| 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 |
| 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. |
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