School of Information and Communication Technology, Hanoi University of Science and Technology, 11615 Hanoi, Vietnam.
Sensors (Basel). 2018 Sep 15;18(9):3118. doi: 10.3390/s18093118.
A correlation characteristic has significant potential advantages for the development of efficient communication protocols in wireless sensor networks (WSNs). To exploit the correlation in WSNs, the correlation model is required. However, most of the present correlation models are linear and distance-dependent. This paper proposes a general distance-independent entropy correlation model based on the relation between joint entropy and the number of members in a group. This relation is estimated using entropy of individual members and entropy correlation coefficients of member pairs. The proposed model is then applied to evaluate two data aggregation schemes in WSNs including data compression and representative schemes. In the data compression scheme, some main routing strategies are compared and evaluated to find the most appropriate strategy. In the representative scheme, with the desired distortion requirement, a method to calculate the number of representative nodes and the selection of these nodes are proposed. The practical validations showed the effectiveness of the proposed correlation model and data reduction schemes.
相关性特征在开发无线传感器网络（WSN）中的高效通信协议方面具有显著的潜在优势。为了利用 WSN 中的相关性，需要使用相关性模型。然而，目前大多数相关性模型都是线性的，并且依赖于距离。本文提出了一种基于联合熵与组内成员数量之间关系的通用距离独立熵相关性模型。使用各个成员的熵和成员对的熵相关系数来估计这种关系。然后，将所提出的模型应用于评估 WSN 中的两种数据聚合方案，包括数据压缩和代表方案。在数据压缩方案中，比较和评估了一些主要的路由策略，以找到最合适的策略。在代表方案中，根据所需的失真要求，提出了一种计算代表性节点数量和选择这些节点的方法。实际验证表明，所提出的相关性模型和数据缩减方案是有效的。
