| Title |
Anomaly Detection on IOT Data for Smart City |
| ID_Doc |
39926 |
| Authors |
Bellini, P; Cenni, D; Nesi, P; Soderi, M |
| Title |
Anomaly Detection on IOT Data for Smart City |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/SMARTCOMP50058.2020.00087 |
| Abstract |
Smart Cities are probably on the more complex environment for IOT data collection. IOT data could have different producers, sample rates, periodic and aperiodic, typical trends, structures and stacks, faults, etc. Thus, a strongly flexible and scalable solution is needed to avoid investing huge amount of resources in anomaly detection that has to be done in real time and has to be agnostic to the above-mentioned problems. This paper presents a solution for automatic detection of anomalies. The proposed approach scales seamlessly and integrates in different contexts, featuring different sensor types, protocols, and data formats, and computationally cheap. The research has been developed in the context of Snap4City PCP Select4Cities project and is presently implemented in the Https://www.snap4city.org solution adopted in several cities and regions. |
| Author Keywords |
anomaly detection; sentient cities; IoT; smart cities; machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000853032900067 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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