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A fundamental problem in collaborative sensing lies in providing an accurateprediction of critical events (e.g., hazardous environmental condition, urbanabnormalities, economic trends). However, due to the resource constraints,collaborative sensing applications normally only collect measurements from asubset of physical locations and predict the measurements for the rest oflocations. This problem is referred to as sparse collaborative sensingprediction. In this poster, we present a novel closed-loop prediction model byleveraging topic modeling and online learning techniques. We evaluate ourscheme using a real-world collaborative sensing dataset. The initial resultsshow that our proposed scheme outperforms the state-of-the-art baselines.

Poster Abstract: A Dynamic Data-Driven Prediction Model for Sparse Collaborative Sensing Applications