* 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](zotero://open-pdf/library/items/WXTJVNM8?page=1))

> "Table 1 compares the existing works on AI applications in networks." ([Sun et al 2019:69](zotero://open-pdf/library/items/WXTJVNM8?page=2))

> "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](zotero://open-pdf/library/items/WXTJVNM8?page=2))

> "Wang et al. [11] explored the trade-off between energy consumption and service latency in IIoT" ([Sun et al 2019:69](zotero://open-pdf/library/items/WXTJVNM8?page=2))

> "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](zotero://open-pdf/library/items/WXTJVNM8?page=3))

> "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](zotero://open-pdf/library/items/WXTJVNM8?page=3))

> "[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](zotero://open-pdf/library/items/WXTJVNM8?page=7))