A large number of emerging IoT applications rely on machine learning routines
for analyzing data. Executing such tasks at the user devices improves response
time and economizes network resources. However, due to power and computing
limitations, the devices often cannot support such resource-intensive routines
and fail to accurately execute the analytics. In this work, we propose to
improve the performance of analytics by leveraging edge infrastructure. We
devise an algorithm that enables the IoT devices to execute their routines
locally; and then outsource them to cloudlet servers, only if they predict they
will gain a significant performance improvement. It uses an approximate dual
subgradient method, making minimal assumptions about the statistical properties
of the system's parameters. Our analysis demonstrates that our proposed
algorithm can intelligently leverage the cloudlet, adapting to the service
requirements.

本研究旨在提高 IoT 设备分析数据的性能，通过使用边缘计算服务器来增强分析，具体使用算法进行优化，使得可以智能地利用云端服务满足需求

通过选择性边缘执行改善物联网分析

Improving IoT Analytics through Selective Edge Execution

With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.

本文主要介绍了物联网 (IoT) 背景下，雾计算 (fog computing) 及其边缘计算 (edge computing) 子范畴如多接入边缘计算 (MEC) 和 cloudlet 等的工作原理和应用研究，总结了相关工作的分类与问题，提出本领域未来研究方向和挑战。