Robust State Prediction with Incomplete and Noisy Measurements in Collaborative Sensing

Collaborative sensing has emerged as a new sensing paradigm where sensors collaboratively report measurements about the phenomena or events in the physical world. This paper focuses on a robust state prediction problem in collaborative sensing where the goal is to provide an accurate prediction on the dynamic status of the physical environment (e.g., air quality index, temperature, traffic) based on the incomplete and noisy data contributed by collaborative sensors. While significant progress has been made to study the state prediction problem, we identify two fundamental challenges that have not been well addressed by the current literature. The first challenge is "latent spatial dynamics": the spatial correlations among measured variables are highly dynamic and the features that affect such dynamics may not be directly observable from the sensing data. The second challenge is "incomplete and noisy data": a significant amount of measurements are missing in collaborative sensing due to the prohibitively high cost of deploying sensors for full spatial-temporal coverage. The collaborative sensors are also known to be unreliable in many applications and can easily contribute to noisy measurements. To address these challenges, this paper develops a Context-Aware Collaborative Sensing Prediction (CACSP) scheme inspired by techniques from latent semantic analysis and statistics. We evaluate our scheme through two real-world collaborative sensing applications and the results show that CACSP significantly outperforms the state-of-the-art baselines.