96 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
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
Practical implementation of a hybrid indoor localization system
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor localization systems occupy a significant role to track objects during their life
cycle, e.g., related to retail, logistics and mobile robotics. These positioning systems use
several techniques and technologies to estimate the position of each object, and face several
requirements such as position accuracy, security, coverage range, energy consumption and
cost. This master thesis describes a real-world scenario implementation, based on Bluetooth
Low Energy (BLE) beacons, evaluating a Hybrid Indoor Positioning System (H-IPS)
that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting
(FP). The objective is to track a target node, assuming that the object follows a linear
motion model. It was employed Kalman Filter (KF) to decrease the positioning errors of
the MLT and FP techniques. Furthermore a Track-to-Track Fusion (TTF) is performed
on the two KF outputs in order to maximize the performance. The results show that the
accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed
in 52%. Finally, the proposed solution demonstrated a probability of error < 2 m
of 80%, while the same probability for the FP and MLT are 56% and 20%, respectively.Os sistemas de localização de ambientes internos desempenham um papel importante
na localização de objectos durante o seu ciclo de vida, como por exemplo os relacionados
com o varejo, a logística e a robótica móvel. Estes sistemas de localização utilizam várias
técnicas e tecnologias para estimar a posição de cada objecto, e possuem alguns critérios
tais como precisão, segurança, alcance, consumo de energia e custo. Esta dissertação
de mestrado descreve uma implementação num cenário real, baseada em Bluetooth Low
Energy (BLE) beacons, avaliando um Sistema Híbrido de Posicionamento para Ambientes
Internos (H-IPS, do inglês Hybrid Indoor Positioning System) que combina duas abordagens
baseadas no Indicador de Intensidade do Sinal Recebido (RSSI, do inglês Received
Signal Strength Indicator): Multilateração (MLT) e Fingerprinting (FP). O objectivo é
localizar um nó alvo, assumindo que o objecto segue um modelo de movimento linear.
Foi utilizado Filtro de Kalman (FK) para diminuir os erros de posicionamento do MLT
e FP, além de aplicar uma fusão de vetores de estado nas duas saídas FK, a fim de
maximizar o desempenho. Os resultados mostram que a precisão do H-IPS supera o FP
original em 21%, enquanto que o MLT original tem um desempenho superior a 52%. Finalmente,
a solução proposta apresentou uma probabilidade de erro de < 2 m de 80%,
enquanto a mesma probabilidade para FP e MLT foi de 56% e 20%, respectivamente
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
The Fragility of Noise Estimation in Kalman Filter: Optimization Can Handle Model-Misspecification
The Kalman Filter (KF) parameters are traditionally determined by noise
estimation, since under the KF assumptions, the state prediction errors are
minimized when the parameters correspond to the noise covariance. However,
noise estimation remains the gold-standard regardless of the assumptions - even
when it is not equivalent to errors minimization. We demonstrate that even
seemingly simple problems may include multiple assumptions violations - which
are sometimes hard to even notice. We show theoretically and empirically that
even a minor violation may largely shift the optimal parameters. We propose a
gradient-based method along with the Cholesky parameterization to explicitly
optimize the state prediction errors. We show consistent improvement over noise
estimation in tens of experiments in 3 different domains. Finally, we
demonstrate that optimization makes the KF competitive with an LSTM model -
even in non linear problems
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