Anomaly Detection in Streaming Sensor Data

TL;DR

This paper explores anomaly detection methods in streaming cell phone sensor data, focusing on a decision support system for emergency response and addressing privacy and security concerns.

Contribution

It introduces a framework for detecting anomalies in streaming sensor data from cell phones and discusses three specific detection methods within a decision support system.

Findings

Implemented three anomaly detection methods on cell phone data

Demonstrated the system's potential for emergency response support

Addressed privacy and security issues in sensor data handling

Abstract

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.