Week 6 - Oct 09 - 15
Having set up everything to kickstart the project, this blog will cover up my further progress in moving the device (Nucleo-L476RG kit plus Case) "Into the traffic". This will be the second blog covering module 1. A glance over the previous week's blog #5 will help you in understanding trafficpredictor project progress better.
Module 1 - Traffic prediction with machine learning on an added advantage of mass storage capability of the STM32 Nucleo-64 development board
To be covered later/ in this blog,
App (Android / Web)
Code Snippets
MATLAB GUI
Explored Kit's Storage Capacity
App (Android)
It took me almost a day & more than 5GB internet data to complete Android studio SDK along with AVD installation. Meanwhile, I tried few online mobile apps and websites for android application development sites (These are restricted to basic layout modifications & fewer functionalities when compared to Android Studio). Out of the available websites to develop an android app, I found the following websites to be best:
Advantage - They had faster time from development to production plus more options and layouts. And overall they are FREE!
The main limitation I felt using Android Studio is, it uses most of the computer's RAM (Full capacity of the computer - 4GB). This results in freezing/ hanging off my computer due to 90+% RAM usage. This is severely impacting the application development process as and when I need to test the code I have to wait for 10 to 20 minutes to visualize the output in the Virtual Device Emulator. Credits to IObit's performance monitor which saved my computer from most of the hanging part with an option "Clean RAM" [Refer below screenshot - Broomstick icon].
One more option which I tried out was to reduce the RAM allotted to the AVD from and select Graphics to be Hardware. Refer below screenshot for steps which are given inside the screenshot.
If you have suggestions to make Android Studio work fluently without hanging while emulating the output, please do share in the comments section below.
[App is under development and details will be posted in the next blog...]
GPS Data
GPS data collected from the user has to be saved in a database for further processing (Machine Learning). I have planned to transfer data to Google spreadsheets which would be cost and time effective in the testing phase. After the successful test and on demand of huge traffic of data, it can be moved into a database ( preferably SQL/ DB2).
Challenging part - GPS & Wi-Fi
Being not aware of the process of extracting information from GPS satellites through GMAPS API and integrating it to work with the kit, it is really a bit challenging. Though initially, I felt connecting the kit to the internet will be easier, it becomes challenging as I progress.
- Wi-Fi Expansion Board - The Wi-Fi Expansion board X-NUCLEO-IDW01M1 comes with a handy instruction manual from the ST (Download it here). As per the manual, I set up the apache server on my PC and managed it through XAMPP. But I was not able to receive messages as instructed in the manual [Refer Screenshot below]. This can be due to the version difference, version from when they had prepared the document to the version available currently. This led me to a state where I am unable to ensure Wi-Fi expansion board setup is complete. Otherwise, things are good to go.
- GPS - Being the heart of the project I am still clueless to start things with GPS though I have an idea of how would things work.
How I overcome - Thank you DAB after your comment, I had some spark to work with. It is a bit challenging to begin with GPS, provided there is no sensor in the kit for directly receiving GPS data. Hence I have planned to kick things through the mobile app. I have now started to use GMAPS API and it is very interesting to work with.
Tutorials which helped me are GMAPS API for Android - by Google & the below tutorial in youtube
Progress made so far,
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| {tabbedtable} Tab Label | Tab Content |
|---|---|
| Entry & Introduction | IoT on Wheels Design Challenge - Traffic predictor and auto pilot mode |
| Plan | Traffic Predictor #3 - The Plan |
| Initial Setup | |
| Module 1 | Traffic Predictor #5 - Machine Learning and Building a case for the kit |
| Module 2 | Yet to Begin |
| Module 3 | Yet to Begin |
| Integration | Yet to Begin |
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