scholarly journals Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3325 ◽  
Author(s):  
Jung Ho Lee ◽  
Beomju Shin ◽  
Donghyun Shin ◽  
Jinwoo Park ◽  
Yong Sang Ryu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Service Providers ◽  
Signal Strength ◽  
Multipath Fading ◽  
Location Based Services ◽  
Walking Distance ◽  
Signal Interference ◽  
Measured Signal ◽  
Movement Trajectory ◽  
Fingerprinting Method

The Global Satellite Navigation System (GNSS) used in various location-based services is accurate and stable in outdoor environments. However, it cannot be utilized in an indoor environment because of low signal availability and degradation of accuracy due to the multipath distortion of satellite signals in urban areas. On the contrary, LTE signals are available almost everywhere in urban areas and are quite stable without much variation throughout the year. This is because of the fixed location of base stations and the well-maintained policy of mobile communication service providers. Its varied stability and reliability make LTE signals a more viable method for localization. However, there are some complexities in utilizing LTE signals including signal interference distortion phenomena during propagation multipath fading, and various types of noise. In this paper, we propose a surface correlation-based fingerprinting method to utilize LTE signals for localization in urban areas. The surface correlation converts timely measured signal strength into spatial pattern using the walking distance from a Pedestrian Dead-Reckoning (PDR). The surface correlation is carried out by comparing the spatial signal strength pattern of a pedestrian`s movement trajectory with a fingerprinting database to estimate the location. A reference trajectory of the moving pedestrian is chosen to have a greater correlation among the multiple trajectory candidates generated from a link-based fingerprinting database. By comparing spatial signal strength patterns, the proposed method can improve robustness in localization overcoming the accuracy degradation problem due to RF multipath and noise that are dominant in the conventional RSS measurement-based LTE localization scheme. The test results in urban areas demonstrate that the proposed surface correlation-based fingerprinting method has improved performance compared to the other conventional methods, thus proving to be a useful complementary method to the GNSS in urban areas.

scholarly journals A Pairwise SSD Fingerprinting Method of Smartphone Indoor Localization for Enhanced Usability

2019 ◽  
Vol 11 (5) ◽  
pp. 566 ◽  
Author(s):  
Fan Yang ◽  
Jian Xiong ◽  
Jingbin Liu ◽  
Changqing Wang ◽  
Zheng Li ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Indoor Localization ◽  
Signal Strength ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Positioning Errors ◽  
Area Of Interest ◽  
Fingerprinting Method ◽  
Strength Difference ◽  
Radio Map

Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals Examining Customers’ Critical Acceptance Factors toward Ridepooling Services

2021 ◽  
pp. 036119812110263
Author(s):  
Oliver Werth ◽  
Marc-Oliver Sonneberg ◽  
Max Leyerer ◽  
Michael H. Breitner
Keyword(s):  
Urban Areas ◽  
Service Providers ◽  
Road Traffic ◽  
Environmental Awareness ◽  
Optimal Operation ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Mobility Service ◽  
Customer Acceptance ◽  
Use Of Technology

Ridepooling is a new mobility service mainly for people in cities and urban areas. By matching the routes of customers with similar start and end points while driving in an optimally pooled manner, meaningful reductions in road traffic and related emissions can be achieved. Such services must meet customers’ demands appropriately to achieve sustainable customer acceptance. Service providers face diverse customer expectations and prejudices that differ from those toward existing transportation modes. Today, most ridepooling trips are conducted with only one customer, confirming impressions of non-optimal operation. Using a survey-based approach, possible relevant constructs for the acceptance of and intention to use ridepooling services are analyzed. Testing constructs from the Unified Theory of Acceptance and Use of Technology 2 and environmental awareness, partial least squares analysis was performed with the software SmartPLS to investigate a dataset of 224 respondents. Results suggest that attitude toward use, perceived usefulness, and performance expectancy have an influence on the behavioral intention to use ridepooling services. In contrast, environmental awareness, price value, and effort expectancy do not have such an influence. The study expands the literature about customer acceptance of ridepooling service as well as new mobility services in general. Further, the paper provides research implications and recommendations for the development and implementation of the ridepooling concept for service providers.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

scholarly journals Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services

2018 ◽  
Vol 7 (11) ◽  
pp. 442 ◽  
Author(s):  
Mehrnaz Ataei ◽  
Auriol Degbelo ◽  
Christian Kray ◽  
Vitor Santos
Keyword(s):  
Data Privacy ◽  
Location Privacy ◽  
Service Providers ◽  
Location Based Services ◽  
Legal Text ◽  
General Data Protection Regulation ◽  
Location Data ◽  
Privacy Laws ◽  
Technical Issues ◽  
General Data

