TY  - JOUR
A1  - Kraft, Robin
A1  - Birk, Ferdinand
A1  - Reichert, Manfred
A1  - Deshpande, Aniruddha
A1  - Schlee, Winfried
A1  - Langguth, Berthold
A1  - Baumeister, Harald
A1  - Probst, Thomas
A1  - Spiliopoulou, Myra
A1  - Pryss, Rüdiger
T1  - Efficient processing of geospatial mHealth data using a scalable crowdsensing platform
JF  - Sensors
N2  - Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
KW  - mHealth
KW  - crowdsensing
KW  - tinnitus
KW  - geospatial data
KW  - cloud-native
KW  - stream processing
KW  - scalability
KW  - architectural design
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207826
SN  - 1424-8220
VL  - 20
IS  - 12
ER  -