Viveka: Context-Aware Sensing for Energy Efficiency in Smart Wearables

2026-08-05

AI Summary

This work addresses the challenges of high energy consumption and limited storage in intelligent wearable devices under multi-sensor, high-fidelity sensing. Existing approaches to jointly optimizing sensor selection and sampling rates either lack fine-grained contextual awareness or incur excessive overhead, while often neglecting the impact of context misclassification on accuracy. To overcome these limitations, we propose Viveka, a lightweight context-aware framework that uniquely integrates context recognition confidence with a stability-gated mechanism. A always-on controller dynamically switches between aggressive sensing configurations and safe fallback strategies based on contextual reliability. Context-specific configurations are derived through permutation feature importance and spectral energy analysis. Evaluated on the MHEALTH and PAMAP2 datasets, Viveka reduces energy consumption by up to 75% and data volume by up to 78% compared to baselines, with only a modest 3–5% drop in classification accuracy.

Abstract

The proliferation of multi-sensor Internet of Things (IoT) systems, from Body Sensor Networks (BSNs) to industrial monitoring, is increasingly constrained by strict energy budgets and limited on-device storage. Continuous high-fidelity sensing leads to rapid battery depletion and data gaps that compromise application reliability. Existing strategies address this through sensor selection or adaptive sampling in isolation, or rely on computationally expensive agents for joint optimization. They lack context granularity or introduce significant overhead, and critically, they do not account for the risk that an aggressive, context-specific sensing policy applied to a misidentified context degrades accuracy. In this paper, we formulate joint sensor and sampling-rate selection as an NP-hard energy-minimization problem and propose Viveka, a lightweight, context-aware framework. Viveka couples a cheap, always-on controller that estimates context and how much to trust that estimate with a stability and confidence gated policy that applies an aggressive per-context configuration only when context is certain, and falls back safely otherwise. Per-context configurations are instantiated using permutation feature importance and spectral energy analysis. Evaluation on the MHEALTH and PAMAP2 datasets shows that Viveka achieves up to 75% energy savings and 78% data reduction over standard baselines in a best-case configuration, while maintaining classification accuracy within 3-5% of the baselines.

Problem

• energy efficiency
• context-aware sensing
• smart wearables
• sensor selection
• adaptive sampling

Innovation

• context-aware sensing
• energy efficiency
• sensor selection
• adaptive sampling
• confidence-gated policy