Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity

Haus, Michael; Ding, Aaron Yi; Ott, Jörg

doi:10.1109/percomworkshops48775.2020.9156105

Cited by 4 publications

(2 citation statements)

References 19 publications

(20 reference statements)

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“…The paper by [6] described another way to accomplish how a tracking system works with a camera-based hand-held system. Moreover, the researchers could obtain Bluetooth communication through various topics such as object tracking, child monitoring, and location detection [7][8][9]. Existing research [10][11][12][13] has shown how GPS could be interpreted through various aspects and combine multiple systems [14,15].…”

Section: Introductionmentioning

confidence: 99%

D-Tags Design by Combining Bluetooth Router, IoT, and Mobile Phone to Track Personal Items

Farouq¹,

Fathurrahman²

2023

ELKHA

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Losing personal items such as a wallet or room keys is disturbing. Problems arise when clues to find the item are lacking or even non-existent. Of one hundred-two people who filled out the questionnaire about how often losing their belongings, 76% had experienced it. Because of that, it must be hard to remember where the last they put the stuff. Therefore people need tools that can help them easily find their item with a transmitter and connect to a mobile phone. Previous research showed that the transmitter with a frequency system had a detection distance of only 5 meters. From this weakness, the authors propose the development of a tracking items device that combines an Internet of Things-based Bluetooth transmitter and receiver system approach called D-Tags by combining Bluetooth routers, IoT, and mobile phones. The system is designed for both indoor and outdoor areas. Bluetooth testing allows the device to detect items up to 7.43 meters without wall obstacles. The system provided location information such as Living Room or Bedroom and the coordinates when outside the room. Regarding time, a single detection item is faster in the range of 15.13 seconds to 15.60 seconds than searching for two things simultaneously. From the tracking radius of the outdoor area, the device can track items up to 31.8 meters from the last item's position. All information tracking history can be seen on the web application. The experiment results prove that D-Tags can be used to track items by indicating their location and with a relatively short search duration.

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Section: Introductionmentioning

confidence: 99%

D-Tags Design by Combining Bluetooth Router, IoT, and Mobile Phone to Track Personal Items

Farouq¹,

Fathurrahman²

2023

ELKHA

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“…In another aspect addressed in this work, it is noted that handling of multi-user location information over extended periods of time generates large volumes of spatio-temporal data, which highlights the need for compact data representations [25][26][27][28]. Processing techniques applied to proximity and community detection [29][30][31], and trajectory similarities and cluster formations [32][33][34] have also been of interest recently. For our purpose, emphasis is placed on applying low-complexity reduction to achieve a balanced tradeoff between data compression savings and retained accuracy in mobiles' proximity detection.…”

Section: Introductionmentioning

confidence: 99%

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

2022

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The paper considers mobile location tracking and trajectory data reduction techniques for applications pertaining to mobile contact tracing and proximity detection in wireless cellular networks. Unscented Kalman filtering with non-line-of-sight bias mitigation is first applied for robust mobile trajectory estimation. An approach for modeling and analysis of pair-wise proximity and multi-mobile clustering scenarios is then introduced within a hypothesis testing framework, and a thorough performance evaluation is presented to assess the achievable detection and false alarm probabilities based on factors pertaining to proximity distance and timespan, ranging accuracy and bias statistics. For scenarios of practical interest, results show that correct proximity detection rates in excess of 70-to-80% range can be achieved while maintaining very low false alarm rates. Data reduction using the discrete Haar transform is subsequently applied for efficient storage of trajectory data. An analysis of the tradeoffs between reduction level and proximity detection reliability is presented to demonstrate the viability of the proposed approach with its low complexity and good performance at moderate reduction levels. Additional comparative analysis is presented to assess the impact of specific distance measures and wavelet types, and it is found that the Chebyshev distance offers improvements in detection accuracy compared to Euclidean and Manhattan measures, while wavelet change, when retaining short support, didn't have a significant impact.

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Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

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Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity

Cited by 4 publications

References 19 publications

D-Tags Design by Combining Bluetooth Router, IoT, and Mobile Phone to Track Personal Items

D-Tags Design by Combining Bluetooth Router, IoT, and Mobile Phone to Track Personal Items

Efficient Mobile Location Tracking and Data Reduction for Proximity Detection Applications

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

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