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In recent years, innovative roadside parking vacancy crowdsensing solutionshave emerged as a cost-effective alternative to traditional methods, which cansignificantly reduce sensor installation and maintenance expenses. Thiscrowdsensing scheme relies on vehicles equipped with sensors, such as buses andtaxis, roaming around urban streets to detect on-street parking availability.Therefore, the accuracy of this scheme strongly depends on the vehicles' routesand the frequency of their passage through parking spots. This paper presentsan integer programming-based optimal sensor allocation model to ensure thedetection accuracy of the scheme while using the minimum number of sensing kitsor probing vehicles. Moreover, a customized heuristic algorithm is proposed tohasten the solution process. Numerical simulations using the street datasetfrom San Francisco confirm the model's ability to reduce probing vehicle usagewhile ensuring detection accuracy. Thus, our approach represents an effectivemeans of optimizing roadside parking detection in a crowdsensing way.

Minimizing Sensor Allocation Cost for Crowdsensing On-street Parking Availability