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”
Recent research provides a detailed survey on Bluetooth indoor localisation. The paper underscores the importance of indoor localisation and the unique challenges it presents, such as the inability of GPS to function indoors. There’s an overview of the types of localisation methods, including triangulation, scene analysis and proximity, as well as the metrics used in … Continue reading “Survey of Bluetooth Indoor Localisation”
New research Simplified Indoor Localisation Using Bluetooth Beacons and Received Signal Strength (RSSI) Fingerprinting with Smartwatch, introduces an innovative system for indoor localisation using Bluetooth Low Energy beacons and smartwatches, aimed at simplifying the process for users. This system is designed to detect a user’s location within specific areas like rooms within a house, rather … Continue reading “Enhancing Indoor Localisation for Ambient Assisted Living”
Minew has recently launched two new beacon models, the MWC01 and MBT02, a new type of product that is a repeater. These repeaters are designed to scan for beacons and re-transmit the strongest signal, providing a new approach to locating, and a way of extending Bluetooth communication. The intended use of these repeaters isn’t entirely … Continue reading “Minew’s New MWC01 and MBT02 Repeater Beacons”
Analysing sensor data from Bluetooth beacons using machine learning involves several steps, from data collection and preprocessing to model development and deployment. Here’s an overview of the process: Step 1: Data CollectionThe first step in analysing sensor data from Bluetooth beacons is to collect the data. Beacons continuously broadcast data such as unique identifiers, signal … Continue reading “Analysing Sensor Data From Bluetooth Beacons Using AI Machine Learning”
The new paper titled A Mobile App-based Indoor Mobility Detection Approach using Bluetooth Signal Strength (PDF) by Muztaba Fuad, Anthony Smith and Debzani Deb from Winston-Salem State University, explores the development and application of a novel system for detecting indoor mobility patterns using the Bluetooth signal strength from mobile devices. This research is significant for … Continue reading “App-Based Indoor Mobility Detection”