社交网络下的移动群智感知任务扩散

移动群智感知是智慧城市数字化建设的核心基础技术之一,是移动计算领域的热点研究课题.近年来,移动群智感知虽然已有许多代表性的研究成果,但从整体上看距离大规模的普及应用仍有不少距离,在实际推广应用中仍面临着用户参与度不高的问题.为此,引入社交网络IM (影响最大化)传播模型,考虑到现实情况下概率信息的缺失,通过在线学习的方式,在进行影响力活动的同时学习影响力概率,即根据用户反馈不断更新影响力模型信息,从而提出新的基于该模型的任务扩散方案.通过使用真实的社交网络数据集进行实验,结果表明提出的方法在传播范围方面比传统的IM方法更有效,为移动群智感知系统的实际推广应用做出贡献.

Task Diffusion of Mobile Crowdsensing in Social Network

Mobile crowdsensing is one of the core basic technologies in the digital construction of smart cities, and it is a hot research topic in the field of mobile computing. In recent years, although there have been many representative research results on mobile crowdsensing, there is still a long way to go before it is widely used on a large scale, and it still faces the problem of low user participation in the actual promotion and application. To this end, the social network influence maximization (IM) transmission model is introduced. It considers the lack of probabilistic information in reality and learns the probability of influence while performing influence activities through online learning, or in other words, the influence model information is constantly updated according to the user feedback, so as to propose a new task diffusion scheme based on the model. Through experiments with real social network data sets, the results show that the proposed method is more effective than the traditional IM method in terms of transmission scope, and it makes a contribution to the practical promotion and application of mobile crowdsensing systems.