In this paper we develop a methodology for defining stopping rules in a general class of global random search algorithms that are based on the use of statistical procedures. To build these stopping rules we reach a compromise between the expected increase in precision of the statistical procedures and the expected waiting time for this increase in precision to occur.

We study a new random search process: the taxi drive. The motivation for this process comes from urban sensing in which sensors are mounted on moving vehicles such as taxis, allowing environments to be opportunistically monitored. Inspired by movements of real drive is composed both and regular parts: passengers brought randomly chosen locations via deterministic (i.e. shortest paths) routes. s...