Can Early Joining Participants Contribute More? - Timeliness Sensitive Incentivization for Crowdsensing
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This paper investigates the incentive mechanism design from a novel and practically important perspective in which mobile users as contributors do not join simultaneously and a requester desires large efforts from early contributors. A two-stage Tullock contest framework is constructed:at the second stage the potential contributors compete for splittable reward by exerting efforts, and at the first stage the requester can orchestrate the incentive mechanism to maximize his crowdsensing efficiency given the rewarding budget. A general reward discrimination mechanism is developed for timeliness sensitive crowdsensing where an earlier contributor usually has a larger maximum achievable reward and thus allocates more efforts. Owning to the lack of joining time information, two practical implementations, namely earliest-n and termination time, are announced to the contributors. For each of them, we formulate a Stackelberg Bayesian game in which the joining time of a contributor is his type and not available to his opponents. The uniqueness of Bayesian Nash equilibrium (BNE) is proved in each strategy. To maximize the requester's efficiency, we compute the optimal number of rewarded contributors in the earliest-n scheme and the optimal deadline in the termination time scheme. Our contest framework is applicable not only to the closed crowdsensing with fixed number of contributors, but also to the open crowdsensing that the arrival of contributors is governed by a stochastic process. Extensive simulations manifest that with appropriate reward discriminations, the requester is able to achieve a much higher efficiency with the optimal selection of the number of rewarded contributiors and the termination time.
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