[Paper Review] Can Early Joining Participants Contribute More? - Timeliness Sensitive Incentivization for Crowdsensing

Yuedong Xu, Yifan Zhou+3 more|arXiv (Cornell University)|Oct 5, 2017

Mobile Crowdsensing and Crowdsourcing18 references3 citations

TL;DR

This paper proposes a timeliness-sensitive incentive mechanism for crowdsensing using a two-stage Tullock contest framework, where early contributors receive higher rewards to encourage greater effort. By modeling the requester's orchestration as a Stackelberg Bayesian game, it proves the existence and uniqueness of Bayesian Nash equilibrium and shows that optimal selection of rewarded contributors (earliest-n) or termination time significantly boosts sensing efficiency under budget constraints.

ABSTRACT

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.

Motivation & Objective

• To address the challenge of incentivizing early participation in crowdsensing, where timely data is more valuable.
• To design an incentive mechanism that rewards contributors based on their joining time, favoring earlier contributors.
• To maximize the requester's sensing efficiency under a fixed budget while accounting for incomplete information on contributors' joining times.
• To develop practical, implementable schemes—earliest-n and termination time—that enable optimal reward discrimination.
• To prove the existence and uniqueness of Bayesian Nash equilibrium in the proposed game-theoretic framework.

Proposed method

• Formulates a two-stage Tullock contest where contributors compete for a splittable reward based on effort, with the requester setting incentives in the first stage.
• Models the joining time as a private type in a Stackelberg Bayesian game, where contributors do not observe each other's types.
• Develops a general reward discrimination mechanism that assigns higher maximum rewards to earlier-joining contributors to induce greater effort.
• Proposes two practical implementations: the earliest-n scheme (rewarding top n earliest contributors) and the termination time scheme (setting a deadline for reward eligibility).
• Derives analytical expressions for Bayesian Nash equilibrium (BNE) and proves its uniqueness under both schemes.
• Optimizes the requester’s efficiency by computing the optimal number of rewarded contributors (n) and optimal termination time (T) using budget constraints and expected expenditure models.

Experimental results

Research questions

• RQ1 How can a requester design an incentive mechanism that favors early contributors in a crowdsensing system with incomplete information on joining times?
• RQ2 What is the optimal configuration of reward parameters (e.g., number of rewarded contributors or termination time) to maximize sensing efficiency under a fixed budget?
• RQ3 Can a Stackelberg Bayesian game framework effectively model the strategic interaction between contributors and a requester in timeliness-sensitive crowdsensing?
• RQ4 Does the proposed reward discrimination mechanism lead to a unique and stable equilibrium in contributor behavior?
• RQ5 How does the proposed mechanism perform in both closed and open crowdsensing systems with fixed or stochastic contributor arrivals?

Key findings

• The proposed reward discrimination mechanism significantly increases sensing efficiency by incentivizing early contributors to exert higher efforts.
• The Bayesian Nash equilibrium (BNE) is uniquely determined in both the earliest-n and termination time schemes, ensuring stable strategic behavior.
• Optimal selection of the number of rewarded contributors (n) in the earliest-n scheme leads to a substantial improvement in efficiency compared to uniform reward schemes.
• In the termination time scheme, the optimal deadline (T) is derived to maximize efficiency while respecting the budget constraint, with simulations showing improved performance over non-optimized configurations.
• The framework is applicable to both closed systems (fixed number of contributors) and open systems (stochastic arrivals), demonstrating broad practical relevance.
• Extensive simulations confirm that with appropriate reward discrimination, the requester achieves higher efficiency, especially when the optimal n or T is selected.