An Incentive Framework for Collaborative Sensing in Fog Networks

Chongchong Zhang, Fei Shen, Guowei Zhang, F. Qin, Feng Yan, P. Martins
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Abstract

As the big data era arrives, massive data traffic and applications generated by various terminal devices need to be processed in real time. To relieve the pressure of cloud computing on link congestion, delay, and energy consumption caused by the long distance between terminals and cloud server, the promising fog computing has been proposed. The fog network consisting of several fog clusters is considered, in which a fog controller $(\Gamma \mathrm{C})$ collects all the resource information of all its fog nodes (FNs). In order to better serve the terminal nodes, different FCs are willing to exchange the information of their FNs and share their services to some extent. Therefore, in this paper, we propose a novel incentive framework for collaborative sensing to motivate the fog cluster to provide service for other fog clusters. The SRs use the computation reward prices to motivate the SP to provide more computational capability to complete the tasks. The utility functions of the SRs and the SP are proposed, considering the payment for task computation, the task delay and the computation cost. The existences of the global optimums of both the utilities for the SRs the SP are proved. Numerous simulations verify our theoretical analyses and indicate the importance of our proposed incentive framework for collaborative sensing between fog clusters subscribed to different mobile providers in the fog network.
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雾网络协同传感的激励框架
随着大数据时代的到来,各种终端设备产生的海量数据流量和应用需要实时处理。为了缓解云计算带来的链路拥塞、延迟以及终端与云服务器之间距离较长所带来的能耗压力,人们提出了具有发展前景的雾计算。考虑由多个雾簇组成的雾网络,其中雾控制器$(\Gamma \mathrm{C})$收集其所有雾节点(FNs)的所有资源信息。为了更好地服务于终端节点,不同的fc愿意交换各自的fc的信息,并在一定程度上共享各自的服务。因此,本文提出了一种新的协同传感激励框架,以激励雾团为其他雾团提供服务。SRs使用计算奖励价格来激励SP提供更多的计算能力来完成任务。在考虑任务计算支付、任务延迟和计算成本的基础上,提出了系统优化算法和系统优化算法的效用函数。证明了两种效用的全局最优存在性。大量的模拟验证了我们的理论分析,并表明我们提出的激励框架对于雾网络中订阅不同移动提供商的雾簇之间的协同感知的重要性。
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