支持雾的物联网部署中的资源优化

Visali Mushunuri, A. Kattepur, H. Rath, Anantha Simha
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引用次数: 19

摘要

物联网(IoT)设备通常部署在资源(能源、计算能力)受限的环境中。由于网络行为多变以及高延迟开销,将此类设备连接到云是不切实际的。雾计算指的是一种可扩展的分布式计算架构,它将计算任务移动到更靠近边缘设备或智能网关的地方。作为移动物联网场景的一个例子,在机器人部署中,计算密集型任务(如运行时映射)可以在对等机器人或智能网关上执行。这些计算任务大多涉及在运行时在计算节点内运行优化算法,并根据结果做出快速决策。在本文中,我们将优化库整合到部署在机器人传感器执行器上的机器人操作系统(ROS)中。使用基于ROS的仿真环境Gazebo,我们演示了运行时优化的案例研究场景。使用优化的分布式计算可以显着改善延迟和节省大型计算负载的电池。执行运行时优化的可能性在移动物联网部署中开辟了广泛的用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resource optimization in fog enabled IoT deployments
Internet of Things (IoT) devices are typically deployed in resource (energy, computational capacity) constrained environments. Connecting such devices to the cloud is not practical due to variable network behavior as well as high latency overheads. Fog computing refers to a scalable, distributed computing architecture which moves computational tasks closer to Edge devices or smart gateways. As an example of mobile IoT scenarios, in robotic deployments, computationally intensive tasks such as run time mapping may be performed on peer robots or smart gateways. Most of these computational tasks involve running optimization algorithms inside compute nodes at run time and taking rapid decisions based on results. In this paper, we incorporate optimization libraries within the Robot Operating System (ROS) deployed on robotic sensor-actuators. Using the ROS based simulation environment Gazebo, we demonstrate case-study scenarios for runtime optimization. The use of optimized distributed computations are shown to provide significant improvement in latency and battery saving for large computational loads. The possibility to perform run time optimization opens up a wide range of use-cases in mobile IoT deployments.
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