Metacognitive Selective Ensemble for Mobile Systems

2026-09-25

AI Summary

This study addresses the high execution costs of deep ensemble models in mobile sensing and the additional computational overhead incurred by adaptive selection methods that must evaluate inactive candidates. To overcome these limitations, this work proposes MetaSE (Metacognitive Selective Ensemble), a framework that maintains a small active subset by exploiting the short-term persistence of model reliability. Based on posterior evidence, MetaSE dynamically removes unreliable members and triggers lightweight routing for their replacement. This stateful design enables efficient proactive ensemble inference by accessing the diversity of a large model pool without requiring full-pool evaluation. Experimental results on human activity recognition (HAR) datasets demonstrate that MetaSE achieves accuracy comparable to full ensembles while delivering a 2.7× inference speedup and reducing memory footprint by 69% during edge deployment on a Raspberry Pi 4B.

Abstract

Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few members reduces this cost, yet adaptive selection often requires additional model execution to obtain reliable evidence about inactive candidates. We present MetaSE, an active ensemble framework that exploits short-term persistence in per-model reliability. MetaSE maintains a small active set across windows, uses post-execution evidence to reject unreliable members, and invokes lightweight routing only when replacement is needed. This stateful design accesses the diversity of a larger pool without repeated full-pool evaluation. Across four HAR datasets and four model architectures, MetaSE consistently improves over a fixed three-model ensemble and achieves accuracy comparable to substantially more expensive adaptive and full-ensemble inference. On a Raspberry Pi 4B, MetaSE is 2.7x faster and uses 69% less memory than full ten-model inference.

Problem

• mobile sensing
• deep ensemble
• computational cost
• model selection
• ensemble pruning

Innovation

• Metacognitive Selective Ensemble
• Mobile Sensing
• Stateful Active Routing
• Lightweight Routing
• Deep Ensembles