High Resolution Beacon-Based Proximity Detection for Dense Deployment

Ng, Pai Chet; She, James; Park, Soochang

doi:10.1109/tmc.2017.2759734

Cited by 35 publications

(14 citation statements)

References 43 publications

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“…Proximity sensing has been employed in many scenarios, including identification of the user's proximity to museum collection [31], and gallery art pieces [32]. 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

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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.

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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

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“…In this study, the scan window could be enlarged, and the scan intervals could be shortened. Ng et al [25] analyzed the device density by using a small scan interval and adjusted the scan interval more adaptively through spontaneous differential evolution. Maciej et al [38] proposed a method to terminate the shortterm broadcast operation of the broadcast node by using the scan response packet of the active scan in the large-scale voting environment, so as to reduce the broadcast collision in large-scale application scenarios.…”

Section: Dense Iot Environmental Collisions 1) Analysis Of Collision Factorsmentioning

confidence: 99%

“…Next we investigate advertising collisions in dense environments. The existing solutions focus on dynamic intervals [21], [23]- [25] and random backoffs [26]- [28].…”

Section: Introductionmentioning

confidence: 99%

Anti-Collision Voting Based on Bluetooth Low Energy Improvement for the Ultra-Dense Edge

Dong-Hoon

Cao

et al. 2021

IEEE Access

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Due to the popularity of short-range communications and the ultra-dense deployment of the Internet of Things (IoT), the occurrence of severe signal collision in crowded nodes is becoming increasingly critical. We propose a scheme called anti-collision voting based on Bluetooth low energy improvement for the ultra-dense edge, in which one topic can be identified and separated from the others to enable intensive voting. The scheme alleviates the exponential growth of advertising collisions and achieves efficient consensus between the devices and humans by localized the message advertising. The previous proposals focused on enhancing the hardware and optimizing the parameters to establish a connection for interaction. Considering the limitation of connective devices, our scheme adopts a novel advertising model in which the nodes can promptly exit the advertising state by actively responding. In addition, the scheme merges active responses into a group to notify the advertising nodes and randomly chooses a channel to reduce redundancy and save energy. The simulations demonstrate that the scheme can reduce the collisions by more than 90% and channel occupancy by half, while doubling the capacity and efficiently supporting intensive voting within edge networks.

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“…The best results showed a positioning error under 2.6 m in a dense beacon network. In [17], the authors utilize beacons to design a proximity detection system. They stress the effects of interference between beacons in a dense network.…”

Section: Related Workmentioning

confidence: 99%

“…Beacons can operate on coin cell batteries or even batteryless [15], [16]. Many contextaware applications are using beacons, such as Proximity-Based Services (PBS) [17], [18] and Location-Based Services (LBS) [19]- [27], especially in GPS-less environments. Both services might use similar infrastructures, like beacons, however, they have different underlying operational logic.…”

Section: Introductionmentioning

confidence: 99%

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

Mackey

Spachos

Song

et al. 2020

IEEE Internet Things J.

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The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application such as Proximity-Based Services (PBS), and Location-Based Services (LBS). For these systems to perform, it is essential to have reliable hardware and predict a user's position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize Received Signal Strength Indicators (RSSI) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this work, Bluetooth Low Energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30% in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

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High Resolution Beacon-Based Proximity Detection for Dense Deployment

Cited by 35 publications

References 43 publications

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

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

Anti-Collision Voting Based on Bluetooth Low Energy Improvement for the Ultra-Dense Edge

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

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