Effectively managing the data generated by Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. One important step for managing and querying such sensor network data is to create abstractions of the data in the form of models. These models can then be stored, retrieved, and queried as required. In our OpenSense project, we advocate an adaptive model-cover driven strategy towards effectively managing such data. Our strategy is designed considering the fundamental principles of LCSNs.

Effectively managing the data generated by community-driven mobile geo-sensor networks is a new and challenging problem. One important step for managing and querying sensor network data is to create abstractions of the data in the form of models. These models can then be stored, retrieved, and queried, as required. There has been significant amount of prior literature on using models for query processing [1]-[5]. On the contrary, however, there has been a lack of understanding on developing reliable models, considering the unique characteristics of community-driven geo-sensor networks. In an effort to correct this situation, this paper proposes various approaches for modeling the data from a community-driven mobile geo-sensor network. This data is typically collected over a large geographical area with mobile sensors having uncontrolled or semi-controlled mobility. Therefore, we propose adaptive techniques that take into account such mobility patterns and produce an accurate representation of the sensed spatiotemporal phenomenon. To substantiate our proposals, we perform extensive evaluation of our methods on two real datasets.