A Distributed Anomaly Filtering Algorithm for Heterogeneous Data Based on City Computing

In modern cities, numerous urban perception devices collect and release urban data all the time, but urban data may become abnormal due to environmental interference or artificial tampering. In view of the problem that urban data will face data anomalies, this paper designs a distributed gauss membership anomaly data filtering algorithm, and defines a set of extraction protocols suitable for heterogeneous data. Simulation results show that this algorithm can filter abnormal data in real time, improve the efficiency of urban computing and reduce the cost of network.