Data-Driven Anomaly Detection Based on Multi-Sensor Data Fusion
- Resource Type
- Conference
- Authors
- Wang, Di; Al-Rubaie, Ahmad; Stincic, Sandra; Davies, John; Aljasmi, Alia
- Source
- 2021 International Conference on Smart Applications, Communications and Networking (SmartNets) Smart Applications, Communications and Networking (SmartNets), 2021 International Conference on. :1-8 Sep, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Correlation
Buildings
Data integration
Sensor systems
Real-time systems
Anomaly detection
Intelligent sensors
anomaly detection
data driven
multiple sources
data fusion
- Language
In the age of IoT, a huge amount of real time data is produced every second from the colossal number and different types of sensors deployed. A generic and intelligent method to monitor these large data streams from a wide range of sources without human supervision or the use of expert knowledge is a big challenge. In this paper we propose, develop, and test a generic method for anomaly detection which is completely data-driven without human supervision. The proposed method is able to detect the underlying correlations amongst multiple sensors and detect the data patterns from all correlated sensor data through time. Anomalies are detected from marginal deviations from the normal identified patterns. The proposed method is applied to Building Management System’s data which include various types of sensors and proves the generality of the proposed method.