The rapid growth of the Internet of Things (IoT) has exposed billions of interconnected, heterogeneous, and resource-constrained devices to increasingly sophisticated threats. To evaluate the readiness of current intrusion detection systems (IDSs), this study reviews 32 recent IoT-IDS proposals spanning conventional, machine-learning, deep-learning, and hybrid approaches. Each system is assessed against 10 criteria that reflect practical IoT requirements, including real-time performance, latency, lightweight design, detection accuracy, mitigation capabilities, integrated detection-and-mitigation workflows, adaptability, resilience to advanced attacks, validation in realistic environments, and scalability. The results indicate that although many approaches achieve high detection accuracy, most do not meet real-time and lightweight thresholds commonly cited in IoT deployment literature. Mitigation features are often absent, adaptability is rarely implemented, and 29 out of 32 studies rely solely on offline datasets, thereby limiting confidence in their robustness to deployment. Scalability remains the most significant limitation, as none of the reviewed IDSs have tested their performance under realistic multi-node or high-traffic conditions, even though scalability is critical for large IoT ecosystems. Overall, the review suggests that future IoT IDS research should move beyond accuracy-focused models and toward lightweight, adaptive, and autonomous solutions that incorporate mitigation, support real-time inference, and undergo standardized evaluations under real-world operating conditions.