Energy Efficiency on Edge Computing: Challenges and Vision
Energy Efficiency on Edge Computing: Challenges and Vision
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DOI：
10.1109/ipccc55026.2022.9894303
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Tyler Holmes;C. Mclarty;Yong Shi;P. Bobbie;Kun Suo
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文献类型：
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作者：
Tyler Holmes;C. Mclarty;Yong Shi;P. Bobbie;Kun Suo
在过去的几年里，物联网(IoT)一直是下一代技术许多进步的关键。通过将传感器、执行器和智能对象等生态系统元素概念性地组合在一起，可以执行环境监测、智能交通系统、智能建筑和智能城市等复杂操作。边缘计算技术扩展了物联网生态系统的覆盖范围，通过互联网连接多个设备，提供强大的计算能力。遗憾的是，这种形式的计算有一个显著的缺点，即严格的能源限制和低功率效率，这极大地限制了它的潜力和使用。在本文中，我们介绍了高能效物联网边缘设备规划中的一些挑战，并讨论了一些最近的研究工作，这些研究工作提出了解决这些挑战的有前途的解决方案。具体地说，我们首先分析了提升边缘平台和物联网设备能耗的挑战和原因。接下来，我们进行案例研究，概述智能电网、智能城市、电动汽车(EV)、智能家居设备以及虚拟现实和增强现实(VR/AR)中的节能技术。我们进一步讨论了不同的方法，如计算卸载，边缘设备的硬件和软件设计，以及一些有助于降低能耗的算法。最后，我们概述了未来可能的发展方向和我们对提高EDGE平台能效的愿景。
The Internet of Things (IoT) has been the key to many advancements in next-generation technologies for the past few years. With a conceptual grouping of ecosystem elements such as sensors, actuators, and smart objects connected together, complex operations like environmental monitoring, intelligent transport systems, smart buildings, and smart cities are able to be performed. Edge computing technology extends the reach/scope of IoT ecosystems, offering robust and powerful computational capabilities by connecting multiple devices through the Internet. Unfortunately, this form of computation comes with a significant drawback with strict energy constraints and low power efficiency, which highly limits its potential and usage. In this paper, we present some of the challenges in the planning of energy-efficient IoT edge devices and discuss some of the recent research efforts that proposed promising solutions that address these challenges. Specifically, we first analyze the challenges and reasons for improving the energy consumption of edge platforms and IoT devices. Next, we perform case studies that outline the energy-saving techniques in smart grids, smart cities, electric vehicles (EV), smart home devices, and Virtual Reality and Augmented Reality (VR/AR). We further discuss different approaches such as computation offloading, edge devices hardware and software designs, and a number of algorithms that help reduce energy consumption. Finally, we outline possible future directions and our vision of improving energy efficiency on edge platforms.