EvoPhySec: A Multi-Objective Evolutionary Framework for Adaptive Calibration of Physics-Informed Anomaly Detection in Smart City IoT Networks

Physics-informed anomaly detectors on smart city IoT gateways face a conflict that static calibration cannot solve. Higher detection accuracy (F1) costs inference latency and edge energy. The trade-off differs across urban sensor domains. PhySec-Edge showed that hybrid physics-informed edge-AI detection works in industrial IoT. Its calibration is static. Transferred to smart city verticals with different physical constraints, it degrades. EvoPhySec addresses this problem by introducing a multi-objective evolutionary calibration layer that adapts PhySec-Edge to five heterogeneous smart city verticals under simultaneous accuracy, latency, and energy constraints. We treat the Physics Validation Engine (PVE) thresholds, the Edge AI Detection Engine (EADE) ensemble weights, and the decision rule parameters as one three-objective optimization problem. It is solved with NSGA-III plus the Physics-Constraint Preservation Operator (PCPO), which keeps candidate solutions physically feasible during the search. Evaluated on a synthetic multi-domain smart city dataset (n = 47,250 samples, five urban verticals, eleven attack classes, five random seeds) and validated on BATADAL and a CIC-IoT2023-inspired benchmark, EvoPhySec achieves mean F1 = 0.847 ± 0.004 at 34.2 ms latency and 0.71 W edge power, Pareto-dominating the static baseline on all three objectives. The ablation study isolates the PCPO contribution at +0.031 HVI over unconstrained NSGA-III. Cross-domain transfer analysis identifies a three-vertical transferable cluster and confirms that critical infra-structure requires domain-specific calibration. These results are demonstrated on synthetic data; real-world multi-vertical validation remains future work.