Evaluation of AI-based Smart-Sensor Deployment at the Extreme Edge of a Software-Defined Network

被引：0

作者：

Aguilar-Rivera, Anton ^{[1
]}

Vilalta, Ricard ^{[1
]}

Parada, Raul ^{[1
]}

Mira Perez, Fermin ^{[1
]}

Vazquez-Gallego, Francisco ^{[2
]}

机构：

[1] Ctr Tecnol Telecomunicac Catalunya CTTC CERCA, Castelldefels, Spain

[2] I2CAT Fdn, Barcelona, Spain

来源：

2022 IEEE CONFERENCE ON NETWORK FUNCTION VIRTUALIZATION AND SOFTWARE DEFINED NETWORKS (IEEE NFV-SDN) | 2022年

关键词：

Connected vehicles; V2X; Machine-learning; Hazard detection;

D O I：

10.1109/NFV-SDN56302.2022.9974729

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The introduction of AI-based smart-sensors on the network might suppose stringent requirements for the network edge, including the necessity to process real-time video feeds. Moreover, the introduction of vehicular communications allows the multiple location placement of necessary computational processes. To this end, we have proposed an AI-based smart-sensor solution that is able to be deployed at the extreme edge of the network (i.e., on the vehicle). The architecture for the connected vehicle is presented and accuracy results are provided for the proposed smart-sensor.

引用

页码：1 / 5

页数：5

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