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Abstract There exist many natural phenomena where direct measurement is either impossible or extremely invasive. To obtain approximate measurements of these phenomena we can build prediction models based on other sensing modalities such as features extracted from data collected by an imager. These models are derived from controlled experiments performed under laboratory conditions, and can then be applied to the associated event in nature. In this paper we explore various different methods for generating such models and discuss their accuracy, robustness, and computational complexity. Given sufficiently computationally simple models, we can eventually push their computation down towards the sensor nodes themselves to reduce the amount of data required to both flow through the network and be stored in a database. The addition of these models turn in-situ imagers into powerful biological sensors, and image databases into useful records of biological activity.

Resources Slides (Power Point) (1.8MB) Slides (PDF) (4.9MB) Paper (PDF) (1.2MB) Poster (PDF) (659KB)

The workshop [1] will focus on all important aspects of sensor data management: from data processing in networks of remote, wireless, resource-constrained sensors to managing heterogeneous, noisy, and sometimes sensitive sensor data in databases. The resource-constrained, lossy, noisy, distributed, and remote nature of sensor networks means that traditional database techniques often cannot be applied without significant re-tooling. Challenges associated with acquiring and processing large-scale, heterogeneous sets of live sensor data also call for novel data management techniques. Finally, in many applications, collecting sensor data raises important privacy and security concerns that require new protection and anonymization techniques.

[1] DMSN 2007