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International Conference on Computational Logistics

ICCL 2018: Computational Logistics pp 331–343Cite as

Adapting the A* Algorithm to Increase Vehicular Crowd-Sensing Coverage

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11184))

Abstract

Current vehicles are incorporating an even wider number of environmental sensors, mainly needed to improve safety, efficiency and quality of life for passengers. These sensors bring a high potential to significantly contribute also to urban surveillance for Smart Cities by leveraging opportunistic crowd-sensing approaches. In this context, the achievable spatio-temporal sensing coverage is an issue that requires more investigations, since usually vehicles are not uniformly distributed over the road network, as drivers mostly select a shortest time path to destination. In this paper we present an evolution of the standard \(\mathbf A \varvec{^{*}}\) algorithm to enhance vehicular crowd-sensing coverage. In particular, with our solution, the route is chosen in a probabilistic way, among all those satisfying a constraint on the total length of the path. The proposed algorithm has been empirically evaluated by means of a public dataset of real taxi trajectories, showing promising performances in terms of achievable sensing coverage.

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Author information

Authors and Affiliations

  1. Department of Electrical and Telecommunications Engineering, University of Naples Federico II, Naples, Italy

    Sergio Di Martino & Dario Asprone

  2. Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, Italy

    Paola Festa

Authors
  1. Sergio Di Martino
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  2. Paola Festa
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  3. Dario Asprone
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Corresponding author

Correspondence to Sergio Di Martino .

Editor information

Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Raffaele Cerulli

  2. University of Salerno, Fisciano, Italy

    Andrea Raiconi

  3. Institute of Information Systems, University of Hamburg, Hamburg, Germany

    Stefan Voß

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Di Martino, S., Festa, P., Asprone, D. (2018). Adapting the A* Algorithm to Increase Vehicular Crowd-Sensing Coverage. In: Cerulli, R., Raiconi, A., Voß, S. (eds) Computational Logistics. ICCL 2018. Lecture Notes in Computer Science(), vol 11184. Springer, Cham. https://doi.org/10.1007/978-3-030-00898-7_22

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  • DOI: https://doi.org/10.1007/978-3-030-00898-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00897-0

  • Online ISBN: 978-3-030-00898-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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