期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 7, 页码 9765-9774出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3110725
关键词
66; microservices; deep learning; state space model; safety-as-a-service (safe-aas); road transportation; decision parameters
This paper introduces a microservices and deep learning-based scheme for Safety-as-a-Service provision in a 6G environment. The proposed solution improves latency, energy consumption, and throughput compared to existing schemes through the use of microservices and deep learning models.
In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 - 31.2%, energy consumption is reduced by 22.1 - 29.9%, throughput is increased by 26.1 - 31.7%, compared to the existing schemes.
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