Mobasheri Motahareh, Kim Yangwoo, Kim Woongsup
Information and Communication Engineering Department, Dongguk University, Seoul 04620, Korea.
Sensors (Basel). 2020 Dec 4;20(23):6942. doi: 10.3390/s20236942.
The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better.
自从物联网（IoT）通过各种智能环境使数据生成速度加快以来，“大数据”一词已在网络概念中出现。相比之下，带宽的提升较为缓慢；因此，它已成为一个瓶颈，产生了解决带宽限制的需求。随着时间的推移，由于智能环境的扩展和物联网设备数量的增加，雾节点的数量也增加了。在本研究中，我们引入了与传统雾计算相对的雾分片计算。我们利用雾节点及其协作来解决带宽管理问题，以克服物联网设备在紧急情况下所需的额外带宽和带宽限制。我们使用强化学习方法来制定雾节点的决策问题，并开发一种Q学习算法，通过迫使雾节点在特殊条件下相互帮助来实现高效决策。据我们所知，到目前为止还没有针对这一目标的研究。因此，我们将本研究与另一种考虑单个雾节点的场景进行比较，以表明我们新的扩展方法性能要好得多。
