An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings

Authors

  • Alexandros Zenonos University of Southampton
  • Sebastian Stein University of Southampton
  • Nicholas Jennings University of Southampton

DOI:

https://doi.org/10.1609/aaai.v30i1.9903

Keywords:

Participatory sensing, Coordination, Gaussian processes, Emissions, Air quality, Human mobility, Dynamic environments

Abstract

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.

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Published

2016-03-05

How to Cite

Zenonos, A., Stein, S., & Jennings, N. (2016). An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9903

Issue

Section

Special Track: Computational Sustainability