You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Crowdsourcing can be applied to the Internet-of-Things (IoT) systems to provide more scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) task valuation and workers' punctuality, and 2) crowdsourcing service market by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare by attracting and retaining more participants in the long-term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. Simulation results show that the ESWM mechanism achieves higher expected social welfare and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness.

Cited Abstract(s):
[1]: Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing mechanisms focus on the expected utility prior to sensing, ignoring the risk of low quality solution and privacy leakage. Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism and its variants are not applicable here. In this paper, to address these challenges, we propose a behavior based incentive mechanism for crowd sensing applications with budget constraints by applying sequential all-pay auctions in mobile social networks (MSNs), not only to consider the effects of extensive user participation, but also to maximize high quality of sensing data submission for the platform (crowd sensing organizer) under the budget constraints, where users arrive in a sequential order. Through an extensive simulation, results indicate that incentive mechanisms in our proposed framework outperform existing solutions.

[2]: Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers’ types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

[3]: Mobile phone sensing has become increasingly popular since it can collect and analyze real-time data anywhere anytime, especially with the help of mobile phone users via crowdsourcing. In order to stabilize the mobile crowdsourcing at a massive scale, incentive mechanisms are needed not only to stimulate the users of mobile phones to participate in sensing, but also to incentivize the organizer of the sensing tasks with maximum service time and payoff. In this paper, we study a practical problem in mobile sensing, i.e., sensing time uncertainty, which may lead to failures of existing incentive mechanisms. In particular, we model this problem as a perturbed Stackelberg game in which mobile phone users may actually conduct sensing tasks during different periods of time rather than what they intend, like through a trembling hand. We find that there exist Trembling-Hand Perfect Equilibria (THP) given proper rewards. After characterizing THPs in this game with rigorous analysis, we design incentive mechanisms that can achieve a THP with the maximum system wide total sensing time and maximum platform utility. We finally verify the correctness and efficiency of our proposed incentive mechanisms and algorithms through extensive experiments.
J. [1] proposed a behavior-based incentive mechanism for crowdsensing with budget constraint. This work aims to achieve both the extensive user participation and high quality sensing data submission, based on users’ behavior abilities. S. [3] presented an incentive mechanism for mobile phones with uncertain sensing time. In order to address the sensing time uncertainty problem, this work modeled the problem as a perturbed Stackelberg game where mobile phone users sensing task times may be different from their original plans. T. [2] proposed an incentive mechanism for heterogeneous crowdsourcing using all-pay contests where workers have not only different type information (abilities and costs), but also the different beliefs (probabilistic knowledge) about their respective type information. In this work, the belief is modeled as a probability distribution.