Abstract
Crowdsensing is a new activity form gathering a suitable set of users to collectively finish a sensing task. It has attracted great attention because it provides an easy-access sensing scheme and reduces the sensing cost compared with the traditional sensing method. Hence, several crowdsensing platforms have emerged at the right moment, where the requester can publish sensing tasks and the users compete for the winners of the tasks. Thus, there is a multi-round game among users, in which we consider a case that the users bid their available time for the specific sensing tasks, and the purpose of a user is to obtain as many tasks as possible within the available time budget. To this end, we propose a Multi-round Bidding strategy based on Game theory for Crowdsensing task (MBGC), where each user decides the bidding for the specific task according to its trade-off between the expected number of obtained tasks and remaining available time. Then, a user dynamically decides the probabilities to bid different kinds of biddings in the different rounds according to the Nash Equilibrium solution. We conduct extensive simulations to simulate the game process for the crowdsensing tasks. The results show that compared with other bidding strategies, MBGC always achieves the largest number of obtained tasks with an identical time budget.
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Wang, E., Yang, Y., Wu, J., Wang, H. (2019). Multi-round Bidding Strategy Based on Game Theory for Crowdsensing Task. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11611. Springer, Cham. https://doi.org/10.1007/978-3-030-24907-6_16
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DOI: https://doi.org/10.1007/978-3-030-24907-6_16
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