Privacy in Indoor Positioning Systems: A Systematic Review

Holcer, Sylvia; Torres-Sospedra, Joaquín; Gould, Michael; Remolar, Inmaculada

doi:10.1109/icl-gnss49876.2020.9115496

Cited by 12 publications

(5 citation statements)

References 46 publications

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“…It should be distinguished from data privacy, which is an active method of controlling the access to personally identifiable information. Data that is properly secured through encryption can still reveal a user’s identity by being shared or sold to third parties [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Reasons and Strategies for Privacy Features in Tracking and Tracing Systems—A Systematic Literature Review

Jandl

Wagner

Moser

et al. 2021

Sensors

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In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.

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Section: Discussionmentioning

confidence: 99%

Reasons and Strategies for Privacy Features in Tracking and Tracing Systems—A Systematic Literature Review

Jandl

Wagner

Moser

et al. 2021

Sensors

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“…privacy-centric wireless technology-based positioning systems exist (Holcer et al, 2020), they often require supplementary infrastructure. For certain applications like tracking assembly line goods, relative location matters more than precise x-y coordinates.…”

Section: Whilementioning

confidence: 99%

Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments

Hemadasa,

Venzke,

Turau

et al. 2024

Chronicle of Computing

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Indoor Positioning Systems have gained significance in numerous industrial applications. While state-of-the-art solutions are accurate, their reliance on external infrastructures can lead to considerable costs, deployment complexities, and privacy concerns, making them suboptimal for specific contexts. Recent advancements in machine learning have surfaced as a potential solution, leveraging data solely from onboard IoT sensors. Nonetheless, the optimal machine learning models for IoT's resource constraints remain uncertain. This research introduces an indoor positioning system using motion and ambient sensors tailored for factories and similar settings with predetermined paths. The problem is framed as multivariate time series classification, comparing various ML models. A novel dataset simulating factory assembly lines is utilized for evaluation. Results demonstrate models achieving over 80% accuracy, with 1 Dimensional-Convolutional Neural Networks showing the most balanced performance followed by Multilayer Perceptrons, considering accuracy, memory footprint and latency. Decision Trees exhibit the lowest memory footprint and latency, rendering its potential for practical implementation.

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“…Sylvia et al [52] focus on privacy in navigation systems in SLR. Another SLR [53] overview of cooperative indoor positioning systems.…”

Section: Existing Survey Articlesmentioning

confidence: 99%

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

et al. 2023

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These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

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Privacy in Indoor Positioning Systems: A Systematic Review

Cited by 12 publications

References 46 publications

Reasons and Strategies for Privacy Features in Tracking and Tracing Systems—A Systematic Literature Review

Reasons and Strategies for Privacy Features in Tracking and Tracing Systems—A Systematic Literature Review

Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

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