A method of neighborhood data fusion in decentralized anomaly detection is proposed.
The effects of neighborhood size and spatio-temporal correlation are explored.
Performance increases when the system is deployed in a correlated environment.
Fusing small neighborhoods is preferred over larger neighborhoods.