While prior studies have designed incentive mechanisms to attract the public to share their collected data, they tend to ignore information asymmetry between data requesters and collectors. In reality, the sensing costs information (time cost, battery drainage, bandwidth occupation of mobile devices, and so on) is the private information of collectors, which is unknown by the data requester. In this article, we model the strategic interactions between health-data requester and collectors using a bilevel optimization model. Considering that the crowdsensing market is open and the participants are equal, we propose a Walrasian equilibrium-based pricing mechanism to coordinate the interest conflicts between health-data requesters and collectors. Specifically, based on the exchange economic theory, we transform the bilevel optimization problem into a social welfare maximization problem with the constraint condition that the balance between supply and demand, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by health-data requesters and collectors. We prove that the optimal task price is equal to the marginal utility generated by the collector's health data. To avoid obtaining the collector's private information, a distributed iterative algorithm is then designed to obtain the optimal task pricing strategy. Furthermore, we conduct computational experiments to evaluate the performance of the proposed pricing mechanism and analyze the effects of intrinsic rewards, sensing costs on optimal task prices, and collectors' health-data supplies.
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Central Ohio Archaeological Digitization Survey: Preliminary Report
The Central Ohio Archaeological Digitization Survey (COADS) is a collaborative research project between Ball State University, the University of Akron, and over a dozen private collectors from Ohio to Colorado funded by the National Science Foundation (BCS #1723879 and BCS #1723877). COADS’ three primary goals are to 1) investigate patterns of land use and technology over long expanses of past time in central Ohio (see Nolan 2014), and 2) to leverage the large, if selective, datasets of private collectors for analytical purposes (characterizing point types using geometric morphometrics methods and modeling the transitions between types). Finally, COADS is also designed to serve as a model of productive collaboration between archaeologists and responsible collectors that, among other things, will greatly increase relevant sample sizes (Pitblado and Shott 2015; Shott 2008; 2015). The goal of this paper is to summarize the initial results of collaboration with local collectors, who own the majority of projectile points across the Midwest (Shott 2017), and general patterns interpreted from one of the largest amassed projectile point databases in the world.
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- Award ID(s):
- 1723877
- PAR ID:
- 10226100
- Date Published:
- Journal Name:
- Current Research in Ohio Archaeology
- Volume:
- 2021
- Page Range / eLocation ID:
- 1-9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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