Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Real-time Probabilistic Data Fusion for Large-scale IoT Applications
AU - Akbar, Adnan
AU - Kousiouris, George
AU - Pervaiz, Haris
AU - Sancho, Juan
AU - Ta-Shma, Paula
AU - Carrez, Francois
AU - Moessner, Klaus
PY - 2018/3/15
Y1 - 2018/3/15
N2 - Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.
AB - Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.
U2 - 10.1109/ACCESS.2018.2804623
DO - 10.1109/ACCESS.2018.2804623
M3 - Journal article
VL - 6
SP - 10015
EP - 10027
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -