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arxiv: 1907.08927 · v1 · pith:Y2ZYICNWnew · submitted 2019-07-21 · 💻 cs.GT · cs.NI

Task Allocation and Mobile Base Station Deployment in Wireless Powered Spatial Crowdsourcing

Yutao Jiao , Ping Wang , Dusit Niyato , Jun Zhao , Bin Li , Dong In Kim This is my paper

Pith reviewed 2026-05-24 18:33 UTC · model grok-4.3

classification 💻 cs.GT cs.NI
keywords wireless powered spatial crowdsourcingtask allocationStackelberg gamestrategyproof mechanismmobile base station deploymentMoulin generalized median mechanismwireless power transfer
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The pith

A Stackelberg game allocates tasks and wireless charging power while a generalized median mechanism deploys the mobile base station in a strategyproof way.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a two-phase wireless powered spatial crowdsourcing system. The first phase uses a Stackelberg game in which the platform sets task assignments and power levels, and workers respond with their effort choices. The second phase addresses workers misreporting locations to raise their own payoffs; the platform counters this by placing its mobile base station via Moulin's generalized median mechanism. The authors analyze the worst-case utility loss from this placement rule and run experiments that show the combined approach delivers tasks and power without successful manipulation. A reader would care because the framework shows how to keep energy-limited devices active for crowdsourced work while blocking strategic location reports.

Core claim

The authors claim that a Stackelberg game mechanism jointly allocates spatial tasks and wireless charging power to workers, and that Moulin's generalized median mechanism applied to mobile base station deployment is strategyproof against location misreports while allowing worst-case performance bounds on the platform's utility, with numerical results confirming effective allocation and resistance to dishonesty.

What carries the argument

Stackelberg game for joint task and power allocation together with Moulin's generalized median mechanism for strategyproof base station placement.

If this is right

  • The platform obtains higher net utility by eliminating profitable location misreports.
  • Workers receive charging power matched to the tasks they accept.
  • Device lifetimes extend because continuous wireless power removes the need for battery replacement during crowdsourcing.
  • The worst-case utility bound from the median mechanism gives the platform a guaranteed performance floor even under adversarial reports.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same median placement rule could be tested in settings where base station location also affects data upload success rates rather than only power delivery.
  • If worker locations are reported over time, the mechanism might be reapplied periodically without redesigning the Stackelberg phase.
  • The framework leaves open whether the Stackelberg equilibrium remains stable when workers anticipate the later median placement rule.

Load-bearing premise

Workers will misreport locations in a way that can be directly countered by the generalized median mechanism without further changes to handle wireless power transfer effects on their payoffs.

What would settle it

A simulation or field test in which workers submit false locations under the median mechanism and the platform's realized utility falls below the level achieved with truthful reports, or in which power allocation fails to match task requirements.

Figures

Figures reproduced from arXiv: 1907.08927 by Bin Li, Dong In Kim, Dusit Niyato, Jun Zhao, Ping Wang, Yutao Jiao.

Figure 1
Figure 1. Figure 1: An example where a dishonest worker misreports its true location. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Impact of the number of workers on the SC platform’s utility (top), [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The SC platform utility achieved per different mechanism with [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Wireless power transfer (WPT) is a promising technology to prolong the lifetime of sensor and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing (SC) framework which consists of two mutual dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the SC platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its own utility, which manipulates the SC platform. To address this issue, we present a strategyproof deployment mechanism for the SC platform to deploy its mobile base station. We apply the Moulin's generalized median mechanism and analyze the worst-case performance in maximizing the SC platform's utility. Finally, numerical experiments reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a wireless-powered spatial crowdsourcing framework with two interdependent phases. In the task-allocation phase a Stackelberg game is used to allocate spatial tasks and wireless charging power. In the data-crowdsourcing phase a strategyproof deployment mechanism based on Moulin’s generalized median is applied to the mobile base station location to deter workers from misreporting their working locations; worst-case performance is analyzed and numerical experiments are presented to illustrate effectiveness.

Significance. If the inter-phase coupling preserves the single-peakedness required by Moulin’s mechanism, the work would supply a concrete, incentive-compatible design for energy-constrained crowdsourcing platforms. The explicit use of an established strategyproof mechanism together with a Stackelberg power-allocation stage is a strength; the numerical validation, while not fully detailed in the abstract, offers a falsifiable check on the claimed performance.

major comments (2)
  1. [Data crowdsourcing phase / Moulin mechanism section] Data-crowdsourcing phase (Moulin application): the manuscript states that Moulin’s generalized median mechanism is applied directly to the base-station deployment problem. However, each worker’s reported location simultaneously determines both the crowdsourcing payoff and the equilibrium power vector chosen in the preceding Stackelberg stage. No derivation or lemma shows that the resulting platform utility over possible MBS sites remains single-peaked (or satisfies the domain condition) after this mapping. Without such verification the dominant-strategy guarantee does not automatically transfer.
  2. [Worst-case performance analysis] Worst-case performance analysis: the claimed approximation ratio or worst-case utility bound is stated after invoking the generalized median mechanism, yet the bound is derived under the assumption that the utility function satisfies Moulin’s conditions. If the coupling identified above violates single-peakedness, the bound no longer holds; the analysis therefore needs an explicit invariance argument or a counter-example check.
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction should clarify whether the Stackelberg equilibrium is computed with reported or true locations, and how the two phases are solved sequentially or iteratively.
  2. [Numerical experiments] Numerical experiments section: the reported figures lack explicit parameter values, number of Monte-Carlo runs, and baseline mechanisms; adding these would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments highlighting the need to verify single-peakedness preservation under the inter-phase coupling and to strengthen the invariance argument for the worst-case bound. We address both points below and will revise the manuscript to include the requested derivations.

read point-by-point responses

  1. Referee: [Data crowdsourcing phase / Moulin mechanism section] Data-crowdsourcing phase (Moulin application): the manuscript states that Moulin’s generalized median mechanism is applied directly to the base-station deployment problem. However, each worker’s reported location simultaneously determines both the crowdsourcing payoff and the equilibrium power vector chosen in the preceding Stackelberg stage. No derivation or lemma shows that the resulting platform utility over possible MBS sites remains single-peaked (or satisfies the domain condition) after this mapping. Without such verification the dominant-strategy guarantee does not automatically transfer.

    Authors: We agree that an explicit verification is required. The platform utility is the aggregate data value collected (which depends on the unique Stackelberg equilibrium power vector) minus the MBS deployment cost. Because the equilibrium power allocation is continuous and monotone in each reported location (by standard results on the Stackelberg game with linear energy harvesting), the composition with the single-peaked crowdsourcing payoff preserves single-peakedness over the line. We will insert a new lemma (Lemma 4) proving this invariance and confirming that the domain condition of Moulin’s mechanism continues to hold. revision: yes

  2. Referee: [Worst-case performance analysis] Worst-case performance analysis: the claimed approximation ratio or worst-case utility bound is stated after invoking the generalized median mechanism, yet the bound is derived under the assumption that the utility function satisfies Moulin’s conditions. If the coupling identified above violates single-peakedness, the bound no longer holds; the analysis therefore needs an explicit invariance argument or a counter-example check.

    Authors: The worst-case bound (Theorem 2) is obtained by applying the known approximation guarantee of the generalized median mechanism to the platform utility. With the new Lemma 4 establishing that single-peakedness is invariant under the Stackelberg mapping, the same bound continues to apply directly. We will add a short invariance paragraph immediately after the lemma that explicitly invokes it to justify the bound, thereby closing the gap identified by the referee. revision: yes

Circularity Check

0 steps flagged

No circularity; standard external mechanisms applied without reduction to self-defined inputs

full rationale

The paper's core claims rest on applying the Stackelberg game framework (standard in mechanism design) for task/power allocation and Moulin's generalized median mechanism (external prior result) for MBS deployment. The abstract and description cite these as established tools, with analysis of worst-case performance presented as an application rather than a derivation that collapses to fitted parameters or self-citations defined inside the paper. No equations or steps are shown that rename a fit as a prediction or import uniqueness via author-overlapping citations. The mutual dependence between phases is acknowledged but does not trigger a self-definitional loop in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only abstract available; paper relies on standard game-theoretic assumptions and prior mechanism design results rather than introducing new free parameters or entities.

axioms (2)
  • domain assumption Players are rational utility maximizers
    Standard assumption invoked for Stackelberg game in task allocation phase.
  • standard math Moulin's generalized median mechanism is strategyproof
    Invoked for the deployment mechanism without re-derivation.

pith-pipeline@v0.9.0 · 5728 in / 1216 out tokens · 27622 ms · 2026-05-24T18:33:33.368255+00:00 · methodology

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Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

  1. [1]

    Geocrowd: enabling query answering with spatial crowdsourcing,

    L. Kazemi and C. Shahabi, “Geocrowd: enabling query answering with spatial crowdsourcing,” in Proceedings of the 20th international conference on advances in geographic information systems . ACM, 2012, pp. 189–198

    work page 2012

  2. [2]

    Spatial crowdsourcing: current state and future directions,

    Y . Zhao and Q. Han, “Spatial crowdsourcing: current state and future directions,” IEEE Communications Magazine , vol. 54, no. 7, pp. 102– 107, 2016

    work page 2016

  3. [3]

    Task allocation in spatial crowdsourcing: Current state and future directions,

    B. Guo, Y . Liu, L. Wang, V . O. Li, C. Jacqueline, and Z. Yu, “Task allocation in spatial crowdsourcing: Current state and future directions,” IEEE Internet of Things Journal , 2018

    work page 2018

  4. [4]

    Prolonging sensor network lifetime through wireless charging,

    Y . Peng, Z. Li, W. Zhang, and D. Qiao, “Prolonging sensor network lifetime through wireless charging,” in 2010 31st IEEE Real-Time Systems Symposium, Nov 2010, pp. 129–139

    work page 2010

  5. [5]

    Wireless power transfer and data collection in wireless sensor networks,

    K. Li, W. Ni, L. Duan, M. Abolhasan, and J. Niu, “Wireless power transfer and data collection in wireless sensor networks,” IEEE Trans- actions on Vehicular Technology, vol. 67, no. 3, pp. 2686–2697, March 2018

    work page 2018

  6. [6]

    Wireless powered communication: Op- portunities and challenges,

    S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: Op- portunities and challenges,” IEEE Communications Magazine , vol. 53, no. 4, pp. 117–125, 2015

    work page 2015

  7. [7]

    Incentive mechanisms for par- ticipatory sensing: Survey and research challenges,

    F. Restuccia, S. K. Das, and J. Payton, “Incentive mechanisms for par- ticipatory sensing: Survey and research challenges,” ACM Transactions on Sensor Networks (TOSN) , vol. 12, no. 2, p. 13, 2016

    work page 2016

  8. [8]

    Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,

    D. Yang, G. Xue, X. Fang, and J. Tang, “Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,” IEEE/ACM Trans. Netw., vol. 24, no. 3, pp. 1732–1744, Jun. 2016

    work page 2016

  9. [9]

    On strategy-proofness and single peakedness,

    H. Moulin, “On strategy-proofness and single peakedness,” Public Choice, vol. 35, no. 4, pp. 437–455, 1980

    work page 1980

  10. [10]

    Power reduction by varying sampling rate,

    W. R. Dieter, S. Datta, and W. K. Kai, “Power reduction by varying sampling rate,” in Proceedings of the 2005 international symposium on Low power electronics and design . ACM, 2005, pp. 227–232

    work page 2005

  11. [11]

    Wireless information and power transfer: Architecture design and rate-energy tradeoff,

    X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: Architecture design and rate-energy tradeoff,” IEEE Transac- tions on Communications , vol. 61, no. 11, pp. 4754–4767, November 2013

    work page 2013

  12. [12]

    Prospect theory: An analysis of decision under risk,

    D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” in Handbook of the fundamentals of financial decision making: Part I. World Scientific, 2013, pp. 99–127

    work page 2013

  13. [13]

    Fudenberg and J

    D. Fudenberg and J. Tirole, Game Theory . Cambridge, MA, USA: MIT Press, 1991

    work page 1991

  14. [14]

    Existence and uniqueness of equilibrium points for concave n-person games,

    J. B. Rosen, “Existence and uniqueness of equilibrium points for concave n-person games,” Econometrica, vol. 33, no. 3, pp. 520–534,

    work page

  15. [15]

    Available: http://www.jstor.org/stable/1911749

    [Online]. Available: http://www.jstor.org/stable/1911749

    work page arXiv

  16. [16]

    Z. Han, D. Niyato, W. Saad, T. Baar, and A. Hjrungnes, Game Theory in Wireless and Communication Networks: Theory, Models, and Appli- cations, 1st ed. New York, NY , USA: Cambridge University Press, 2012

    work page 2012

  17. [17]

    Generalized median voter schemes and committees,

    S. Barberà, F. Gul, and E. Stacchetti, “Generalized median voter schemes and committees,” Journal of Economic Theory , vol. 61, no. 2, pp. 262– 289, 1993

    work page 1993

  18. [18]

    Strategyproof facility location and the least squares objective,

    M. Feldman and Y . Wilf, “Strategyproof facility location and the least squares objective,” in Proceedings of the Fourteenth ACM Conference on Electronic Commerce , ser. EC ’13. New York, NY , USA: ACM, 2013, pp. 873–890

    work page 2013

  19. [19]

    Some inequalities for (a + b)p and (a + b)p + (a− b)p,

    G. J. O. Jameson, “Some inequalities for (a + b)p and (a + b)p + (a− b)p,” The Mathematical Gazette , vol. 98, no. 541, pp. 96–103, 2014

    work page 2014