In IoT data processing, cloud computing alone does not suffice due to latency constraints, bandwidth limitations, and privacy concerns. By introducing intermediary nodes closer to the edge of the network that offer compute services in proximity to IoT devices, fog computing can reduce network strain and high access latency to application services. While this is the only viable approach to enable efficient IoT applications, the issue of component placement among cloud and intermediary nodes in the fog adds a new dimension to system design. State-of-the-art solutions to this issue rely on either simulation or solving a formalized assignment problem through heuristics, which are both inaccurate and fail to scale with a solution space that grows exponentially. In this paper, we present a three step process for designing practical fog-based IoT applications that uses best practices, simulation, and testbed analysis to converge towards an efficient system architecture. We then apply this process in a smart factory case study. By deploying filtered options to a physical testbed, we show that each step of our process converges towards more efficient application designs.
翻译:在IoT数据处理中,单凭云计算是不够的,因为潜伏限制、带宽限制和隐私问题。通过引入靠近网络边缘的中间节点,在IoT设备附近提供计算服务的网络边缘,雾计算可以减少网络紧张,减少对应用服务的高通度。虽然这是实现高效 IoT 应用的唯一可行办法,但云和中间节点在雾中配置组件的问题为系统设计增添了新的层面。 这一问题的最先进的解决方案依赖于模拟或通过超常主义解决正式的派任问题,而超常主义既不准确,又无法与快速增长的解决方案空间相匹配。 在本文中,我们介绍了设计实用的基于雾的IoT应用程序的三步进程,这些应用程序使用最佳的做法、模拟和测试分析方法,以便向高效的系统架构汇合。我们随后将这一过程应用在智能工厂案例研究中。通过将过滤的选项应用到物理测试台,我们显示我们过程的每一步都趋向于更高效的应用设计。