Smart devices and low-powered sensors are becoming increasingly ubiquitous and nowadays everything is connected, which is a promising foundation for crowdsensing of data related to various environmental phenomena. Resulting
data is especially meaningful when it is related to time and location. Interestingly, many existing approaches built their solution on monolithic backends that process data on a per-request basis. However, for many scenarios, such technical setting is not suitable for managing data requests of a large crowd. For example, when dealing with millions of data points, still many
challenges arise for modern smartphones if calculations or advanced visualization features must be accomplished directly on the smartphone. Therefore, we realized an architectural design for managing geospatial data of tinnitus patients, which combines a cloud-native approach with Big Data concepts used in the Internet of Things. The architectural design shall serve
as a generic foundation to implement (1) a scalable backend for a platform that covers the aforementioned crowdsensing requirements as well as to provide (2) a sophisticated stream processing concept to calculate and pre-aggregate incoming measurement data. Following this, a visualization feature was
realized to provide users with a comprehensive overview of noise levels in their environment based on noise measurements. This shall help tinnitus or hearing-impaired patients to avoid locations with a burdensome sound level.

Regard the corresponding publication available at:
dbis.eprints.uni-ulm.de/1783/