A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called $QnQ$ Q n Q , to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, $QnQ$ Q n Q exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT) , we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate $QnQ$ Q n Q experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Josang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that $QnQ$ Q n Q is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

中文翻译：

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QnQ：基于质量和数量的安全可信移动人群感知统一方法

移动人群感知应用程序的一个主要挑战是由于用户的自私和恶意行为或对事件的错误感知而产生虚假（或垃圾邮件）贡献。由于不当激励，这种虚假贡献会导致收入损失，并且还会影响应用程序的运行可靠性。为了解决这些问题，我们提出了一个名为 $QnQ$ Q n Q 的事件信任和用户声誉模型，以隔离不同的用户类别，例如诚实的、自私的或恶意的。由此产生的用户声誉分数基于他们贡献的“质量”（贡献的准确性）和“数量”（参与程度）。具体来说，$QnQ$ Q n Q 利用评级反馈机制来评估特定事件的预期真实性，然后将其转化为稳健的信息质量 (QoI) 指标，以削弱自私和恶意用户行为的各种影响。最终，用户参与的各种事件的 QoI 被聚合以计算他的声誉分数，进而用于明智地分配用户奖励，以减少 CS 应用程序提供商的奖励损失。随后，受累积前景理论（CPT）的启发，我们提出了一种风险容忍度和声誉感知的可信决策方案来确定是否应该发布事件，从而提高应用程序的运行可靠性。为了通过实验评估 $QnQ$ Q n Q，我们将车辆人群感知应用程序视为概念验证。我们将我们的模型实现的 QoI 性能与 Josang 的信念模型进行比较，使用基于 Dempster-Shafer 的声誉模型的声誉评分，以及使用预期效用理论的操作（决策）准确性。实验结果表明，$QnQ$QnQ 能够更好地捕捉基于质量和数量的用户行为的细微差异，减少激励损失，并显着提高存在流氓贡献的操作准确性。