Geography Reference
In-Depth Information
Measuring Environmental Noise Data with Mobile Phones
Mobile Phones as a Sensor Network
Sensor data collection, which uses mobile phones as sensors, is technologically
based on mobile sensor networks. The difference between conventional cellular
networks and specialized sensor networks lies more or less on communication
technology that is used. The main advantage of sensor networks, consisting of
mobile phones, is their wide accessibility. Smart mobile phones have become the
easiest way, how to engage public in environmental data collection. Applications
enabling collecting of various sensor data can be distributed through centralized
servers and services, which offer applications designed for the particular smart
mobile phone
'
s operating system [e.g. Apple
2013
(iTunes), Google
2013
(Google
Play), etc.].
In environmental noise mapping, semi-mobile sensor network technology has
been successfully tested on a relatively large area of Madrid (Manvell et al.
2004
).
Technical feasibility of environmental noise mapping using mobile phones was
tested e.g. at Trinity College in Dublin (McDonald et al.
2008
). Projects aimed to
measure noise in an urban environment (D
Hondt et al.
2013
; Kanjo
2010
;N¨st
2013
) have also report generally positive results.
'
Mobile Phones as Noise Dosimeters
The main difference between noise mapping using mobile phones and classical
mapping approaches is the style of noise source classification. Current applications
for measuring noise with mobile phones use a person-centralized approach in which
the mobile phone is used as a noise dose meter, which indicates the overall noise
experienced by one particular person (equivalent to radiation dosimeters). The
noise level is, simultaneously with time and possibly position, recorded every
second (which corresponds to the “slow” regime of a standard sound meter).
This approach is considerably simpler in comparison with conventional mapping
of the spatial distribution of environmental noise. First of all, different noise sources
(e.g. transport, industry) are not differentiated, including noise created by user
himself. Also information about position is just secondary, does not have to be
provided, and is used rather for orientation purposes (e.g. in which environment was
this value measured). For this reason, NoiseTube application (Stevens
2012
) pro-
vides post-processing tool, which automatically assigns position (if measured) to
the nearest point on the nearest street. For information purposes such position
manipulations are adequate, but when studying the spatial distribution of noise, a
somewhat more sensitive approach should be chosen.
