116 research outputs found
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
Item Tracer
One of our daily issues for searching indoor lost item remain unresolved until today as there is no any systematic way of locating it. Unaccounted amount of time and energy has been wasted each day trying to retrieve it based on memory. Therefore, in this project, a prototype is proposed to locate indoor lost item utilizing received signal strength (RSS) for distance estimation. The prototype primary consists of a small size tag for attaching on any item and a reader for computing the estimated location of the tag. A positioning algorithm is developed to analyse the behaviour of received signal strength and calculate the probability of the target location. As the nature of indoor environment varies across each location, the prototype is tested at multiple indoor locations for refining the algorithm and verifying its robustness and consistency in estimating the target location. The results obtained showed that the percentage of error for direction probability is 32 % and accuracy of distance is at 0.9m
Passive round-trip-time positioning in dense ieee 802.11 networks
The search for a unique and globally available location solution has attracted researchers for a long time. However, a solution for indoor scenarios, where high accuracy is needed, and Global Positioning System (GPS) is not available, has not been found yet. Despite the number of proposals in the literature, some require too long a calibration time for constructing the fingerprinting map, some rely on the periodic broadcast of positioning information that may downgrade the data communication channel, while others require specific hardware components that are not expected to be carried on commercial off-the-shelf (COTS) wireless devices. The scalability of the location solution is another key parameter for next-generation internet of things (IoT) and 5G scenarios. A passive solution for indoor positioning of WiFi devices is first introduced here, which merges a time-difference of arrival (TDOA) algorithm with the novel fine time measurements (FTM) introduced in IEEE 802.11mc. A proof of concept of the WiFi passive TDOA algorithm is detailed in this paper, together with a thorough discussion on the requirements of the proposed algorithmThis work was funded by the Spanish Government and European Regional Development Fund (ERDF) through Comisión Interministerial de Ciencia y Tecnología (CICYT) under Project PGC2018-099945-B-I00.Peer ReviewedPostprint (published version
Collaborative Indoor Positioning Systems: A Systematic Review
Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing
steadily due to their potential to improve on the performance of their non-collaborative counterparts.
In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in
(collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is
being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of
84 works, published between 2006 and 2020, have been identified. These articles were analyzed and
classified according to the described system’s architecture, infrastructure, technologies, techniques,
methods, and evaluation. The results indicate a growing interest in collaborative positioning, and
the trend tend to be towards the use of distributed architectures and infrastructure-less systems.
Moreover, the most used technologies to determine the collaborative positioning between users are
wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time
of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended
Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the
basis of the analysis and results, several promising future research avenues and gaps in research
were identified
Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap
Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D
Self-healing radio maps of wireless networks for indoor positioning
Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real
nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente,
a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais
possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos,
sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os
sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados
para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições
conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação,
adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente
em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam
em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que
seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento
para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas
de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de
movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas
duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a
localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar
quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração
nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências
sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços
de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which
benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring,
improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be
exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and
interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they
represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi-
Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation
effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated
to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming
and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly
present in industrial environments, can be explored to help build and maintain radio maps, as long as it
is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial
vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked
using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the
self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are
proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when
Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human
effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this
solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and
analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018
Doctor of Philosophy
dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments
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
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