  One important problem in crowdsourcing is that of assigning tasks to workers.
We consider a scenario where a worker is traveling on a preferred/typical path
(e.g., from school to home) and there is a set of tasks available to be
performed. Furthermore, we assume that: each task yields a positive reward, the
worker has the skills necessary to perform all available tasks and he/she is
willing to possibly deviate from his/her preferred path as long as he/she
travels at most a total given distance/time. We call this problem the In-Route
Task Selection (IRTS) problem and investigate it using the skyline paradigm in
order to obtain the exact set of non-dominated solutions, i.e., good and
diverse solutions yielding different combinations of smaller or larger rewards
while traveling more or less. This is a practically relevant problem as it
empowers the worker as he/she can decide, in real time, which tasks suit
his/her needs and/or availability better. After showing that the IRTS problem
is NP-hard, we propose an exact (but expensive) solution and a few others
practical heuristic solutions. While the exact solution is suitable only for
reasonably small IRTS instances, the heuristic solutions can produce solutions
with good values of precision and recall for problems of realistic sizes within
practical, in fact most often sub-second, query processing time.
