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

  • Sergio Di MartinoEmail author
  • Paola Festa
  • Dario Asprone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)


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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sergio Di Martino
    • 1
    Email author
  • Paola Festa
    • 2
  • Dario Asprone
    • 1
  1. 1.Department of Electrical and Telecommunications EngineeringUniversity of Naples Federico IINaplesItaly
  2. 2.Department of Mathematics and Applications “R. Caccioppoli”University of Naples Federico IINaplesItaly

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