This article focuses on the distributed non-Bayesian quickest change detection based on the cumulative sum (CUSUM) algorithm in an energy harvesting wireless sensor network, where the distributions before and after the change point are assumed to be known. Each sensor is powered by randomly available harvested energy from the surroundings. It samples the observation signal and computes the log-likelihood ratios (LLRs) of the aforementioned two distributions if enough energy is available in its battery for sensing and processing the sample (EsE_{s}). Otherwise, the sensor decides to abstain from the sensing process during that time slot and waits until it accumulates enough energy to perform the sensing and processing of a sample. This LLR is used for performing the CUSUM test to arrive at local decisions about the change point, which are then combined at the fusion center (FC) by a predecided fusion rule to arrive at a global decision. In this article, we derive the asymptotic expressions (as the average time to a false alarm goes to infinity) for the expected detection delay and the expected time to a false alarm at the FC for three common fusion rules, namely, or, and, and rr out of NN majority rule, respectively, by considering the scenario, where the average harvested energy at each sensor is greater than the energy required for sensing and processing a sample EsE_{s}. To this end, we use the theory of order statistics and the asymptotic distribution of the first passage times of the local decisions. Numerical results are also provided to support the theoretical claims.

中文翻译：

---

能量收集传感器分布式非贝叶斯最快变化检测的渐近性能分析


本文重点研究能量收集无线传感器网络中基于累积和（CUSUM）算法的分布式非贝叶斯最快变化检测，其中假设变化点之前和之后的分布已知。每个传感器均由从周围环境中随机收集的能量供电。如果电池中有足够的能量用于感测和处理样本 (EsE_{s})，它会对观测信号进行采样并计算上述两个分布的对数似然比 (LLR)。否则，传感器决定在该时隙内放弃感测过程，并等待，直到它积累足够的能量来执行样本的感测和处理。该 LLR 用于执行 CUSUM 测试以得出有关变化点的局部决策，然后通过预先确定的融合规则在融合中心 (FC) 进行组合以得出全局决策。在本文中，我们针对三种常见的融合规则（即，or、and、和 rr 分别脱离 NN 多数规则，考虑以下场景：每个传感器收集的平均能量大于感测和处理样本 EsE_{s} 所需的能量。为此，我们使用阶次统计理论和局部决策首次通过时间的渐近分布。还提供了数值结果来支持理论主张。