移动群智感知中面向任务需求的用户选择激励机制

现有的移动群智感知激励机制大多以平台为中心或是以用户为中心进行设计,缺乏对感知任务需求的多维考虑,从而无法切实地以任务为中心进行用户选择,导致无法满足任务需求的最大化和多样化。针对上述问题,提出一种面向任务需求的用户选择激励机制TRIM,这是一种以任务为中心的设计方法。首先,感知平台根据任务需求发布感知任务,并从任务类型、时空特性以及感知报酬等多维度构建任务向量以最大化满足任务需求,而感知用户则基于意愿偏好、个人贡献值以及期望报酬等属性构建用户向量,实现个性化选择感知任务参与响应;然后,通过引入高效且隐私保护的余弦相似度计算协议(PCSC),计算任务和用户的相似度并根据相似度高低进行用户匹配筛选得到目标用户集,更好地满足感知任务需求的同时保护用户隐私不泄露;最后,通过仿真实验表明,在感知任务和感知用户的匹配过程中,与采用Paillier加密协议的激励机制相比,TRIM缩短了指数级增量的计算时间开销,提高了计算效率;与采用直接余弦相似度计算协议的激励机制相比,TRIM保证了感知用户的隐私安全,达到了98%的匹配精确度。

Task requirement-oriented user selection incentive mechanism in mobile crowdsensing

Most existing incentive mechanisms in mobile crowdsensing are platform-centered design or user-centered design without multidimensional consideration of sensing task requirements. Therefore, it is impossible to make user selection effectively based on sensing tasks and meet the maximization and diversification of the task requirements. To solve these problems, a Task Requirement-oriented user selection Incentive Mechanism (TRIM) was proposed, which is a task-centered design method. Firstly, sensing tasks were published by the sensing platform according to task requirements. Based on multiple dimensions such as task type, spatio-temperal characteristic and sensing reward, the task vectors were constructed to optimally meet the task requirements. To implement the personalized sensing participation, the user vectors were constructed based on the user preferences, individual contribution value, and expected reward by the sensing users. Then, by introducing Privacy-preserving Cosine Similarity Computation protocol (PCSC), the similarities between the sensing tasks and the sensing users were calculated. In order to obtain the target user set, the user selection based on the similarity comparison results was performed by the sensing platform. Therefore, the sensing task requirements were better met and the user privacy was protected. Finally, the simulation experiment indicates that TRIM shortens the computational time overhead of exponential increments and improves the computational efficiency compared with incentive mechanism using Paillier encryption protocol in the matching process between sensing tasks and sensing users; compared with the incentive mechanism using direct PCSC, the proposed TRIM guarantees the privacy of the sensing users and achieves 98% matching accuracy.