In wireless sensor networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes considerable sensor node resources. Data redundancy occurs due to the spatial and temporal correlations among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates knowledge extraction by eliminating the correlated data. However, most data aggregation techniques have low accuracy because they do not consider the presence of erroneous data from faulty nodes, which represents an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels: the node level and the cluster head level. At the node level, the data aggregation is performed using the exponential moving average (EMA), and a threshold-based mechanism is adopted to detect any outliers to improve the accuracy of data aggregation. At the cluster head level, we have employed a modified version of the Euclidean distance function to provide highly refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, and energy consumption at the nodes and cluster heads and delivers highly refined, fused data to the base station.

在无线传感器网络（WSN）中，数据冗余是一个具有挑战性的问题，不仅会导致网络拥塞，而且会消耗大量传感器节点资源。由于相邻节点收集的数据之间存在空间和时间相关性，因此出现了数据冗余。数据聚合是一项杰出的技术，可以对冗余数据进行网络内过滤，并通过消除相关数据来加速知识提取。但是，大多数数据聚合技术的准确性较低，因为它们不考虑来自故障节点的错误数据的存在，这代表了开放的研究挑战。为了解决这一挑战，我们为基于集群的分层WSN提出了一种新颖，轻便，节能的基于函数的数据聚合方法。我们建议的方法在两个层面上起作用：节点级别和集群头级别。在节点级别，使用指数移动平均值（EMA）进行数据聚合，并采用基于阈值的机制来检测任何异常值，以提高数据聚合的准确性。在簇头级别，我们采用了欧几里德距离函数的修改版本，以向基站提供高度精确的聚合数据。我们的实验结果表明，我们的方法降低了节点和群集头的通信成本，传输成本和能耗，并向基站提供了高度精炼的融合数据。采用基于阈值的机制来检测异常值，以提高数据聚合的准确性。在簇头级别，我们采用了欧几里德距离函数的修改版本，以向基站提供高度精确的聚合数据。我们的实验结果表明，我们的方法降低了节点和群集头的通信成本，传输成本和能耗，并向基站提供了高度精炼的融合数据。采用基于阈值的机制来检测异常值，以提高数据聚合的准确性。在簇头级别，我们采用了欧几里德距离函数的修改版本，以向基站提供高度精确的聚合数据。我们的实验结果表明，我们的方法降低了节点和群集头的通信成本，传输成本和能耗，并向基站提供了高度精炼的融合数据。