iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
In a previous post we explained how the Received Signal Strength Indication (RSSI) can be used to infer distance. Contract tracing apps provided by governments and workplace social distancing solutions use the RSSI to detect close contact. This post explores some factors that affect how well such systems work. With Bluetooth LE the sender repeatedly … Continue reading “Bluetooth RSSI, Social Distancing and Contact Tracing”
Bluetooth LE can be used to infer distance as is being used in contact tracing and social distancing apps. This is performed from the receiving end using what’s called the Received Signal Strength Indication (RSSI). This is a number, in dBm units produced by the receiving Bluetooth hardware that gives the wireless signal strength. [dBm … Continue reading “Bluetooth LE Distance Determination Using RSSI”
There’s a recent paper by Hongji Cao,Yunjia Wang,Jingxue Bi and Hongxia Qi of China University of Mining and Technology on An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance. The paper looks into how to combine both Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP) to provide for an … Continue reading “Using Bluetooth and WiFi RSSI for Locating”
In our previous post on iBeacon Microlocation Accuracy we explained how distance can be inferred from the received signal strength indicator (RSSI). We also explained how techniques such as trilateration, calibration and angle of arrival (AoA) can be used to improve location accuracy. There’s new research presented at The 17th Annual International Conference on Mobile … Continue reading “Using AI Machine Learning on Bluetooth RSSI to Obtain Location”
RSSI is the signal strength at the Bluetooth receiver. The signal type, for example, iBeacon, Eddystone or sensor beacon is irrelevant. The value of the RSSI can be used to infer distance. The accuracy of the distance measurement depends on many factors such as the type of sending device used, the output power, the capability of … Continue reading “Obtaining Distance from RSSI”
Research on Bluetooth dataset for proximity detection in indoor environments collected with smartphones by Michele Girolami, Davide La Rosa, and Paolo Barsocchi, outlines the creation and details of a dataset aimed at enhancing proximity detection between people and points of interest (POIs) within indoor environments, particularly museums. This dataset is created from Bluetooth beacon data … Continue reading “Sample Bluetooth Beacon Museum Data Available”
A recent new paper, BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement, focuses on improving the performance of indoor localisation using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, specifically employing the Received Signal Strength Indicator (RSSI) for distance estimation. Different solutions to enhance this localisation technology’s performance are explored, … Continue reading “Anchor-based Bluetooth Low Energy (BLE) 5.0 Positioning”
The new paper Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities by Christina Garcia and Sozo Inoue from the Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Japan, presents a study on enhancing machine learning for indoor localisation in caregiving, specifically in nursing homes, using Bluetooth Low Energy … Continue reading “Indoor Locating Using Beacons in Nursing Care”
Bluetooth beacons are a powerful tool for creating personalised experiences through mobile apps. They work by transmitting signals that can be detected by all smartphones. When a user comes within range, the app detects the beacon’s signal and identifies the user’s proximity to a specific location. This enables the delivery of contextually relevant content, such … Continue reading “Using Beacons with Smartphone Apps”
Recent research A Novel Optimized iBeacon Localization Algorithm Modeling by Jiajia Shi et al, addresses the challenges in achieving high accuracy in indoor object localisation or tracking using iBeacon systems. These systems, which use Bluetooth sensors, are appealing due to their low cost and ease of setup but there can be challenges with accuracy and … Continue reading “Novel iBeacon Localisation Algorithm Modelling”