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Efficient processing of geospatial mHealth data using a scalable crowdsensing platform

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-207826
  • 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 technicalSmart 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.zeige mehrzeige weniger

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Autor(en): Robin Kraft, Ferdinand Birk, Manfred Reichert, Aniruddha Deshpande, Winfried Schlee, Berthold Langguth, Harald Baumeister, Thomas Probst, Myra Spiliopoulou, Rüdiger Pryss
URN:urn:nbn:de:bvb:20-opus-207826
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):Sensors
ISSN:1424-8220
Erscheinungsjahr:2020
Band / Jahrgang:20
Heft / Ausgabe:12
Aufsatznummer:3456
Originalveröffentlichung / Quelle:Sensors (2020) 20:12, 3456. https://doi.org/10.3390/s20123456
DOI:https://doi.org/10.3390/s20123456
Allgemeine fachliche Zuordnung (DDC-Klassifikation):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Freie Schlagwort(e):architectural design; cloud-native; crowdsensing; geospatial data; mHealth; scalability; stream processing; tinnitus
Datum der Freischaltung:09.06.2022
Datum der Erstveröffentlichung:18.06.2020
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International