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A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

机译：基于犹豫模糊熵的无线传感器网络数据融合新算法

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The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.
机译：无线传感器网络（WSN）主要由大量具有有限能量和资源的传感器节点组成。因此，无线传感器网络中的能耗是实践中最具挑战性的问题之一。另一方面，数据融合可以有效减少数据冗余，减少网络中的数据传输量和能耗，延长网络生命周期，提高带宽利用率，从而克服能耗和带宽消耗的瓶颈。提出了一种基于犹豫模糊熵（DFHFE）的数据融合新算法。新算法旨在减少从源头收集传感器节点上的重复数据，并努力利用冗余数据提供的信息来提高数据可靠性。利用犹豫模糊熵来融合来自汇聚节点处群集中传感器节点的原始数据，以获得更高质量的数据并针对感兴趣的事件做出本地决策。宿节点定期向基站发送本地决策，该本地决策汇总本地决策并做出最终判断，在此过程中，基站处理所有数据的负担被大大释放。根据我们的实验，提出的数据融合算法大大提高了整个网络的鲁棒性，准确性和实时性。仿真结果表明，新算法在能耗和实时性能方面都比最新技术有效。

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