| Title |
Scalable Anomaly Detection for Smart City Infrastructure Networks |
| ID_Doc |
39940 |
| Authors |
Difallah, DE; Cudré-Mauroux, P; McKenna, SA |
| Title |
Scalable Anomaly Detection for Smart City Infrastructure Networks |
| Year |
2013 |
| Published |
Ieee Internet Computing, 17, 6 |
| DOI |
10.1109/MIC.2013.84 |
| Abstract |
Dynamically detecting anomalies can be difficult in very large-scale infrastructure networks. The authors' approach addresses spatiotemporal anomaly detection in a smarter city context with large numbers of sensors deployed. They propose a scalable, hybrid Internet infrastructure for dynamically detecting potential anomalies in real time using stream processing. The infrastructure enables analytically inspecting and comparing anomalies globally using large-scale array processing. Deployed on a real pipe network topology of 1,891 nodes, this approach can effectively detect and characterize anomalies while minimizing the amount of data shared across the network. |
| Author Keywords |
Monitoring; Cities and towns; Internet; Sensors; Arrays; Real-time systems; Wireless sensor networks; array data processing; smart cities; water data management; sensor networks; stream processing |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000328945700006 |
| WoS Category |
Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
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