Collaborative Data Acquisition
This addresses data collection inefficiencies for requesters in crowdsourcing, though it is incremental as it builds on existing mechanisms with social network integration.
The paper tackles the problem of inefficient data acquisition in crowdsourcing by proposing a novel mechanism based on social networks, which incentivizes workers to provide all their data and recruit others, resulting in acquiring all data at a cost no more than its value and outperforming traditional methods.
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.