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

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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.