Mobile crowdsourcing refers to systems where the completion of tasks
necessarily requires physical movement of crowdworkers in an on-demand
workforce. Evidence suggests that in such systems, tasks often get assigned to
crowdworkers who struggle to complete those tasks successfully, resulting in
high failure rates and low service quality. A promising solution to ensure
higher quality of service is to continuously adapt the assignment and respond
to failure-causing events by transferring tasks to better-suited workers who
use different routes or vehicles. However, implementing task transfers in
mobile crowdsourcing is difficult because workers are autonomous and may reject
transfer requests. Moreover, task outcomes are uncertain and need to be
predicted. In this paper, we propose different mechanisms to achieve outcome
prediction and task coordination in mobile crowdsourcing. First, we analyze
different data stream learning approaches for the prediction of task outcomes.
Second, based on the suggested prediction model, we propose and evaluate two
different approaches for task coordination with different degrees of autonomy:
an opportunistic approach for crowdshipping with collaborative, but
non-autonomous workers, and a market-based model with autonomous workers for
crowdsensing.

移动众包是指任务的完成必须要求众包人员在按需工作力量中进行物理移动的系统。本文提出了在移动众包中实现任务转移、结果预测和任务协调的不同机制，包括数据流学习方法用于任务结果预测，以及具有不同自治程度的两种不同方法实现任务协调，分别为具有协作但非自治的众包运输机制和具有自治众包人员的市场模式。

数据流支持下的移动众包协同评估

Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported  Coordination of Mobile Crowdsourcing

As digital technology evolves, the increasing use of connected devices brings
both challenges and opportunities in the areas of mobile crowdsourcing, edge
computing, and recommender systems. This survey focuses on these dynamic
fields, emphasizing the critical need for privacy protection in our
increasingly data-oriented world. It explores the latest trends in these
interconnected areas, with a special emphasis on privacy and data security. Our
method involves an in-depth analysis of various academic works, which helps us
to gain a comprehensive understanding of these sectors and their shifting focus
towards privacy concerns. We present new insights and marks a significant
advancement in addressing privacy issues within these technologies. The survey
is a valuable resource for researchers, industry practitioners, and policy
makers, offering an extensive overview of these fields and their related
privacy challenges, catering to a wide audience in the modern digital era.

数字技术的发展带来了移动众包、边缘计算和推荐系统方面的挑战和机遇。本综述聚焦于这些领域，强调在我们日益数据化的世界中对隐私保护的重要需求，并探索了这些相互关联的领域的最新趋势，特别关注隐私和数据安全。综述通过对各种学术工作的深入分析，帮助我们全面了解这些行业及其对隐私问题的关注，提供了对这些领域及其相关隐私挑战的广泛概述，是研究人员、行业从业者和政策制定者的宝贵资源，在现代数字时代迎合了广大受众。