Topology–Channel Correlation Correction for Heterogeneous Sensing Errors

Investigate and derive the correction to the zeroth-order evolution and evolutionarily stable misinformation ratio for ISAC-enabled UAV swarms when heterogeneous sensing errors are strongly correlated with network topology and local misinformation ratios, such as when edge UAVs simultaneously have high sensing error probabilities and high local misinformation ratios.

Background

The paper proves that, under a mean-field neighborhood-statistics assumption requiring the per-UAV sensing error probability to be uncorrelated with the local misinformation ratio, the population-level zeroth-order dynamics and the evolutionarily stable state depend on heterogeneous sensing errors only through their population mean. This result supports a simplified fusion-center implementation that tracks the average sensing error rather than the full error distribution.

The authors explicitly note that this conclusion may fail when physical-layer channel conditions and network topology are coupled. In particular, systematic association between high sensing errors and high local misinformation ratios could invalidate the averaging step used in the theorem. The unresolved task is therefore to determine how such correlations modify the misinformation dynamics and the resulting stable state.

References

It should be noted that the above robustness is premised on Assumption \ref{ass:mf} (uncorrelated $\varepsilon_i$ and $\rho_i$); if channel conditions are strongly coupled with network topology (e.g., edge nodes simultaneously have high $\varepsilon_i$ and high $\rho_i$), the conclusion of the theorem requires correction, and such cases are left for future work.

— Securing Cooperative Sensing in UAV Swarms Against Conformity-Driven Byzantine Attacks  (2608.28017 - Ren et al., 28 Aug 2026) in Remark following Theorem 1, Section 5.1 (Heterogeneous Sensing Errors)

And the signing episode shows the collective moving, under pressure, from unchecked trust toward verifiable claims. These are instances the record happens to contain, not a measured pattern, and the question of whether correction generally kept pace with propagation is one I leave to future work.

— The Crowd in the Machine: A Crisis-Informatics Reading of the 2026 Autonomous Agent Incidents  (2609.31060 - Simon, 25 Sep 2026) in Section 5, “Self-regulation and the condition of verifiability”