Anomaly Detection in Streaming Sensor Data

In this chapter we consider a cell phone network as a set of automatically deployed sensors that records movement and interaction patterns of the population. We discuss methods for detecting anomalies in the streaming data produced by the cell phone network. We motivate this discussion by describing the Wireless Phone Based Emergency Response (WIPER) system, a proof-of-concept decision support system for emergency response managers. We also discuss some of the scientific work enabled by this type of sensor data and the related privacy issues. We describe scientific studies that use the cell phone data set and steps we have taken to ensure the security of the data. We describe the overall decision support system and discuss three methods of anomaly detection that we have applied to the data. Keywords Data clustering, data mining, data streams, emergency response, Markov Modulated 

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Alec Pawling, Ping Yan, Julián Candia, Timothy W. Schoenharl, Gregory R. Madey
• Keywords: Cell Phone, Cell Phone Network, CORR 2008, Education, Emergency Response