标题： 无线传感器网络的寿命与分类精度的平衡 显示英文标题

标题： Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks

摘要： 无线传感器网络由分布式传感器组成，可用于信号检测或分类。 假设的似然函数通常事先未知，因此需要通过监督学习来学习决策规则。 一个特定的此类算法是Fisher判别分析（FDA），其分类精度已在无线传感器网络的背景下进行了先前的研究。 然而，以往的工作并未考虑传感器网络的通信协议或电池寿命；本文通过提出一种模型扩展了现有研究，该模型捕捉了电池寿命与分类精度之间的关系。 为了实现这一点，我们将FDA与一个模型结合，该模型捕获了载波侦听多路访问（CSMA）算法的动力学，这是用于调节传感器网络通信的随机接入算法。 这使我们能够研究分类精度、电池寿命和用于学习的努力之间的相互作用，以及CSMA的退避率对准确性的影响。 我们刻画了训练阶段长度和精度之间的权衡，并表明由于训练样本大小的变化和过拟合，精度在退避率上是非单调的。 显示英文摘要

摘要： Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA), the classification accuracy of which has been previously studied in the context of wireless sensor networks. Previous work, however, does not take into account the communication protocol or battery lifetime of the sensor networks; in this paper we extend the existing studies by proposing a model that captures the relationship between battery lifetime and classification accuracy. In order to do so we combine the FDA with a model that captures the dynamics of the Carrier-Sense Multiple-Access (CSMA) algorithm, the random-access algorithm used to regulate communications in sensor networks. This allows us to study the interaction between the classification accuracy, battery lifetime and effort put towards learning, as well as the impact of the back-off rates of CSMA on the accuracy. We characterize the tradeoff between the length of the training stage and accuracy, and show that accuracy is non-monotone in the back-off rate due to changes in the training sample size and overfitting.