CRAWLING: a Crowdsourcing Algorithm on Wheels for Smart Parking

TL;DR

CRAWLING is a novel crowdsourcing algorithm for smart parking that enables connected cars to collaboratively optimize routing by leveraging heterogeneous data streams and control-theoretic principles.

Contribution

It introduces a control-theoretic, crowdsourcing-based routing algorithm for connected cars, capable of handling stochastic behaviors and heterogeneous data sources.

Findings

Effective vehicle orchestration in simulations

Online reaction to road conditions

Minimized routing costs

Abstract

We present the principled design of CRAWLING: a CRowdsourcing Algorirthm on WheeLs for smart parkING. CRAWLING is an in-car service for the routing of connected cars. Specifically, cars equipped with our service are able to {\em crowdsource} data from third-parties, including other cars, pedestrians, smart sensors and social media, in order to fulfill a given routing task. CRAWLING relies on a solid control-theoretical formulation and the routes it computes are the solution of an optimal control problem where cars maximize a reward capturing environmental conditions while tracking some desired behavior. A key feature of our service is that it allows to consider stochastic behaviors, while taking into account streams of heterogeneous data. We propose a stand-alone, general-purpose, implementation of CRAWLING and we show its effectiveness on a set of scenarios aimed at illustrating all the key features of our service. Simulations show that, when cars are equipped with CRAWLING, the service effectively orchestrates the vehicles, making them able to react online to road conditions, minimizing their cost functions. The code implementing our service and to replicate the numerical results is made openly available.