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Route selection for mobile sensor nodes on public transport networks

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Abstract

The sensing range of a sensor is spatially limited. Thus, achieving a good coverage of a large area of interest requires installation of a huge number of sensors which is cost and labor intensive. For example, monitoring air pollution in a city needs a high density of measurement stations installed throughout the area of interest. As alternative, we install a smaller number of mobile sensing nodes on top of public transport vehicles that regularly traverse the city. In this paper, we consider the problem of selecting a subnetwork of a city’s public transport network to achieve a good coverage of the area of interest. In general case, public transport vehicles are not assigned to fix lines but rather to depots where they are parked overnight. We introduce an algorithm that selects the installation locations, i.e., number of vehicles within each host depot, such that sensing coverage is maximized. Since we are working with low-cost sensors, which exhibit failures and drift over time, vehicles selected for sensor installation have to be in each other’s vicinity from time to time to allow comparing sensor readings. We refer to such meeting points as checkpoints. Our algorithm optimizes sensing coverage while providing a sufficient number of checkpoint locations. We evaluate our algorithm based on the tram network of Zurich and show how an accurate selection of vehicles for installing measurement stations affects the overall system quality. We show that our algorithm outperforms random search, simulated annealing, and the greedy approach.

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Notes

  1. MiCS-OZ-47 from http://www.e2v.com, CO-AF from http://www.alphasense.com, DiSCmini from http://www.matter-aerosol.ch.

  2. The lines on the figure are re-numbered to have a sequential ordering of lines without gaps.

  3. http://www.bafu.admin.ch/luft/luftbelastung.

  4. http://www.ostluft.ch.

  5. We denote with \({\bf s}_{\bf f}\) a selector vector whose fth element is 1 and all other elements are 0, i.e., \(s_3^T = (0, 0, 1, 0, \ldots, 0). \)

  6. http://www.openstreetmap.org.

  7. http://www.zvv.ch.

  8. Pollution data and real-time tram locations are online at http://data.opensense.ethz.ch/position.html.

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Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments and suggestions. Further, we thank Christoph Walser for his technical support and VBZ for their support and collaboration. This work was funded by NanoTera.ch with Swiss Confederation financing.

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Authors and Affiliations

  1. Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland

    Olga Saukh, David Hasenfratz & Lothar Thiele

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  1. Olga Saukh
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  3. Lothar Thiele
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Correspondence to David Hasenfratz.

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Saukh, O., Hasenfratz, D. & Thiele, L. Route selection for mobile sensor nodes on public transport networks. J Ambient Intell Human Comput 5, 307–321 (2014). https://doi.org/10.1007/s12652-012-0170-7

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