Department of Computer Science, Comsats University, Islamabad, 45550, Pakistan.
Faculty of Computing and Informatics, University of Malaysia Sabah, Malaysia.
Comput Intell Neurosci. 2022 Apr 11;2022:5112375. doi: 10.1155/2022/5112375. eCollection 2022.
Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using -mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
数据冗余或融合是与资源受限网络（如无线传感器网络（WSN）和物联网（IoT））相关的常见问题之一。为了解决这个问题，文献中提出了许多数据聚合或融合方案。通常，它用于减小收集数据的大小，从而提高物联网在拥塞控制、数据准确性和生命周期方面的性能。然而，这些方法在数据精炼阶段不考虑设备（在这种情况下为簇头）的邻居信息。在本文中，提出了一种智能和智能邻居启用的数据聚合方案，其中每个设备（簇头）都被限制在将收集的数据发送到相关服务器模块之前对其进行精炼。为此，提出的数据聚合方案分为两个阶段：（i）基于 MAC 地址和位置识别相邻节点，以及（ii）使用均值聚类算法和支持向量机（SVM）进行数据聚合。此外，每个 CH 都足够智能，只能比较相邻节点的数据集；也就是说，根本不会比较非邻居的数据。这些算法在网络模拟器 2（NS-2）中实现，并根据各种性能指标进行了评估，例如数据冗余率、生命周期和能量效率。仿真结果验证了所提出方案的性能优于现有方法。
