Honey robbing in honey bees is a fast, destructive event between colonies that can rapidly deplete food reserves, cause colony loss, and facilitate the spread of parasites and pathogens within an apiary. Early detection remains challenging because visual signs at early stages are often indistinguishable from normal foraging activity. This paper proposes a data-driven early warning approach for honey robbing based on online IoT monitoring of smart hives, with a focus on data quality, reproducibility, and readiness for integration into beehive monitoring systems. The study is based on a homogeneous network of ten AmoHive smart hives observed during a single nectar season, including one confirmed robbing case and nine reference hives. The paper contributes: (i) an open dataset combining raw IoT logs with standardized, cleaned, and quality-controlled time series together with a documented preprocessing workflow and provenance information; (ii) a formalization of a hidden early warning window from 10 to 1 days before the overt event, during which destructive weight loss accumulates without obvious external symptoms; and (iii) a lightweight and interpretable cumulative detector based on standard deviation, inter-hive normalization, and cumulative negative deviation, conceptually linked to approaches from statistical process control and negative cumulative summation. In the documented case, the system generated stable warnings several days before the overt robbing phase without false alarms in the studied network, demonstrating practical feasibility for precision beekeeping.