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Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems

机译:社交雾计算系统的启用人群感知的安全服务建议

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Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on “friend” relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.
机译:雾计算将情报和资源从远程云转移到边缘网络,具有为传感数据源与用户之间的通信提供低延迟的潜力。对于从物联网(IoT)到云的对象,对象之间建立起类似于社会的关系是一种新趋势,这有效地将发达的社交性带来的好处带入了复杂的环境。随着雾服务变得越来越复杂,雾用户通过社交网络共享他们自己的服务,资源和数据将变得更加方便。同时,高效的社会组织可以实现更灵活,安全和协作的网络。前述优点使社交网络成为雾计算系统的潜在架构。在本文中,我们设计了一种用于社交雾计算的体系结构,其中基于“朋友”关系提供了雾服务。据我们所知,这是基于有组织的雾计算系统的社交模型的首次尝试。同时,社交网络增加了雾计算服务的复杂性和安全风险,在社交雾计算中增加了安全服务推荐的难度。为了解决这个问题,我们提出了一种新颖的支持人群感知的安全服务供应方法,以在社交雾计算系统中准确地推荐安全服务。仿真结果表明,社交雾计算系统中支持人群感知的安全服务推荐方法的可行性和有效性。

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