. Sometimes, it needs to combine with other
sensors and positioning methods in order to determine the location with more
intelligent, reliable and ubiquity. By using ubiquitous positioning in mobile
navigation system, it is a promising ubiquitous location technique in a mobile
phone since as it is a familiar personal electronic device for many people.
However, as research on ubiquitous positioning systems goes beyond basic
methods there is an increasing need for better comparison of proposed
ubiquitous positioning systems. System developers are also lacking of good
frameworks for understanding different options during building ubiquitous
positioning systems. This paper proposes taxonomy to address both of these
problems. The proposed taxonomy has been constructed from a literature study of
papers and articles on positioning estimation that can be used to determine
location everywhere on mobile navigation system. For researchers the taxonomy
can also be used as an aid for scoping out future research in the area of
ubiquitous positioning.Comment: 15 Pages, 3 figure
A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices.Comment: 9 pages, 6 figure
Wi-Fi Location Determination for Semantic Locations
In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations
Challenges of Implementing Automatic Dependent Surveillance Broadcast in the Nextgen Air Traffic Management System
The Federal Aviation Administration is in the process of replacing the current Air Traffic Management (ATM) system with a new system known as NextGen. Automatic Dependent Surveillance-Broadcast (ADS-B) is the aircraft surveillance protocol currently being introduced as a part of the NextGen system deployment. The evolution of ADS-B spans more than two decades, with development focused primarily on increasing the capacity of the Air Traffic Control (ATC) system and reducing operational costs. Security of the ADS-B communications network has not been a high priority, and the inherent lack of security measures in the ADS-B protocol has come under increasing scrutiny as the NextGen ADS-B implementation deadline draws near.
The research conducted in this thesis summarizes the ADS-B security vulnerabilities that have been under recent study. Thereafter, we survey both the theoretical and practical efforts which have been conducted concerning these issues, and review possible security solutions. We create a classification of the ADS-B security solutions considered and provide a ranking of the potential solutions. Finally, we discuss the most compatible approaches available, given the constraints of the current ADS-B communications system and protocol
Generalizable Deep-Learning-Based Wireless Indoor Localization
The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To address the generalizability challenge faced by conventionally trained deep learning localization models, we propose the use of meta-learning-based approaches. By leveraging meta-learning, we aim to improve the models\u27 ability to adapt to new environments without extensive retraining. Additionally, since meta-learning algorithms typically require diverse datasets from various scenarios, which can be difficult to collect specifically for localization tasks, we introduce a novel meta-learning algorithm called TB-MAML (Task Biased Model Agnostic Meta Learning). This algorithm is specifically designed to enhance generalization when dealing with limited datasets. Finally, we conduct an evaluation to compare the performance of TB-MAML-based localization with conventionally trained localization models and other meta-learning algorithms in the context of indoor localization
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