Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice

A Concise Route to Isocanthin-6-one

An efficient, four-step route to isocanthin-6-one (4) is reported. The key step is an intramolecular hetero Diels-Alder reaction

Intramolecular Hetero Diels–Alder Routes to γ-Carboline Alkaloids

Concise and efficient routes to the carboline alkaloids isocanthine (3), isocanthin-6-one (4), 1-methylisocanthine (5), and 1-methylisocanthin-6-one (6) are reported. In each case, the key synthetic step was an intramolecular hetero Diels–Alder reaction of a 1-aza-1,3-diene with an acetylenic dienophile

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