4.8 Article

Incentivizing Platform-User Interactions for Crowdsensing

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 10, 页码 8314-8327

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3044731

关键词

Auction model; crowdsensing; incentive mechanism

资金

  1. NSF of China [61872447, 62072303]
  2. Natural Science Foundation of Chongqing [CSTC2018JCYJA1879]

向作者/读者索取更多资源

In order to incentivize interactions between platforms and users in crowdsensing, a mechanism called Picasso was designed to achieve personalized bidding for users with efficiency and minimal social costs. By representing bids in a 3D expression space and balancing expressiveness, computation complexity, and description efficiency, Picasso provides a win-win solution for platform-user interactions. Additionally, Picasso enables more tasks bidding for users while reducing the platform owner's payment, thus encouraging more participation.
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.

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