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Energies 2017, 10(7), 929;

Stochastic Navigation in Smart Cities

BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA
Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
StageMotion, R&D Department, C/Orfebres 10, 34005 Palencia, Spain
Authors to whom correspondence should be addressed.
Academic Editor: Michael Gerard Pecht
Received: 18 April 2017 / Revised: 9 June 2017 / Accepted: 30 June 2017 / Published: 5 July 2017
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In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies. View Full-Text
Keywords: electric vehicle routing; charging stations; bio-inspired algorithm; stochastic process; smart cities electric vehicle routing; charging stations; bio-inspired algorithm; stochastic process; smart cities

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Martín García, R.; Prieto-Castrillo, F.; Villarrubia González, G.; Prieto Tejedor, J.; Corchado, J.M. Stochastic Navigation in Smart Cities. Energies 2017, 10, 929.

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