A Compressive Sensing Approach to Detect the Proximity Between Smartphones and BLE Beacons

Ng, Pai Chet; She, James; Ran, Rong

doi:10.1109/jiot.2019.2914733

Cited by 33 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Very promising approach also in the COVID-19 contact-tracing context, even if not studied for this purpose in [85]; detection probabilities up to 90% for sub-m distances.…”

Section: Ref Main Findingsmentioning

confidence: 99%

“…• Improved signal processing at the transmitter side, for example, by the optimized power amplifier to obtain high efficiency at low transmit power levels [166]; • Improved signal processing at the receiver side-the authors of [31] used, for example, dynamic modeling via Markov chains for more efficient integration of sensors readings for positioning; • Improved communications and/or localization protocols for example by optimized routing of the events or packets [167]; • Ultra low-power communication technologies: low-power technologies, such as BLE, ZigBee, LoRa, etc., are essential for a lasting battery life, but decreasing even more the power consumption is a topic of active research under the umbrella of "ultra low-power" technologies such as wireless-powered networks with back-scattered communications [168], tunable impulse radio UWB technologies [169], or wearable technologies relying on sensors which use the electrostatic induction current generated by human motion [170]; • Data compression methods for transmitting a lower amount of data by removing redundancies in data to be transmitted -while such methods have been vastly studied in the context of wireless communications, e.g., in [171] or via compressed sensing in [85], their applicability to user tracking and contact tracing is still to be determined; • Approximate computing methods rely on trading accuracy for a lower power consumption [172], for example, by reducing the number of quantization bits of by approximating some tasks in the execution flow; • Task offloading methods [173] rely on delegating/moving some of the more computationally demanding tasks to an edge or cloud server; such methods typically demand the presence of a centralized unit/server and therefore are not well suited to decentralized approaches. In addition, task offloading increases the wireless transmission delays and may hinder a real-time contact-tracing app's viability.…”

Section: Energy-efficiency Aspectsmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

“…Very promising approach also in the COVID-19 contact-tracing context, even if not studied for this purpose in [85]; detection probabilities up to 90% for sub-m distances.…”

Section: Ref Main Findingsmentioning

confidence: 99%

Section: Energy-efficiency Aspectsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…with Bluetooth Low Energy (BLE) [14,15] more acclaimed than the others. Magnetic field, ultrasound and UWB used for contact tracing require to set up expensive systems, even though they can ensure high localization accuracy.…”

Section: Contact Tracingmentioning

confidence: 99%

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

Uthayan¹,

Prasad²,

Mohan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease (i.e., . As a countermeasure, contact tracing and social distancing are essential to prevent the transmission of the virus, which can be achieved using indoor location analytics. Based on the indoor location analytics, the human mobility on a site can be monitored and planned to minimize human's contact and enforce social distancing to contain the transmission of COVID-19. Given the indoor location data, the clustering can be applied to cluster spatial data, spatio-temporal data and movement behavior features for proximity detection or contact tracing applications. More specifically, we propose the Coherent Moving Cluster (CMC) algorithm for contact tracing, the density-based clustering (DBScan) algorithm for identification of hotspots and the trajectory clustering (TRACLUS) algorithm for clustering indoor trajectories. The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users. The network of users is used to model an optimization problem to manage the human mobility on a site. The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools. The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30% in terms of accuracy of clustering trajectories. By adopting this system for human mobility management, the count of close contacts among the users within a confined area can be reduced by 80% in the scenario where all users are allowed to access the site.

show abstract

“…There are also works study proximity detection in dense environments [33], or proximity accuracy with filtering technique [34]. Most of these works study the proximity detection between a human and an object attached to a BLE beacon [35], but not the proximity sensing between the devices carried by humans.…”

Section: Proximity Sensing and Classificationmentioning

confidence: 99%

COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing

Spachos

Plataniotis

2021

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

While contact tracing is of paramount importance in preventing the spreading of infectious diseases, manual contact tracing is inefficient and time consuming as those in close contact with infected individuals are informed hours, if not days, later. This article proposes a smart contact tracing (SCT) system utilizing the smartphone's Bluetooth low energy signals and machine learning classifiers to automatically detect those possible contacts to infectious individuals. SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communication protocol. To protect the user's privacy, both broadcasted and observed signatures are stored in the user's smartphone locally and only disseminate the stored signatures through a secure database when a user is confirmed by public health authorities to be infected. Using received signal strength each smartphone estimates its distance from other user's phones and issues real-time alerts when social distancing rules are violated. Extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers indicate that a decision tree classifier outperforms other state-of-the-art classification methods with an accuracy of about 90% when two users carry their smartphone in a similar manner. Finally, to facilitate research in this area while contributing to the timely development, the dataset of six experiments with about 123 000 data points is made publicly available.

show abstract

A Compressive Sensing Approach to Detect the Proximity Between Smartphones and BLE Beacons

Cited by 33 publications

References 45 publications

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

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

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing

Contact Info

Product

Resources

About