scholarly journals Indoor Localization Based on Infrared Angle of Arrival Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6278 ◽  
Author(s):  
Damir Arbula ◽  
Sandi Ljubic
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Indoor Localization ◽  
Application Programming Interface ◽  
Signal Propagation ◽  
Line Of Sight ◽  
Full Potential ◽  
Angle Of Arrival ◽  
Localization Accuracy ◽  
Server Side

Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components.

scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

AoSP-Based Secure Localization for Wireless Sensor Network

2020 ◽  
pp. 125-144
Author(s):  
Ankur Shrivastava ◽  
Nitin Gupta ◽  
Shreya Srivastav
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Signal Propagation ◽  
Multipath Fading ◽  
Wireless Sensor ◽  
Coordinate Frame ◽  
Angle Of Arrival ◽  
Node Localization ◽  
Secure Localization ◽  
Anchor Nodes

In wireless sensor network, node localization is helpful in reporting the event's origin, assisting querying of sensors, routing, and various cyber-physical system applications, where sensors are required to report geographically meaningful data for location-based applications. One of the accurate ways of localization is the use of anchor nodes which are generally equipped with global positioning system. However, in range-based approaches used in literature, like Angle of Arrival, the accuracy and precision decreases in case of multipath fading environment. Therefore, this chapter proposes an angle of signal propagation-based method where each node emits only two signals in a particular direction and knows its approximate position while receiving the second signal. Further, a method is proposed to define the coordinates of the nodes in reference to a local coordinate frame. The proposed method does the work with a smaller number of transmissions in the network even in the presence of malicious adversaries.

A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment

2013 ◽  
Vol 347-350 ◽  
pp. 3604-3608
Author(s):  
Shan Long ◽  
Zhe Cui ◽  
Fei Song
Keyword(s):  
Real Time ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Wireless Localization ◽  
Localization Accuracy ◽  
Time Dynamic ◽  
Real Time Tracking ◽  
Step Algorithm ◽  
Optimizing Algorithm ◽  
Non Line Of Sight

Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.

scholarly journals Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

2017 ◽  
Author(s):  
Liye Zhang ◽  
Shahrokh Valaee ◽  
Yu Bin Xu ◽  
Lin Ma ◽  
Farhang Vedadi
Keyword(s):  
Supervised Learning ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Propagation Model ◽  
Localization Accuracy ◽  
Crowdsourced Data ◽  
Negative Effect ◽  
Rapid Deployment ◽  
Radio Map ◽  
Localization Systems

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

scholarly journals An Ultra-Wideband (UWB) Ad Hoc Sensor Network for Real-time Indoor Localization of Emergency Responders

10.5772/14232 ◽  
2011 ◽  
Author(s):  
Anthony Lo ◽  
Alexander Yarovoy ◽  
Timothy Bauge ◽  
Mark Russell ◽  
Dave Harmer ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Ad Hoc ◽  
Indoor Localization ◽  
Ultra Wideband ◽  
Emergency Responders

We Hear Your PACE

2021 ◽  
Vol 5 (2) ◽  
pp. 1-24
Author(s):  
Chao Cai ◽  
Henglin Pu ◽  
Peng Wang ◽  
Zhe Chen ◽  
Jun Luo
Keyword(s):  
Indoor Localization ◽  
User Involvement ◽  
Small Scale ◽  
Angle Of Arrival ◽  
Localization Accuracy ◽  
Acoustic Localization ◽  
Low Snr ◽  
Active User ◽  
Median Error ◽  
Indoor Scenarios

Indoor localization is crucial to enable context-aware applications, but existing solutions mostly require a user to carry a device, so as to actively sense location-discriminating signals. However, many applications do not prefer user involvement due to, e.g., the cumbersome of carrying a device. Therefore, solutions that track user locations passively can be desirable, yet lack of active user involvement has made passive indoor localization very challenging even for a single person. To this end, we propose Passive Acoustic loCalization of multiple walking pErsons (PACE) as a solution for small-scale indoor scenarios: it passively locates users by pinpointing the positions of their footsteps. In particular, PACE leverages both structure-borne and air-borne footstep impact sounds (FIS); it uses structure-borne FIS for range estimations exploiting their acoustic dispersion nature, and it employs air-borne FIS for Angle-of-Arrival (AoA) estimations and person identifications. To combat the low-SNR nature of FIS, PACE innovatively employs domain adversarial adaptation and spectral weighting to ranging/identification and AoA estimations, respectively. We implement a PACE prototype and extensively evaluate its performance in representative environments. The results demonstrate a promising sub-meter localization accuracy with a median error of 30 cm.

