Boundary Coherence as a Security Posture Metric for Mobile-IoT Edge Systems: A Structural Complement to Behavioral Intrusion Detection

The behavioral intrusion detection literature has produced a technically mature paradigm for identifying attack events at IoT edge boundaries — deep reinforcement learning, convolutional neural network, and ensemble model approaches achieving 96–99% detection accuracy on benchmark datasets. Even frameworks characterized as proactive remain fundamentally dependent on behavioral signature recognition: measurement begins when an anomalous pattern is detectable. This working paper argues that a structurally distinct and complementary measurement problem remains unaddressed: the coherence state of the mobile-IoT edge boundary prior to attack signature emergence. Applying the Emergent Order Systems (EOS) C-L-R triad — a substrate-independent measurement framework — to the mobile-IoT edge context, the paper proposes a posture-level measurement layer that characterizes structural degradation gradients before behavioral anomalies become detectable. The enterprise AI security posture domain (Kristenson, 2025, DOI: 10.5281/zenodo.18898984) serves as the intermediate validation layer, with structural isomorphism demonstrated between enterprise boundary coherence failure and mobile-IoT edge boundary coherence failure. The triad's three axes — C-Index (drift), L-Index (boundary transfer integrity), and R-Index (margin proximity) — are mapped to the mobile-IoT failure modes that precede detectable attack conditions. The contribution is structural, not prescriptive. No detection algorithms, mitigation architectures, or control mechanisms are proposed. The paper presents a measurement language for the pre-detection window, and defines the boundary conditions under which the framework would offer no additional explanatory surface beyond behavioral detection. Part of the Emergent Order Systems / ECR framework family. Full corpus: https://zenodo.org/search?q=kristenson