Optimal Incentive Design for Cloud-Enabled Multimedia Crowdsourcing

Multimedia crowdsourcing possesses a huge potential to actualize many new applications that are expected to yield tremendous benefits in diverse fields including environment monitoring, emergency rescues during natural catastrophes, online education, sports, and entertainment. Nonetheless, multimedia crowdsourcing unfolds new challenges such as big data acquisition and processing, more stringent quality of service requirements, and heterogeneity of crowdsensors. Consequently, incentive mechanisms specifically tailored to multimedia crowdsourcing applications need to be developed to fully utilize the potential of multimedia crowdsourcing. In this paper, we design an optimal incentive mechanism for the smartphone contributors to participate in a cloud-enabled multimedia crowdsourcing scheme. We establish a condition that determines whether the smartphones are eligible to participate, and provide a close form expression for the optimal duration of service from the contributors, for a given reward from the crowdsourcer. Consequently, we derive the conditions for existence of an optimal reward for the contributors from the crowdsourcer, and prove its uniqueness. We numerically illustrate the performance of our model considering logarithmic and linear cost functions for the cloud resources. The similarity of the results for different cost models corroborates the validity of our model and the results, whereas the difference in the magnitudes suggests that the strategy of the crowdsourcer as well as the strategies of the smartphone participants considerably depend on the cloud cost model.