Self-Organized Intuitionistic Secure Routing via Stochastic Cluster Head Selection and C

Abstract

Applications include environmental monitoring, industrial automation, healthcare and patient monitoring, smart cities, precision agriculture, disaster management,
and military surveillance all make extensive use of mobile wireless sensor networks, or MWSNs. They do, however, confront significant obstacles, such as the
creation of void-holes and energy-holes, in which packets fail to reach the sink because of areas with no active nodes or unequal energy consumption across cluster
leaders. Packet losses, decreased throughput, longer end-to-end latency, and a shorter network lifetime are the outcomes of these problems. Particularly in dense
and large-scale networks, current clustering and routing techniques frequently fall short of simultaneously addressing void and energy-hole issues while preserving
secure and effective communication. This paper devised a new Hexagonal Grid-Based Clustering, Stochastic Cluster-Head Selection, Chaotic Spider Optimization,
and Intuitionistic Fuzzy Logic for Secure Routing in MWSN (HQS-IFR). The proposed framework that combines intuitionistic fuzzy logic (IFL)-based routing for
secure and optimal path selection, stochastic cluster-head selection to improve robustness and load balancing, chaotic spider optimization (CSO) to mitigate energyhole formation, and hexagonal grid-based clustering for uniform node distribution. According to simulation studies, HQS-IFR outperforms conventional methods
by up to 10% in packet delivery ratio, 15% in throughput, 20% in end-to-end delay, and 12% in packet loss. It greatly improves the dependability, energy efficiency,
and general performance of MWSNs by efficiently reducing void holes, balancing energy consumption, and guaranteeing secure routing, offering a better option
than current models