In-route task selection in crowdsourcing

We consider a spatial crowdsourcing scenario where (1) a worker is traveling on a preferred/typical path within a road network where (2) there is a set of tasks, each associated with a positive reward, available to be performed and (3) that the worker is willing to possibly deviate from his/her preferred path to perform tasks as long as (4) he/she travels at most a total given distance/time. We name this the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain a set of diverse solutions yielding good combinations of detour and reward. Given the NP-hardness of the IRTS problem we present a heuristic approach that produces solutions with good values of precision and recall for problems of realistic sizes within practical query processing time.