Energy- and trust-aware secure routing algorithm for big data classification using MapReduce framework in IoT networks

Abstract

Due to the open nature of wireless data transmission, routing and data security pose an important research challenge in the Internet of Things (IoT)-enabled networks. Also, the characteristic features, like constrained resources, heterogeneity, uncontrolled environment, and scalability requirement, make the security issues even more challenging. Hence, an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm (mod-EHTARA) is proposed to increase the energy efficiency and the lifespan of the nodes. The proposed mod-EHTARA is designed by adopting the Link Lifetime (LLT) model with the traditional EHTARA. The optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric, which considers the factors like delay, LLT, energy, and trust. The big data classification process is carried out at the Base Station (BS) using the MapReduce framework. Accordingly, the big data classification is progressed using a stacked autoencoder, which is trained by the Adaptive E-Bat algorithm. The Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm (BA) and Exponential Weighted Moving Average (EWMA). The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.