The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.
ER - TY - JOUR AU - Becker, Martin AU - Caminiti, Saverio AU - Fiorella, Donato AU - Francis, Louise AU - Gravino, Pietro AU - Haklay, Mordechai (Muki) AU - Hotho, Andreas AU - Loreto, Vittorio AU - Mueller, Juergen AU - Ricchiuti, Ferdinando AU - Servedio, Vito D. P. AU - Sîrbu, Alina AU - Tria, Francesca T1 - Awareness and Learning in Participatory Noise Sensing JO - PLOS ONE PY - 2013/12 VL - 8 IS - 12 SP - EP - UR - M3 - 10.1371/journal.pone.0081638 KW - 2013 KW - EveryAware KW - WideNoise KW - article KW - iteg KW - itegpub KW - l3s KW - sitc L1 - SN - N1 - N1 - AB - The development of ICT infrastructures has facilitated the emergenceof new paradigms for looking at society and the environment over
the last few years. Participatory environmental sensing, i.e. directly
involving citizens in environmental monitoring, is one example, which
is hoped to encourage learning and enhance awareness of environmental
issues. In this paper, an analysis of the behaviour of individuals
involved in noise sensing is presented. Citizens have been involved
in noise measuring activities through the WideNoise smartphone application.
This application has been designed to record both objective (noise
samples) and subjective (opinions, feelings) data. The application
has been open to be used freely by anyone and has been widely employed
worldwide. In addition, several test cases have been organised in
European countries. Based on the information submitted by users,
an analysis of emerging awareness and learning is performed. The
data show that changes in the way the environment is perceived after
repeated usage of the application do appear. Specifically, users
learn how to recognise different noise levels they are exposed to.
Additionally, the subjective data collected indicate an increased
user involvement in time and a categorisation effect between pleasant
and less pleasant environments. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM CY - PY - 2013/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - iteg KW - itegpub KW - l3s KW - myown KW - recommendation KW - rsweb KW - sensor KW - sitc KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER -