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]: Jobs submitted into a cluster have varying requirements depending on user-specific needs and expectations. Therefore, in utility-driven cluster computing, cluster resource management systems (RMSs) need to be aware of these requirements in order to allocate resources effectively. Service level agreements (SLAs) can be used to differentiate different value of jobs as they define service conditions that the cluster RMS agrees to provide for each different job. The SLA acts as a contract between a user and the cluster whereby the user is entitled to compensation whenever the cluster RMS fails to deliver the required service. In this paper, we present a proportional share allocation technique called LibraSLA that takes into account the utility of accepting new jobs into the cluster based on their SLA. We study how LibraSLA performs with respect to several SLA requirements that include: (i) deadline type whether the job can be delayed, (ii) deadline when the job needs to be finished, (iii) budget to be spent for finishing the job, and (iv) penalty rate for compensating the user for failure to meet the deadline
However, the heterogeneity of task depreciation over time has not been addressed in the literature. Task depreciation models over time in the existing works are as the value of tasks immediately drop to zero after the given deadline. In other words, there can be only two kinds of task valuation, either full or null. Such dichotomous model overlooks the cases where task valuation remains valid even after the deadline, though depreciating in proportion to the amount of time past the deadline, which can be observed in practice and the literature [1] .