Confidence-Aware Cooperative Expert Ensemble for Anomaly Detection in Multivariate Environmental IoT Time Series

Internet of Things (IoT) environmental monitoring systems continuously generate multivariate sensor data whose reliability depends on the effective detection of heterogeneous abnormal behavior. This paper proposes a Confidence-Aware Cooperative Expert Ensemble (CACE) for multivariate IoT time series anomaly detection. CACE decomposes the detection problem into four specialized Expert libraries targeting Spike, Drift, Flat-Line, and Dropout Expert anomalies. Candidate detectors are combined through reward-derived weighting and Expert confidence estimation, while selective reliability modulation and a class-balanced logistic Coordinator integrate complementary evidence into the final decision. Experiments use real temperature, relative humidity, and illuminance measurements with controlled anomaly injections, five fixed random seeds, nine sensor-board streams, and three anomaly load conditions. CACE achieved mean pointwise F1 scores of 0.331, 0.445, and 0.496, and Event F1 scores of 0.714, 0.638, and 0.513, under Low, Medium, and High loads, respectively. Paired analysis showed significant improvements over the Single-Expert reference across all loads, while comparisons with stronger deterministic, classical, and deep baselines revealed method-dependent trade-offs rather than uniform superiority. Ablation confirmed complementary contributions among specialized Experts, and sensitivity analysis indicated robustness to moderate perturbations of most parameters. On Raspberry Pi 5, the complete end-to-end pipeline required 0.01283 ms per sample, corresponding to approximately 77,994 samples/s, without thermal throttling. These results indicate that CACE provides an interpretable and computationally lightweight cooperative framework for heterogeneous anomaly detection at the IoT edge.