My normal combination on the big-server side would be Apache + MySQL (or PostgreSQL), but on the RPi this seems to be absolute overkill. For data-logging operations I would not use the local system anyway (looking at MQTT as well as Remote MongoDB datastore via REST Webservices).

After some poking around and reading up on the options I decided to go for the following combo: LightHTTPD + SQLite. Both are lightweight replacement of their fully-featured big-server counterparts (Apache HTTP & MySQL) and have very familiar configurations. There would be other options that have even less resource usage, but I really don’t have the time to start from scratch somewhere.

To connect 1-wire serial devices to the RPi I am using a DS9490R USB 1-wire adapter (rather than wiring I2C 1-Wire master components to GPIO I2C – which I might look at sometime down the track)

Install packages

sudo apt-get install owfs ow-shell

Edit config file

vim /etc/owfs.conf

! server: server = localhost:4304
# USB device: DS9490
server: usb = all
######################### OWFS ##########################
mountpoint = /mnt/1wire
allow_other
####################### OWHTTPD #########################
http: port = 2121
####################### OWFTPD ##########################
ftp: port = 2120
####################### OWSERVER ########################
server: port = localhost:4304

Create Startup Script

I created a startup script for owfs modelled on the owserver script (not sure why this one is actually missing)

With the Internet of Things slowly becoming mainstream the potential uses of this technology can also be seen in the Education sector. This blogpost is the first installment of a series of posts that highlights practical examples that can be used in teaching and training.

Part 1 – Environmental Noise Monitoring

Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 Generic License (http://creativecommons.org/licenses/by-nc-nd/2.0/) by leeanderNoise pollution has been a serious problem in many large cities all over the world and with the help of common mobile devices (smartphones) this can be easily measured, monitored and compared with a large quantity of samples from other cities/regions.