Pregled bibliografske jedinice broj: 755605
Real Time Analysis of Sensor Data for the Internet of Things by means of Clustering and Event Processing
Real Time Analysis of Sensor Data for the Internet of Things by means of Clustering and Event Processing // Proceedings of 2015 IEEE International Conference on Communications
London, Ujedinjeno Kraljevstvo: Institute of Electrical and Electronics Engineers (IEEE), 2015. str. 2288-2294 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 755605 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Real Time Analysis of Sensor Data for the Internet of Things by means of Clustering and Event Processing
Autori
Hromic, Hugo ; Serrano, Martin ; Hayes, Conor ; Antonic, Aleksandar ; Podnar Žarko, Ivana ; Le Phuoc, Danh ; Decker, Stefan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 2015 IEEE International Conference on Communications
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2015, 2288-2294
Skup
2015 IEEE International Conference on Communications (ICC2015)
Mjesto i datum
London, Ujedinjeno Kraljevstvo, 08.06.2015. - 12.06.2015
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Cloud Computing ; Interoperability ; Linked Data ; Intelligent Server ; Sensor Data ; Services ; Applications
Sažetak
Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics for the Internet of Things by using the OpenIoT1 middleware ; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ projekt 8065 (HUTS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
