On Extending ETSI MEC to Support LoRa for Efficient IoT Application Deployment at the Edge

被引:7
作者
Ksentini A. [1 ]
Frangoudis P.A. [2 ]
机构
[1] EURECOM, France
[2] TU Wien, Austria
来源
IEEE Communications Standards Magazine | 2020年 / 4卷 / 02期
基金
欧盟地平线“2020”;
关键词
5G mobile communication systems - Application programs - Data Analytics - Edge computing - Life cycle - Low power electronics - Software as a service (SaaS) - Cloud analytics - Wide area networks;
D O I
10.1109/MCOMSTD.001.1900051
中图分类号
学科分类号
摘要
The Internet of Things (IoT) undergoes a rapid transformation this last decade, thanks to the appearance of low-power wide area network technologies, such as LoRa/LoRaWAN, SigFox, and narrowband IoT, which allow reducing the deployment cost of sensors and other IoT devices. Many emerging services such as smart city, Industry 4.0, and autonomous driving are based on IoT devices and applications to collect and analyze data and control end devices (i.e., actuators). Among these services, several IoT applications, such as data analytics, need to be deployed at the edge to either reduce the latency to access data or treat the high amount of generated data locally. However, in the context of LoRa/LoRaWAN, most of the current IoT service deployments run the applications at a central cloud to ease the integration with existing software as a service (SaaS) platforms, without exploiting the benefits of edge computing. In this article, we propose a new framework that leverages the ETSI multi-access edge computing (MEC) model to deploy LoRabased IoT applications at the edge. In particular, the proposed model takes advantage of the ETSI MEC features, such as dynamic deployment of an IoT application at the edge and application life cycle management. In addition, the proposed framework allows running an IoT application as a 5G network slice at the edge. © 2017 IEEE.
引用
收藏
页码:57 / 63
页数:6
相关论文
共 9 条
[1]  
Chiang M., Zhang T., Fog and iot: An overview of research opportunities, IEEE Internet of Things J., 3, 6, pp. 854-864, (2016)
[2]  
An J., Et al., Eif: Toward an elastic iot fog framework for ai services, IEEE Commun. Mag., 57, 5, pp. 28-33, (2019)
[3]  
Afolabi I., Et al., Network slicing and softwarization: A survey on principles, enabling technologies, and solutions, IEEE Commun. Surveys &Tutorials, 20, 2, pp. 2429-2453, (2018)
[4]  
Ksentini A., Frangoudis P., Toward slicing-enabled multi-access edge computing in 5g, IEEE Network, 34, 2, (2020)
[5]  
Truong H.-L., Enabling edge analytics of iot data: The case of lorawan, Proc. 2018 Global Internet of Things Summit, (2018)
[6]  
Sanchez-Iborra R., Sanchez-Gomez J., Skarmeta A.F., Evolving iot networks by the confluence of mec and lp-wan paradigms, Future Generation Comp. Sys., 88, pp. 199-208, (2018)
[7]  
Sabella D., Et al., Developing software for multi-access edge computing, Etsi, (2019)
[8]  
Husain S.S., Et al., Mobile edge computing with network resource slicing for internet-of-things, Proc. 4th Ieee World Forum on Internet of Things, (2018)
[9]  
Zanzi L., Et al., Evolving multi-access edge computing to support enhanced iot deployments, IEEE Commun. Standards Mag., 3, 2, pp. 26-34, (2019)