AI-driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G

【Author】 Jadav, Nilesh Kumar; Nair, Anuja R.; Gupta, Rajesh; Tanwar, Sudeep; Lakys, Yahya; Sharma, Ravi

【Source】INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS

【影响因子】1.882

【Abstract】Internet of things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. However, efficient network management and message exchange are challenging in a dynamic and energy-constrained IoT environment. Hence, for efficient message passing in IoT applications, softwarization of the IoT network is essential, wherein the logical control plane is decoupled from the data plane consisting of hardware devices such as routers and switches. Using softwarization, a centralized software-defined networking (SDN) controller is responsible for routing data packets from source to destination in a dynamic environment. However, to reduce the computational overhead of filtering malicious and nonmalicious packets, artificial intelligence (AI) classifiers prove beneficial. Moreover, such challenges have trade-offs with network requirements such as ultralow latency, high reliability, and higher data rates. Motivated by this, we propose an AI-enabled network softwarization scheme for efficient message exchange under the 6G network. Lastly, the performance of the proposed scheme is evaluated with different performance metrics, such as accuracy, precision, f1-score, packet drop ratio, and latency. The empirical result revealed that the proposed scheme outperforms in terms of accuracy and controller efficiency, that is, 81.64% and 82.2%, respectively.

【Keywords】6G network; artificial intelligence; internet of things; LSTM; network softwarization; software-defined networking

【发表时间】

【收录时间】2022-09-15

【文献类型】实证数据

【主题类别】

区块链技术-协同技术-物联网