Crowdsourcing Platform. This tool is to plan the use of an online crowdsourcing platform. Such platforms are used to host communities that respond to specific challenges by submitting insights, ideas, or business propositions for new products and services.
In several geo-sensing-oriented crowdsourcing applications, for example, traffic monitoring, air pollution measurements, and epidemic monitoring, the structure of the tasks in space or time exhibit a natural special ordering.
We model and analyze the problem of allocating a group of specially ordered crowdsourcing tasks, where it is natural for crowd workers to bid on a subset of contiguous tasks.
If the crowd workers are heterogeneous (that is, with different levels of quality) and strategic (that is, maximizing their own individual utilities), the problems of task allocation and payment determination to achieve social welfare and cost minimization become non-trivial and even impossible.
In this talk, we discuss the challenges involved in designing algorithmic mechanisms for such problems. The main contribution of our work is to propose a scalable approximation algorithm that runs in polynomial time and produces monotone allocations; we can provide theoretical guarantees on the approximation factor.
We combine this algorithm with an appropriate payment scheme to develop a truthful and individually rational mechanism for task allocation in such problems.
