评估间歇供电物联网中截止时间约束推理的固定批量报告
Assessing Fixed-Batch Reporting for Deadline-constrained Inference in Intermittently Powered IoT
浏览论文内容
中文总结 AI 辅助
针对间歇供电物联网的截止时间约束推理，提出统一分析框架，精确比较固定批量报告策略，证明长期状态良好定义，并揭示批量大小受多种因素影响。
中文摘要 AI 辅助
能量采集物联网（IoT）设备必须决定是立即传输每个观测值，还是积累多个观测值后再向边缘报告。早期报告使边缘更早获得初始观测的信息，但需要更多的传输动作，而更大的批量节省报告次数，但延迟了该信息并保留了更多状态。我们开发了一个统一的分析框架，以在具有重复、截止时间约束的推理循环的场景中比较这种固定批量选择。每种配置处理相同的顺序观测，如果所有报告到达，则产生相同的最终后验，从而能够对批量引起的持久状态、动作和能量成本进行受控比较。利用连续排名概率得分（CRPS）下贝叶斯风险的降低，我们将长期及时价值精确表示为统计价值加权的期望决策加权报告可用性之和。这将观测的推断价值与其在应用相关决策时间的可用性分开。我们构建了精确的马尔可夫奖励模型，共同考虑了间歇性采集、有限存储、能量结转、不可靠传输、重传和截止时间。我们证明了长期状态是良好定义的，并推导出精确的批量大小比较、时间基准以及采集律效应分解为循环内和结转贡献。可直接评估的高斯和高斯混合信念特例表明，优选的批量大小可能随先验、决策时间分布、链路可靠性、采集能量统计和积累成本而变化。
英文摘要
Energy-harvesting Internet of Things (IoT) devices must decide whether to transmit each observation immediately or accumulate several observations before reporting to the edge. Early reports make information from initial observations available sooner at the edge but require more transmission actions, whereas larger batches save reports while delaying that information and retaining more state. We develop a unified analytical framework to compare this fixed-batching choice in scenarios with repeated, deadline-constrained inference cycles. Every configuration processes the same ordered observations and yields the same final posterior if all reports arrive, enabling a controlled comparison of the persistent state, actions, and energy costs induced by batching. Using reductions in Bayesian risk under the continuous ranked probability score (CRPS), we express long-run timely value exactly as a statistical-value-weighted sum of expected decision-weighted report availabilities. This separates the inferential value of observations from their availability at application-relevant decision times. We construct exact Markov-reward models accounting jointly for intermittent harvesting, finite storage, energy carry-over, unreliable delivery, retransmissions, and deadlines. We prove that the long-run regime is well defined and derive exact batch-size comparisons, timing benchmarks, and a decomposition of harvesting-law effects into within-cycle and carry-over contributions. Directly evaluable Gaussian and Gaussian-mixture belief specializations show that the preferred batch size can change with the prior, the decision-time profile, link reliability, harvested-energy statistics, and accumulation cost.
发表机构
- University of Oulu（奥卢大学）
机构由 AI 辅助整理，请以论文原文为准。
