Resource-constrained Internet of Things (IoT) devices and wireless sensor networks (WSNs) remain difficult to secure using conventional security mechanisms because their sensing, forwarding, and processing nodes operate with limited memory, computation, energy, bandwidth, and software-update capability. These limits expose constrained deployments to attacks on end nodes, routing paths, wireless links, and sensed data. Recent research has therefore proposed lightweight intrusion detection (IDS) and protection methods that reduce input dimensionality, model size, computation, communication overhead, authentication cost, or energy use. The literature, however, remains uneven in how it defines lightweightness, relates lightweight design to security risk, and demonstrates deployability under constrained operation. This article reviews recent lightweight security risk analysis and IDS research for constrained IoT and WSN systems, with emphasis on studies published from 2023 to 2026. It examines feature selection, compact machine learning, compressed deep learning, TinyML, edge-assisted detection, federated learning, lightweight authentication, trust-based scoring, cryptography, and energy-aware routing. The review compares technique families, attack coverage, resource-efficiency evidence, datasets, metrics, and reporting practices. It also separates IoT and WSN assumptions because IoT IDS papers often rely on flow datasets and edge deployment, whereas WSN studies more often address routing attacks, energy depletion, topology instability, and node compromise. The main contribution is a resource-aware taxonomy and comparative evaluation framework that separate lightweight security claims from deployment evidence, clarify IoT and WSN evaluation assumptions, and identify the minimum reporting requirements needed to judge operational feasibility. The reviewed studies indicate that resource metrics should be treated as primary evaluation outcomes and that lightweight security must be assessed through the joint assessment of detection quality, risk reduction, and operational feasibility.
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Osama Ahmed Khashan (2026) studied this question.
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