IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Abstract

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.