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

TL;DR

This paper introduces a novel dynamic prediction model for sparse collaborative sensing that leverages topic modeling and online learning, demonstrating improved accuracy over existing methods on real-world data.

Contribution

The paper presents a new closed-loop prediction framework combining topic modeling and online learning for sparse collaborative sensing applications.

Findings

Outperforms state-of-the-art baselines

Effective on real-world datasets

Improves prediction accuracy

Abstract

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