DESIGN OF AN EFFICIENT HIGH-TRUST MODEL FOR IMPROVING NETWORK COMMUNICATION CONSISTENCY VIA INCREMENTAL BIOINSPIRED OPTIMIZATIONS: HTMNCB

Abstract

Node failure in Wireless Sensor Networks (WSNs) is common, which might occur due to internal or external faults. But existing fault tolerance models are either highly complex, or showcase lower efficiency when applied to real-time scenarios. To overcome these issues, this paper proposes an efficient high-trust model for improving network communication consistency through incremental bio-inspired optimizations. The model is designed to address the challenges of maintaining consistent and reliable network communication in the presence of network failures, malicious attacks, and other unforeseen events that can disrupt network operations. The proposed model utilizes Grey Wolf Optimization (GWO) & Antlion Optimization (ALO) techniques to incrementally optimize network communication parameters and configurations in response to changing network conditions. The model's effectiveness is evaluated through simulations that demonstrate its ability to maintain consistent network communication and mitigate the impact of network failures and malicious attacks. The results of the simulations show that the proposed model improves network communication consistency while reducing the overall network downtime and increasing its trustworthiness. The high-trust model presented in this paper has significant implications for network communication in critical infrastructure systems, such as healthcare, transportation, and energy, where reliable and consistent network communication is essential under real-time scenarios.