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

Abstract

SummaryInternet 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.

An application of meta‐heuristic and nature‐inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

“…Everything associated with humans should be equipped with smart chips that integrate actuators, sensors, and transceivers as part of the Internet of Things (IoT). 1 Within a specific range, smart devices with smart chips may connect and create networks. These networks can be further linked to the Internet.…”