Ordered transmissions reduces the number of nodes that transmit in a wireless sensor network (WSN) and yet achieves the same performance as the conventional unordered transmissions scheme (UTS) in which all nodes transmit. However, it breaks down in energy harvesting (EH) WSNs because of missed transmissions by EH nodes that lack sufficient energy. For the Bayesian detection framework, we propose a novel scheme that addresses this challenge for the general case in which the log-likelihood ratio is bounded and has a continuous distribution function. Given the probability that a node misses its transmission, it reduces the average number of transmissions compared to UTS. For truncated Gaussian statistics, we then propose a novel refinement that requires even fewer transmissions and that simultaneously lowers the error probability. We also analyze its performance and show that it lends itself to a computationally-efficient Monte Carlo evaluation. When the time evolution of the battery energies of the nodes is tracked and the probability of a missed transmission becomes a function of the scheme itself, the proposed schemes achieve a markedly lower error probability than both UTS and sequential detection, except when the energy harvested is so less that very few nodes can transmit.

中文翻译：

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能量收集无线传感器网络中节能检测的有序传输

有序传输减少了在无线传感器网络 (WSN) 中传输的节点数量，但仍实现了与所有节点都传输的传统无序传输方案 (UTS) 相同的性能。然而，由于缺乏足够能量的 EH 节点错过传输，它在能量收集 (EH) WSN 中出现故障。对于贝叶斯检测框架，我们提出了一种新方案，可以解决对数似然比有界且具有连续分布函数的一般情况下的这一挑战。给定节点错过传输的概率，与 UTS 相比，它减少了平均传输次数。对于截断的高斯统计，我们提出了一种新的改进，它需要更少的传输，同时降低了错误概率。我们还分析了它的性能，并表明它适用于计算效率高的 Monte Carlo 评估。当跟踪节点电池能量的时间演变并且丢失传输的概率成为方案本身的函数时，所提出的方案实现的错误概率明显低于 UTS 和顺序检测，除非收集的能量为少到很少有节点可以传输。