Viveka：面向智能可穿戴设备能效的上下文感知传感技术
Viveka: Context-Aware Sensing for Energy Efficiency in Smart Wearables
浏览论文内容
中文总结 AI 辅助
针对智能可穿戴设备多传感器IoT系统的能量与数据问题，提出轻量级上下文感知框架Viveka，实现最高75%节能、78%数据缩减，且分类精度与基准差距控制在3-5%内。
中文摘要 AI 辅助
从人体传感器网络（BSNs）到工业监测，多传感器物联网（IoT）系统的普及正日益受到严格的能量预算和有限的设备存储的制约。持续的高保真传感会导致电池快速耗尽，并产生数据缺口，从而降低应用的可靠性。现有策略要么仅通过传感器选择或自适应采样来解决该问题，要么依赖计算成本高昂的智能体进行联合优化；这些策略要么缺乏上下文粒度，要么会引入显著的开销，且关键在于，它们未考虑这样的风险：若将针对特定上下文的激进传感策略应用于被错误识别的上下文，会降低精度。在本文中，我们将传感器与采样率的联合选择表述为一个NP难的能量最小化问题，并提出了Viveka，这是一个轻量级的上下文感知框架。Viveka将一个廉价的始终在线控制器（用于估计上下文及该估计的可信度）与一个由稳定性和置信度门控的策略相结合，该策略仅在上下文确定时应用针对特定上下文的激进配置，否则安全地回退。针对特定上下文的配置通过排列特征重要性和谱能量分析来实例化。在MHEALTH和PAMAP2数据集上的评估表明，在最佳配置下，Viveka相比标准基准实现了高达75%的能量节省和78%的数据减少，同时保持的分类精度与基准的差距在3-5%以内。
英文摘要
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.
