Security of Wireless Sensor Networks in the Era of Artificial Intelligence and IoT: A Systematic Literature Review

Wireless Sensor Networks (WSNs) integrated with the Internet of Things (IoT) underpin critical modern infrastructures, including smart cities, healthcare, and industrial automation. However, the inherent resource constraints of sensor nodes, decentralized architectures, and heterogeneous communication protocols expose these networks to severe security vulnerabilities. Traditional security mechanisms, such as static cryptographic protocols and rule-based intrusion detection systems, struggle to mitigate sophisticated, dynamic, and zero-day threats. To address these challenges, this research direction explores the integration of artificial intelligence (AI) paradigms to establish adaptive, autonomous, and intelligent security frameworks. This systematic review analyzes recent advancements in machine learning, deep learning, reinforcement learning, federated learning, blockchain, and edge intelligence tailored for WSN-IoT environments.

The investigation focuses on key security domains, including anomaly detection, threat prediction, decentralized authentication, privacy preservation, and secure communication protocols. By evaluating these methodologies based on computational overhead, detection accuracy, and resource efficiency, this work provides structured guidance for designing robust, AI-driven security architectures. The review serves as a foundational framework for implementing simulation-based evaluations of edge-assisted intrusion detection models in resource-constrained embedded systems.

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