Generating a Heat Map Using iBeacons with an Android Application

Introduction

About a month ago, I had the opportunity to experiment with iBeacons on the Android platform. My initial task was to implement a proximity trigger for one of our Android apps at work. As we delved deeper into the project, we became curious about obtaining more precise data, such as generating a heat map.

Searching for Solutions

I began by searching for existing projects and apps that could generate a heat map based on actual distances from iBeacons. Unfortunately, I couldn’t find a precise solution in the Play Store. After visualizing the raw accuracy data on a graph (as shown in the image below), I discovered a helpful page that confirmed some of my findings. Here are my key observations:

  • The raw data from iBeacons is not as accurate as I initially expected.
  • Accuracy varies depending on the orientation of the iBeacon relative to the smartphone.
  • Beyond a certain distance, the margin of error increases significantly.
  • For more precise data, you need to set the transmit interval and transmit power to their maximum values, but that also leads to more battery consumption.

What I Did

To address these challenges, I developed an Android app designed to generate a real-time heat map. Given the accuracy limitations of iBeacons, I consider this a beta demo of an iBeacon heat map. The app allows users to assign colors to different iBeacons, set room dimensions, and choose from a few approximation methods. While the results are still not as accurate as I’d like, the simplest method—averaging the last 30 values—has produced the most acceptable results so far.

Technical Details

For this project, I used IBKS 105 iBeacons from the Spanish vendor Accent Systems, with both the TX power and transmit interval set to their maximum available values. The app uses the Bluetooth address as a unique identifier, eliminating the need to set UUID, major, or minor values.



Github: https://github.com/svab-m/ibeacon_heatmap_demo
Google Play: https://play.google.com/store/apps/details?id=sk.svb.ibeacon.heatmap


     

Comments

Post a Comment

Popular posts from this blog

Skate Tricks Recognition Using Gyroscope

Image
In this article, we’ll describe how to recognize skateboard tricks using a gyroscope. This sensor is already present in most smartphones, but if you’re not familiar with it, here is a description . Before we dive in, a small disclaimer: This project originated as part of a Slido hackathon with the goal of demonstrating that it’s possible to recognize skateboard tricks using a gyroscope. For simplicity, we’ll focus on recognizing just two basic tricks and won’t consider the skater’s stance on the board. We’ll break down the problem into several smaller tasks: recording the trick, storing the data, describing the trick, analyzing it, and, finally, recognizing it. Recording the Trick Let’s start with trick recording. To obtain precise data, the device with the gyroscope needs to be attached directly to the skateboard. Place it on the bottom of the board to avoid interfering with the execution of the trick. The device should also be compact and lightweight to avoid altering the board’s cen...
Read more

Play table

Image
Using proximity sensors for playing midi tones combined with LED visualization Description The goal of this project was to create a table-sized device equipped with multiple proximity sensors capable of playing MIDI tones. Each sensor is accompanied by LEDs that indicate the distance of the user's hand above the table. This interactive table can be used by one or more people simultaneously. Hardware Setup I began with a cardboard prototype to test the sensors and the underlying logic. Afterward, I ordered a custom plottered sticker with a design which was painted with bare conductive paint. I drilled holes and connected the touchboard to seven Arduino Nanos, each controlling 13 LEDs. Additionally, I incorporated two potentiometers: one for volume control and another for changing the note setup. Programming The programming task was divided into two parts: Master Program (Touchboard): Reads values from the proximity sensors and sends messages to the slave Arduinos. Sl...
Read more

Counting dice and train wagons using computer vision

Image
Computer vision exercises with preprocessing Before starting my next project, I decided to work on some computer vision exercises. Each example is based on straightforward image preprocessing techniques. No complex data structures or machine learning are involved. Dice Detection I got this idea while browsing the net and became curious about how challenging it would be to write such a script. Here’s a step-by-step breakdown of the algorithm: Movement Detection : By comparing several frames with thresholds, we can determine if there is any movement in the frame. Adding a small time buffer after the movement stops gives us more accurate information. Removing the Background : Thresholding the grayscale frame helps to remove the background, leaving us with a binary image that highlights the objects. Cropping the Objects : Using contours, we can detect and isolate the objects by cropping them. Detecting Dots : By inverting the image, the dots on the dice become more distinguish...
Read more