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.

https://www.mdpi.com/2306-5729/5/4/87/pdf

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

The detection and positioning of unmanned aerial vehicles has become essential for both automation and surveillance tasks, in recent years. The design of accurate drone localization systems is challenging, especially in cluttered environments, where the target may be partially or even completely obscured. This paper proposes a precise detection and 3D localization system for drones, by means of a millimetre wave radar. Drone locations are estimated from spatial heatmaps of the received radar signals, which are obtained by applying the super-resolution MUSIC algorithm. These estimates are improved by analysis of the micro-Doppler effect, generated by the rotating propellers, which aids detection in poor visibility conditions. A novel Gaussian Process Regression model is developed, in order to compensate for systematic biases in the radar data. The complete system produces accurate estimates of the target range and direction, and is shown to outperform direct spectral analysis methods.

A Gaussian Process model for UAV localization using millimetre wave radar

In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.

/pdf/a-path-planning-model-for-stock-inventory-using-a-drone-2o4xed91.pdf

A Path Planning Model for Stock Inventory Using a Drone

Performance analysis of fingerprinting indoor positioning methods with BLE

Accurate remote determination of the object coordinates in 3D space is one of the main questions in many applications. In one of the most popular methods, such determination of the location of an object uses the measurement by receiving an electromagnetic signal transmitted by several spatially distributed base stations (BS). The main problem is that it is necessary to reduce errors and computation time. To overcome these difficulties, an analytical method for determining the position of an object based on the analysis of time difference of arrival (TDoA) of signals from the transmitter of the object to the receivers of the BS is proposed. One of the main advantages of this method is that it is possible to eliminate the ambiguity in determining the coordinates of the object in space and to increase the accuracy of determining the coordinates when the TDoA measurement between base stations fluctuates. Applications for autonomous automotive vehicles and spacebased positioning systems are analyzed. The results obtained show that the proposed algorithm has an accuracy of determining coordinates several times higher than the method of linearization of hyperbolic equations and is less sensitive to TDoA fluctuations at base stations.

https://www.mdpi.com/1424-8220/20/19/5472/pdf

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations.

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Nowadays the use of drones is rapidly growing, and the autonomous navigation capability depends on knowing their position at any time. The precision and robustness of a local positioning system for these devices is of particular importance in takeoff and landing maneuvers, especially in GPS-denied environments. The main contribution of this work lies in the development of a precise local positioning and tracking system for drones. For this purpose, a ToF camera has been installed on the ceiling in order to make use of its depth maps. Taking as a reference the disturbance caused by a quadrotor in one of these maps, a novel 2D matched filter has been designed based on a Gaussian wavelet. This filter allows the system to quickly detect all drones flying in the scene. Moreover, it is dynamically adapted to the image portion that they occupy, taking into account the variation of this parameter with their flying altitude, which has also been theoretically determined. The whole algorithm leads to a precise 3D drone positioning. In addition, the proposed system is also robust against short-time occlusions, since the measurements history can be used to predict future positions, thus avoiding the complete loss of tracking.

Precise drone location and tracking by adaptive matched filtering from a top-view ToF camera

To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of the gait speed may be key to enable early detection of cognitive diseases such as dementia or Alzheimer’s disease. Wearable technologies can provide innovative solutions for healthcare problems. Bluetooth Low Energy (BLE) technology is excellent for wearables because it is very energy efficient, secure, and inexpensive. In this paper, the BLE-GSpeed database is presented. The dataset is composed of several BLE RSSI measurements obtained while users were walking at a constant speed along a corridor. Moreover, a set of experiments using a baseline algorithm to estimate the gait speed are also presented to provide baseline results to the research community.

https://www.mdpi.com/2306-5729/5/4/115/pdf

BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed

In the near future, as a consequence of the increasing percentage of elder people with respect to the total population, developed countries will face significant stress on their healthcare systems. The onset of some diseases associated with aging, such as dementia or cognitive decline, has been associated with a number of factors that can be detected in advance, thus offering the possibility of an early intervention to delay their onset. Specifically, several studies have shown a correlation between slower gait speed and serious cognitive diseases such as Alzheimer’s disease. In this work we present a method, based on Bluetooth Low Energy (BLE), capable of detecting the user’s walking speed in a continuous and non-invasive way, providing a useful tool for the monitoring and early detection of this type of diseases. The proposed method estimates the gait speed with an average error of less than 10 cm/s, and is capable of providing continuous non-invasive monitoring of the gait speed of users while they conduct their usual life routines, without any additional requirements other than wearing a smartwatch or an activity band with inertial sensors and BLE capabilities.

/pdf/continuous-non-invasive-assessment-of-gait-speed-through-6uvooo5k.pdf

Fernando J. Aranda

Papers

Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Precise drone location and tracking by adaptive matched filtering from a top-view ToF camera

Performance analysis of fingerprinting indoor positioning methods with BLE

BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed

Continuous Non-Invasive Assessment of Gait Speed Through Bluetooth Low Energy