Robust Edge Intelligence for Urban Ecological Control

Can adaptive numerical precision coexist with reliable ecological decisions during seasonal shift? This review answers by treating edge robustness as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is distributed environmental sensing, where an optimized service can create privacy, security, and distributional harms that are invisible in its technical objective. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of environmental sensing, mobility and service records, infrastructure graphs, threat reports, and planning constraints; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.

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