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Fog Computing as a Resource-Aware Enhancement for Vicinal Mobile Mesh Social Networking

机译:雾计算作为针对专用移动网格社交网络的资源感知增强功能

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Mobile Mesh Social Network (MMSN) represents an environment where the mobile device users are capable of performing various virtual social network activities such as sharing information, forming social groups, text messaging when they encounter each other in the physical vicinity within the wireless network range. Moreover, the characteristics of MMSN such as the Internetless activities and Wireless Mesh Network (WMN)-based connectivity provides various potentials including but not limited to business opportunities, scalable crowdsourcing or crowdsensing deployment, edge computing and so on. Although there exist a fair number of software platforms that help developers to implement MMSN, they still cannot fully overcome the limitation derived from the hardware resource constraint nature of the participative mobile devices. In order to enhance the MMSN in terms of cost efficiency, we introduce Fog Social Network (FSN) model, which utilises the computing and networking resources in users' close vicinity to improve the overall efficiency of MMSN. Further, the proposed FSN framework consists of an adaptive resource-aware cost-performance index (CPI) scheme, which performs dynamic approach selection autonomously at runtime to choose the most efficient route for the delivery of the messages for MMSN activities. With this intention, we have implemented and validated a proof-of-concept prototype.
机译:移动网状社交网络(MMSN)代表一种环境,其中移动设备用户能够在无线网络范围内的物理附近遇到彼此时执行各种虚拟社交网络活动,例如共享信息,形成社交团体,发短信。此外,MMSN的特性(如无互联网活动和基于无线网状网络(WMN)的连接性)提供了各种潜力,包括但不限于商业机会,可扩展的众包或众包部署,边缘计算等。尽管存在许多可帮助开发人员实施MMSN的软件平台,但它们仍不能完全克服由参与式移动设备的硬件资源约束性质所带来的限制。为了提高MMSN的成本效率,我们引入了雾社会网络(FSN)模型,该模型利用用户附近的计算和网络资源来提高MMSN的整体效率。此外,提出的FSN框架由自适应资源感知成本效益指数(CPI)方案组成,该方案在运行时自动执行动态方法选择,以选择最有效的路由来传递MMSN活动的消息。出于这个目的,我们已经实现并验证了概念验证原型。

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