Incentivizing Platform-User Interactions for Crowdsensing

doi:10.1109/JIOT.2020.3044731

Incentivizing Platform-User Interactions for Crowdsensing

2021-05-15

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向朝参 (Chaocan Xiang) *

DOI：10.1109/JIOT.2020.3044731

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For effective crowdsensing, it is essential to incentivize the interactions of participants and platforms. Existing approaches do not tailor users' bidding to their preferences, i.e., personalized bidding (PB). To meet this need, we design an incentive mechanism, called Picasso, that achieves not only the expressiveness and description efficiency of PB for users, but also minimal social cost, computational efficiency, and strategy proof for platform owners. This design is, however, challenging due to the intrinsic conflicting goals of the platform owner and users. To handle these conflicts, Picasso represents bids in a novel 3-D expression space by orchestrating three logical operations to balance among expressiveness, computational complexity, and description efficiency. Moreover, we equivalently decompose and recombine the complex task dependencies of bids originated from the expressiveness of PB, thus achieving a constant-factor approximation of optimal task allocation with strategy proof in polynomial time. These properties of Picasso are proven theoretically. In addition to a detailed simulation study, our trace-driven evaluations show that, compared to existing approaches, Picasso can enable each user to bid 9.7x more tasks, on average, and decrease the description length by 74%, thus encouraging more users' participation. Picasso also reduces the platform owner's payment by more than 61%, hence yielding a win-win solution for incentivizing platform-user interactions.

Keywords:

Auction model

crowdsensing

incentive mechanism

Incentivizing Platform-User Interactions for Crowdsensing

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