Poster abstract: Effectively modeling data from large-area community sensor networks

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 OpenSense1 project, we advocate an adaptive model-cover driven strategy towards effectively managing such data. Our strategy is designed considering the fundamental principles of LCSNs. We describe an adaptive approach, called adaptive k-means, and report preliminary results on how it compares with the traditional grid-based approach towards modeling LCSN data. We find that our approach performs better to model the sensed phenomenon in spatial and temporal dimensions. Our results are based on two real datasets.