论文标题
一种监督的主动学习方法，用于识别无线传感器网络中的关键节点
A supervised active learning method for identifying critical nodes in Wireless Sensor Network
论文作者
论文摘要
无线传感器网络（WSN）的能源效率取决于其主要特征，包括啤酒花，用户的位置，分配的功率和继电器。但是，识别对这些特征有更大影响的节点受到大量计算开销和能源消耗的影响。在本文中，我们提出了一种积极的学习方法，以解决识别WSN中关键节点的计算开销。所提出的方法可以克服识别非关键节点的偏见，而在微调中需要更少的精力来适应WSN的动态性质。这种方法从聚类和分类模块的合作中受益于迭代地减少典型的监督学习场景中所需的数据数量，并在存在非信息示例（即非临界节点）的情况下提高准确性。实验表明，与最先进的方法相比，所提出的方法具有更大的灵活性，该方法将在大规模WSN环境中使用，即第五代移动网络（5G）和大量分布的物联网（即传感器网络），它可以延长网络寿命。
Energy Efficiency of a wireless sensor network (WSN) relies on its main characteristics, including hop-number, user's location, allocated power, and relay. Identifying nodes, which have more impact on these characteristics, is, however, subject to a substantial computational overhead and energy consumption. In this paper, we proposed an active learning approach to address the computational overhead of identifying critical nodes in a WSN. The proposed approach can overcome biasing in identifying non-critical nodes and needs much less effort in fine-tuning to adapt to the dynamic nature of WSN. This method benefits from the cooperation of clustering and classification modules to iteratively decrease the required number of data in a typical supervised learning scenario and to increase the accuracy in the presence of uninformative examples, i.e., non-critical nodes. Experiments show that the proposed method has more flexibility, compared to the state-of-the-art, to be employed in large scale WSN environments, the fifth-generation mobile networks (5G), and massively distributed IoT (i.e., sensor networks), where it can prolong the network lifetime.
