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  - 
TY  - JOUR
A1  - Hossfeld, Tobias
A1  - Heegaard, Poul E.
A1  - Kellerer, Wolfgang
T1  - Comparing the scalability of communication networks and systems
JF  - IEEE Access
N2  - Scalability is often mentioned in literature, but a stringent definition is missing. In particular, there is no general scalability assessment which clearly indicates whether a system scales or not or whether a system scales better than another. The key contribution of this article is the definition of a scalability index (SI) which quantifies if a system scales in comparison to another system, a hypothetical system, e.g., linear system, or the theoretically optimal system. The suggested SI generalizes different metrics from literature, which are specialized cases of our SI. The primary target of our scalability framework is, however, benchmarking of two systems, which does not require any reference system. The SI is demonstrated and evaluated for different use cases, that are (1) the performance of an IoT load balancer depending on the system load, (2) the availability of a communication system depending on the size and structure of the network, (3) scalability comparison of different location selection mechanisms in fog computing with respect to delays and energy consumption; (4) comparison of time-sensitive networking (TSN) mechanisms in terms of efficiency and utilization. Finally, we discuss how to use and how not to use the SI and give recommendations and guidelines in practice. To the best of our knowledge, this is the first work which provides a general SI for the comparison and benchmarking of systems, which is the primary target of our scalability analysis.
KW  - communication networks
KW  - performance
KW  - availability
KW  - scalability
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349403
VL  - 11
ER  -