1 research outputs found
Collaborative Data Acquisition
We consider a requester who acquires a set of data (e.g. images) that is not
owned by one party. In order to collect as many data as possible, crowdsourcing
mechanisms have been widely used to seek help from the crowd. However, existing
mechanisms rely on third-party platforms, and the workers from these platforms
are not necessarily helpful and redundant data are also not properly handled.
To combat this problem, we propose a novel crowdsourcing mechanism based on
social networks, where the rewards of the workers are calculated by information
entropy and a modified Shapley value. This mechanism incentivizes the workers
from the network to not only provide all data they have but also further invite
their neighbours to offer more data. Eventually, the mechanism is able to
acquire all data from all workers on the network and the requester's cost is no
more than the value of the data acquired. The experiments show that our
mechanism outperforms traditional crowdsourcing mechanisms