Authors:
Amsaveni Manigandan, M. Saranya

Addresses:
Department of Computer Science, P. K. R Arts College for Women, Gobichettipalayam, Tamil Nadu, India.

Abstract:

Wireless Sensor Networks (WSNs) play a fundamental role in contemporary data-centric applications, but are severely energy-limited and vulnerable to highly sophisticated insider attacks. Current protocols tend either to treat network lifetime and security as separate concerns (resulting in brittle and short-lived networks) or not consider the problem at all. This paper proposes a new integrated framework: The Adaptive Trust-Driven Metaheuristic Clustering with Energy-Aware Decision Intelligence (ATDMC-EADI). This architecture employs a multi-metric, adaptive trust-based model to dynamically monitor node behaviour, detecting malicious behaviours, including grey hole and black hole attacks. This trust factor, along with the remaining energy, is directly applied to a hybrid metaheuristic clustering algorithm for selecting resilient and high-energy cluster heads. This secure topology is then controlled by a high-level decision intelligence layer, which optimizes routing paths and sleep schedules. The ATDMC-EADI model is validated in the NS-3 (Network Simulator 3) environment using new data. We adopted a custom-created dataset, WSN-TrustSim-498, which contains 498 different network operating scenarios under multiple attack settings. The findings indicate that our integrated mechanism significantly outperforms baseline methods in terms of network lifetime, data correctness, and resilience against diverse insider threats.

Keywords: Wireless Sensor Networks (WSN); Network Resilience; Adaptive Trust; Metaheuristic Clustering; Energy-Aware; Decision Intelligence; Adversarial Conditions; Trust Models.

Received on: 24/10/2024, Revised on: 18/01/2025, Accepted on: 20/02/2025, Published on: 07/09/2025

DOI: 10.69888/FTSCS.2025.000474

FMDB Transactions on Sustainable Computing Systems, 2025 Vol. 3 No. 3, Pages: 157-165

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