J. Y. Koh, G. W. Peters, D. Leong, I. Nevat and W. C. Wong, "Privacy-aware incentive mechanism for mobile crowd sensing," 2017 IEEE International Conference on Communications (ICC), Paris, France, 2017, pp. 1-6. doi: 10.1109/ICC.2017.7997418
Abstract:
Mobile crowd sensing is an emerging sensing paradigm where sensing applications buy sensor data from mobile smartphone users (workers) instead of deploying their own sensor networks to estimate some statistics of a spatial event. In many spatial monitoring applications, the crowdsourcer needs to incentivize smartphone users to contribute sensing data so that the collected dataset has good spatial coverage. To further incentivize privacy-concerned workers to contribute, we propose a Stackelberg incentive framework that allows workers to specify their location privacy requirements while also increasing the spatial coverage of the collected dataset. We then derive a unique Stackelberg equilibrium which demonstrates the stability of our approach. Our simulation results show that our approach is significantly better in terms of data utility than the non-location-aware and uniform-reward approaches.
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