scholarly journals Localino T-shirt: The Real-time Indoor Localization in Ambient Assisted Living Applications

2019 ◽  
Vol 2 (4) ◽  
pp. 196-204
Author(s):  
Stefania Dhamo ◽  
Savvas Vassiliadis ◽  
Ilias Skouras ◽  
Panagiotis Papageorgas ◽  
Ilda Kazani ◽  
...  
Keyword(s):  
Real Time ◽  
Assisted Living ◽  
Indoor Localization ◽  
Ambient Assisted Living ◽  
Mobile User ◽  
Solar Panel ◽  
Home Healthcare ◽  
Sensor Nodes ◽  
Smart Textiles ◽  
Localization Accuracy

In the last decade, smart textiles have become very popular as a concept and have found use in many applications, such as military, electronics, automotive, and medical ones. In the medical area, smart textiles research is focused more on biomonitoring, telemedicine, rehabilitation, sport medicine or home healthcare systems.In this research, the development and localization accuracy measurements of a smart T-shirt are presented, which will be used by elderly people for indoor localization in ambient assisted living applications. The proposed smart T-shirt and the work presented is considered to be applicable in cases of elderly, toddlers or even adults in indoor environments where their continuous real-time localization is critical. This smart T-shirt integrates a localization sensor, namely the Localino sensor, together with a solar panel for energy harvesting when the user is moving outdoors, as well as a battery/power bank that is both connected to the solar panel and the Localino sensor for charging and power supply respectively. Moreover, a mock-up house was deployed, where the Localino platform anchors were deployed at strategic points within the house area. Localino sensor nodes were installed in all the house rooms, from which we obtained the localization accuracy measurements. Furthermore, the localization accuracy was also measured for a selected number of mobile user scenarios, in order to assess the platform accuracy in both static and mobile user cases.Details about the implementation of the T-shirt, the selection and integration of the electronics parts, and the mock-up house, as well as about the localization accuracy measurements results are presented in the paper.

Significance and implementation of SMART Monitoring Tools

2020 ◽  
Author(s):  
Uta Koedel ◽  
Peter Dietrich ◽  
Erik Nixdorf ◽  
Philipp Fischer
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Monitoring Network ◽  
Research Field ◽  
Data Availability ◽  
Sensor Data ◽  
Integrated Analysis ◽  
Full Potential ◽  
Automatic Data ◽  
The Status

<p>The term “SMART Monitoring” is often used in digital projects to survey and analyze data flows in near- or realtime. The term is also adopted in the project Digital Earth (DE) which was jointly launched in 2018 by the eight Helmholtz centers of the research field Earth and Environment (E&E) within the framework of the German Ministry of Education and Research (BMBF). Within DE, the “SMART monitoring” sub-project aims at developing workflows and processes to make scientific parameters and the related datasets SMART, which means <strong>s</strong>pecific, <strong>m</strong>easurable, <strong>a</strong>ccepted, <strong>r</strong>elevant, and <strong>t</strong>rackable (SMART).</p><p>“SMART Monitoring” in DE comprises a combination of hard- and software tools to enhance the traditional sequential monitoring approach - where data are step-by-step analyzed and processed from the sensor towards a repository - into an integrated analysis approach where information on the measured value together with the status of each sensor and possible auxiliary relevant sensor data in a sensor network are available and used in real-time to enhance the sensor output concerning data accuracy,  precision, and data availability. Thus, SMART Monitoring could be defined as a computer-enhanced monitoring network with automatic data flow control from individual sensors in a sensor network to databases enhanced by automated (machine learning) and near real-time interactive data analyses/exploration using the full potential of all available sensors within the network. Besides, “SMART monitoring” aims to help for a better adjustment of sensor settings and monitoring strategies in time and space in iterative feedback.</p><p>This poster presentation will show general concepts, workflows, and possible visualization tools based on examples that support the SMART Monitoring idea.</p>

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