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- Mdnotes File Name: sunAIEnhancedOffloadingEdge2019
Gray Annotations (17/12/2020, 23:14:22)
"According to GE Digital, IIoT is estimated to unlock manufacturing savings and benefit 46 percent of the global economy [1]" (Sun et al 2019:68)
"Table 1 compares the existing works on AI applications in networks." (Sun et al 2019:69)
"Bisio et al. [10] studied the role of context awareness in IIoT applications such as smart health, smart factory, and smart home scenarios." (Sun et al 2019:69)
"Wang et al. [11] explored the trade-off between energy consumption and service latency in IIoT" (Sun et al 2019:69)
"Li et al. [13] applied deep learning for IoT in the edge computing environment. They designed a scheduling algorithm to maximize the number of tasks in edge computing with guaranteed quality of service (QoS) requirements." (Sun et al 2019:70)
"In [3], edge servers learn model parameters from data distributed at the edge nodes, using the gradient-descent method based on distributed learning, instead of sending data to the centralized cloud. They proposed a control algorithm for the trade-off between local update and global parameter aggregation to minimize loss function and under a given resource budget." (Sun et al 2019:70)
"[1] GE Digital Report, "Everything You Need to Know about the Industrial Internet of Things," 2017; https://www.ge.com/ digital/blog/everything-you-needknow-about-industrial-internet-things" (Sun et al 2019:74)