Can I read AI-enabled IoT-Edge Data Analytics for Connected Living on EtoBox?

AI-enabled IoT-Edge Data Analytics for Connected Living by Zhihan Lv; Liang Qiao; Sahil Verma; Kavita is a Computer Science article available to read on EtoBox.

What is AI-enabled IoT-Edge Data Analytics for Connected Living about?

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the perfor

Who reads AI-enabled IoT-Edge Data Analytics for Connected Living?

Author

Zhihan Lv; Liang Qiao; Sahil Verma; Kavita

Publisher

ACM

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)