611 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
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
Impact of Positioning Technology on Human Navigation
In navigation from one place to another, spatial knowledge helps us establish a destination and route while travelling. Therefore, sufficient spatial knowledge is a vital element in successful navigation. To build adequate spatial knowledge, various forms of spatial tools have been introduced to deliver spatial information without direct experience (maps, descriptions, pictures, etc.). An innovation developed in the 1970s and available on many handheld platforms from the early 2000s is the Global Position System (GPS) and related map and text-based navigation support systems.
Contemporary technical achievements, such as GPS, have made navigation more effective, efficient, and comfortable in most outdoor environments. Because GPS delivers such accurate information, human navigation can be supported without specific spatial knowledge. Unfortunately, there is no universal and accurate navigation system for indoor environments. Since smartphones have become increasingly popular, we can more frequently and easily access various positioning services that appear to work both indoors and outdoors. The expansion of positioning services and related navigation technology have changed the nature of navigation. For example, routes to destination are progressively determined by a “system,” not the individual. Unfortunately we only have a partial and nascent notion of how such an intervention affects spatial behaviour. The practical purpose of this research is to develop a trustworthy positioning system that functions in indoor environments and identify those aspects those should be considered before deploying Indoor Positioning System (IPS), all towards the goal of maintaining affordable positioning accuracy, quality, and consistency. In the same way that GPS provides worry free directions and navigation support, an IPS would extend such opportunities to many of our built environments. Unfortunately, just as we know little about how GPS, or any real time navigation system, affects human navigation, there is little evidence suggesting how such a system (indoors or outdoors) changes how we find our way. For this reason, in addition to specifying an indoor position system, this research examines the difference in human’s spatial behaviour based on the availability of a navigation system and evaluates the impact of varying the levels of availability of such tools (not available, partially available, or full availability). This research relies on outdoor GPS, but when such systems are available indoors and meet the accuracy and reliability or GPS, the results will be generalizable to such situations
Improving performance of pedestrian positioning by using vehicular communication signals
Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
WiFi-based urban localisation using CNNs
IEEE Conference on Intelligent Transportation Systems - ITSC 2019, 27-30/10/2019, Auckland, Nueva Zelanda.The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi
access points at almost any place in our cities. Although WiFi
has been mainly used for indoor localisation, there is a growing
number of research in outdoor WiFi-based localisation. This
paper presents a WiFi-based localisation system that takes
advantage of the huge deployment of WiFi networks in urban
areas. The idea is to complement localisation in zones where
the GPS coverage is low, such as urban canyons. The proposed
method explores the CNNs ability to handle large amounts of
data and their high accuracy with reasonable computational
costs. The final objective is to develop a system able to handle
the large number of access points present in urban areas
while preserving high accuracy and real time requirements.
The system was tested in a urban environment, improving the
accuracy with respect to the state-of-the-art and being able to
work in real time
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