On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
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This paper surveys sensing assessment solutions from the literature with a particular focus on techniques which can be used in unknown environments, including the following: sensor fault detection and identification (FDI), sensor or source evaluation, and isolating poorly sensed regions. Each approach is evaluated in terms of its ability to perform sensing assessment tasks in unknown environments and its coverage of the range of potential sensing problems. These tasks include sensing problem detection and characterization, as well as performance evaluation (e.g., estimating accuracy or reliability), for a sensor or group of sensors. This survey shows that over 40 existing approaches are focused on either detection and identification of traditional sensor faults (e.g., drift or physical damage) in known environments or evaluation of the reliability of a source (e.g., sensor or agent). Only eight approaches surveyed have tackled environment-dependent problems (e.g., exteroceptive sensor FDI, miscalibration, or use of an inappropriate sensor) in a useful manner for unknown environments. Even less work (two studies) appears to have been done on isolating poorly sensed regions. The survey concludes with a list of opportunities for future research, including developing methods for detecting and characterizing environment-dependent problems and creating comprehensive sensing assessment systems.