Document Type : Research Articles
Authors
Department of Computer Engineering , ST.C. ,Islamic Azad University ,Tehran , Iran
Abstract
The rapid expansion of the Internet of Things (IoT) has raised critical security concerns across its heterogeneous and resource-constrained networks. Ensuring robust protection with minimal overhead requires identifying a minimal set of key nodes that can provide wide security coverage. This study introduces a novel framework that integrates graph-theoretic modeling with metaheuristic optimization to enhance IoT network security. The proposed approach formulates the problem as an External Independent Dominating Set (EIDS), where selected security nodes ensure full coverage while maintaining independence to reduce correlated vulnerabilities. Four metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO), and Simulated Annealing (SA)—are implemented and compared using multiple network topologies, including random graphs, wireless sensor networks, and real-world smart city infrastructures. Experimental results show that SA achieves the best coverage-to-cost ratio, while PSO offers superior computational efficiency. BCO demonstrates strong independence enforcement, and GA provides balanced performance. The proposed framework achieves over 90% coverage with minimal node overhead, demonstrating scalability and robustness. This work establishes a foundation for hybrid and adaptive metaheuristic strategies in large-scale IoT security deployment.
Keywords
- External Independent Dominating Set
- IoT security
- graph theory
- metaheuristic optimization
- security-node placement
Main Subjects
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