EiF : toward an elastic IoT fog framework for AI services
|Author:||An, JongGwan1; Li, Wenbin2; Le Gall, Franck2;|
1Sejong University, Republic of Korea
2Easy Global Market, France
3NEC Europe Ltd., Germany
4Korea Electronics Technologies Institute, Republic of Korea
5Aalto University, Finland
6Oulu University, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003117819
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-03-11
The first generation of IoT was developed and deployed all over the world by connecting devices with common functionalities that were not sufficiently efficient or reliable for use in dynamic situations that require adaptive solutions. However, these fundamental IoT functions and services mainly targeted stable environments; there is consequently a strong need for the next generation of IoT services to be smarter, faster, and more reliable. We believe that the hyper-connected IoT ecosystem on fog platforms with contextual AI technologies is a promising solution. In this work, we introduce the EiF, a flexible fog computing framework that runs on IoT gateways with adaptive AI services fostered on the cloud. Our approach can be viewed as an integration of three emerging technologies, namely IoT, fog, and AI. Generally, EiF virtualizes an IoT service layer platform for fog nodes, and provides functions to manage and orchestrate various fog nodes; upon service virtualization and orchestration, AI services are fostered within both the federated cloud and distributed edge side and are deployed on fog nodes. We demonstrate the feasibility of EiF via the example of intelligent traffic flow monitoring and management.
IEEE communications magazine
|Pages:||28 - 33|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-01456). This work was also supported in part by the Academy of Finland 6Genesis project under Grant No. 318927. Prof. Song and Prof. Taleb are co-corresponding authors of this work.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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