Akademik Veri Yönetim Sistemi
Computers, cilt.15, sa.4, 2026 (ESCI, Scopus)
Large-scale smart city Internet of Things (IoT) infrastructures must simultaneously provide strong cybersecurity protection, real-time anomaly detection, and energy-efficient operation despite the strict resource limitations of sensing devices. The current body of research typically addresses secure data management, edge intelligence, or energy optimization in isolation, leaving a practical gap in unified frameworks that jointly optimize these objectives. This paper proposes a jointly co-designed energy-aware cybersecurity framework that integrates lightweight secure sensing, hybrid edge-based anomaly detection, Practical Byzantine Fault Tolerance (PBFT)-enabled blockchain integrity, and Grey Wolf Optimization (GWO)-driven edge deployment within a single end-to-end architecture. The practical contribution of the proposed framework lies in enabling tamper-evident trusted sensing, real-time detection of both data and energy anomalies, and communication-efficient operation suitable for scalable smart city deployments. The simulation results demonstrate that the proposed method achieves strong operational efficiency, reaching up to 234.6 transactions per second while maintaining end-to-end latency of approximately 140–194 ms and reducing total energy consumption to about 1.68 J under high-load conditions. In addition, the hybrid anomaly detection mechanism achieves an F1-score of 0.985 and ROC-AUC of 0.992, confirming strong detection capability under realistic sensing and attack scenarios.