A secured authentication and DSM‐KL ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple WSN

Abstract

SummaryBlock chain is extensively seen as a potential alternative in the safety and efficiency problems of the vast internet of things (IoT) data to allow safe and successful data storage/processing/sharing. In this manuscript, a secured authentication and deep slime mould optimized kernel learning (DSM‐KL) ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple wireless sensor network (WSN) is proposed to recover data security performance and efficiency. To reflect the reality of the multi‐WSN network better, local block chain and public block chain are deployed, and a hybrid block chain model is created as per the various capabilities and energy of different nodes. A multi‐WSN network model is intended. It has numerous nodes on IoT. Based on dissimilar functions of nodes, the IoT nodes are divided into base stations, cluster heads and ordinary nodes based on its capabilities that facilitate management and cooperation of nodes. A DSM‐KL algorithm for dynamically selecting/adjusting block producer, consensus algorithm, block size and block interval to recover efficiency to handle both dynamic and broad dimensional properties of IoT systems is designed. The simulation process is executed in the MATLAB platform. The proposed DSM‐KL attains lower drop 27.53%, higher delivery ratio 28.56%, lower drop 28.64%, lower energy depletion rate 38.63%, higher network active time 38.47%, lower overhead 26.47% and higher throughput 27.67%, and the performances are compared with the existing methods such as deep reinforcement learning (DRL) and block chain and reinforcement learning (RLBC), respectively.