CRII: SaTC: Energy Efficient Participatory Data Collection Schemes and Context-Aware Incentives for Trustworthy Crowdsensing via Mobile Social Networks

在群体感测系统中，能量有效的数据收集是移动的感测服务提供商（即，移动的用户经由他们的移动的设备上的内置传感器提供感测作为服务），以便最大化电池寿命，而可信度是终端用户的主要关注点。拟议的研究将同时解决节能数据收集和上下文感知激励问题，以最大限度地降低功耗并最大限度地提高数据可信度。此外，这项研究将提出新的用户驱动的crowdsensing商业模式，智能手机用户相互竞争的基础上，他们的感知数据的有用性和可信度的补偿。该研究的最终社会影响是在公共安全，灾害管理和社区参与领域的新的人群感知应用，这些应用将通过改进节能数据收集，通过上下文感知感知提高人群发送的可信度，以及新的众测商业模式，这些模式将激励更多用户提供他们的内置移动终端，在传感器作为一种服务。拟议的研究将扩展正在进行的努力值得信赖的crowdsensing，以解决能源效率的数据收集和新的上下文感知的用户激励策略，以提高数据的可信度。为了解决能源效率的数据收集，联盟博弈论为基础的算法将提出，而聚合系统的可信度将通过定义新的可信度函数和上下文分析的移动的社交网络的传感数据提供商。这些方法将通过与基准优化模型的比较进行验证。统计和协作的信任分数将被用来引入新的传感服务提供商的可信度和信誉功能。新的可信度和声誉功能将减轻包括Sybil在内的对手的影响，这些对手旨在提供错误信息和操纵。重点将放在与新兴的移动的社交网络（MSN）模型及其相关的时空背景分析的兼容性。研究将通过建立一个框架来完成，该框架结合了节能数据收集和上下文感知用户激励的优点。

In a crowdsensing system, energy efficient data collection is a primary concern for mobile sensing service providers (i.e., mobile users offering sensing as a service via built-in sensors on their mobile devices) in order to maximize battery life whereas trustworthiness is a primary concern for the end users. The proposed research will simultaneously address energy-efficient data collection and context-aware incentives to both minimize power consumption and maximize data trustworthiness. Furthermore, this research will propose new user-driven crowdsensing business models where smart phone users compete with each other for compensation based on the usefulness and trustworthiness of their sensing data. The ultimate societal impacts of the research are new crowdsensing applications in the areas of public safety, disaster management and community engagement that will be enabled by improved energy-efficient data collection, increased crowdsending trustworthiness through context aware sensing, and new crowdsensing business models that will incentivize more users to offer their mobile device built-in sensors as a service.The proposed research will extend the ongoing efforts on trustworthy crowdsensing to address energy efficient data collection and new context-aware user incentive strategies to improve data trustworthiness. In order to address energy efficient data collection, coalitional game theory-based algorithms will be proposed while trustworthiness of the aggregated system will be addressed by defining new trustworthiness functions and context analysis of mobile social networks of the sensing data providers. These methodologies will be validated through comparison to benchmark optimization models. Statistical and collaborative trust scores will be used to introduce new trustworthiness and reputation functions for sensing service providers. The new trustworthiness and reputation functions will mitigate the impact of adversaries including the Sybils who aim at misinformation and manipulation. An emphasis will be placed on compatibility with emerging mobile social network (MSN) models and their associated spatio-temporal context analyses. The research will be completed by building a framework which combines the merits of energy efficient data collection and context-aware user incentives.