Energy aware routing with optimal deep learning based anomaly detection in 6G-IoT networks

Abstract

The sixth generation (6G) wireless communication networks are being planned for the transformation of client services and applications through the Internet of Things (IoT) technology that is headed towards a completely intelligent and autonomous system. The IoT-6G network offers ubiquitous applications on the basis of wireless communication technologies. However, various problems need to be critically addressed in order to realize this technological application. Primarily, the devices encounter energy constraints that prevent it from smart functioning due to limited battery power; so the connectivity issue arises resulting in the failure of the connections, once the device’s energy gets exhausted. Besides, security also remains a challenging issue which can be solved with the help of Machine Learning (ML) and Deep Learning (DL)-based anomaly detection techniques. In this background, the current study presents a Metaheuristic Energy-Aware Routing with Optimal DL-based Anomaly Detection Technique (MER-ODLADT) for 6G-IoT networks. The presented MER-ODLADT technique encompasses two major processes namely, routing and anomaly detection. For the routing process, the proposed MER-ODLADT technique applies the Marine Predator’s Optimization (MPO) algorithm with a fitness function involving multiple input parameters. On the other hand, the MER-ODLADT technique employs the Eco-Geography Optimization with Deep Belief Network (EGODBN) model for anomaly detection and classification. The performance of the proposed MER-ODLADT technique was validated using a benchmark dataset and the results were examined under distinct measures. The experimental outcomes demonstrate the enhanced performance of the proposed MER-ODLADT technique over other existing approaches with a maximum sensitivity of 98.34 %, specificity of 97.91 %, accuracy of 99.43 %, precision of 99.58 % and an F1-score of 98.41 %.