An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers about how to build systems that comply with it. In order to bridge this gap, we systematically analysed the legal text, carried out expert interviews, and ran a nine-week-long take-home study with four developers. We particularly focused on user-facing issues, as these have received little attention compared to technical issues. Our main contributions are a list of aspects from the legal text of the GDPR that can be tackled at the user interface level and a set of guidelines on how to realise this. Our results can help service providers, designers and developers of applications dealing with location information from human users to comply with the GDPR.

scholarly journals Urban Thematic Exploitation Platform - supporting urban research with EO data processing, integrative data analysis and reporting

2020 ◽  
Author(s):  
Felix Bachofer ◽  
Thomas Esch ◽  
Jakub Balhar ◽  
Martin Boettcher ◽  
Enguerran Boissier ◽  
...  
Keyword(s):  
Urban Areas ◽  
High Performance ◽  
Service Providers ◽  
Research Output ◽  
European Space Agency ◽  
Data Access ◽  
Well Being ◽  
Product Portfolio ◽  
Source Information ◽  
End To End

<p>Urbanization is among the most relevant global trends that affects climate, environment, as well as health and socio-economic development of a majority of the global population. As such, it poses a major challenge for the current urban population and the well-being of the next generation. To understand how to take advantage of opportunities and properly mitigate to the negative impacts of this change, we need precise and up-to-date information of the urban areas. The Urban Thematic Exploitation Platform (UrbanTEP) is a collaborative system, which focuses on the processing of earth observation (EO) data and delivering multi-source information on trans-sectoral urban challenges.</p><p>The U-TEP is developed to provide end-to-end and ready-to-use solutions for a broad spectrum of users (service providers, experts and non-experts) to extract unique information/ indicators required for urban management and sustainability. Key components of the system are an open, web-based portal connected to distributed high-level computing infrastructures and providing key functionalities for</p><p>i) high-performance data access and processing,</p><p>ii) modular and generic state-of-the art pre-processing, analysis, and visualization,</p><p>iii) customized development and sharing of algorithms, products and services, and</p><p>iv) networking and communication.</p><p>The service and product portfolio provides access to the archives of Copernicus and Landsat missions, Datacube technology, DIAS processing environments, as well as premium products like the World Settlement Footprint (WSF). External service providers, as well as researchers can make use of on-demand processing of new data products and the possibility of developing and deploying new processors. The onboarding of service providers, developers and researchers is supported by the Network of Resources program of the European Space Agency (ESA) and the OCRE initiative of the European Commission.</p><p>In order to provide end-to-end solutions, the VISAT tool on UrbanTEP allows analyzing and visualizing project-related geospatial content and to develop storylines to enhance the transport of research output to customers and stakeholders effectively. Multiple visualizations (scopes) are already predefined. One available scope exemplary illustrates the exploitation of the WSF-Evolution dataset by analyzing the settlement and population development for South-East Asian countries from 1985 to 2015 in the context of the Sustainable Development Goal (SDG) 11.3.1 indicator. Other open scopes focus on urban green, functional urban areas, land-use and urban heat island modelling (e.g.).</p>

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

Location-Based Services (LBS) in Micro-Scale Navigation

2009 ◽  
Vol 1 (4) ◽  
pp. 51-71 ◽  
Author(s):  
Suleiman Almasri ◽  
Muhammad Alnabhan ◽  
Ziad Hunaiti ◽  
Eliamani Sedoyeka
Keyword(s):  
Service Providers ◽  
Experimental Tests ◽  
Location Based Services ◽  
Third Party ◽  
Mobile Technologies ◽  
Large Field ◽  
Battery Lifetime ◽  
Micro Scale ◽  
Multimedia Contents ◽  
Recent Developments

Pedestrians LBS are accessible by hand-held devices and become a large field of energetic research since the recent developments in wireless communication, mobile technologies and positioning techniques. LBS applications provide services like finding the neighboring facility within a certain area such as the closest restaurants, hospital, or public telephone. With the increased demand for richer mobile services, LBS propose a promising add-on to the current services offered by network operators and third-party service providers such as multimedia contents. The performance of LBS systems is directly affected by each component forming its architecture. Firstly, the end-user mobile device is still experiencing a lack of enough storage, limitations in CPU capabilities and short battery lifetime. Secondly, the mobile wireless network is still having problems with the size of bandwidth, packet loss, congestions and delay. Additionally, in spite of the fact that GPS is the most accurate navigation system, there are still some issues in micro scale navigation, mainly availability and accuracy. Finally, LBS server which hosts geographical and users information is experiencing difficulties in managing the huge size of data which causes a long query processing time. This paper presents a technical investigation and analysis of the performance of each component of LBS system for pedestrian navigation, through conducting several experimental tests in different locations. The results of this investigation have pinpointed the weaknesses of the system in micro-scale environments. In addition, this paper proposes a group of solutions and recommendations for most of these shortcomings.

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