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Wavelet-Based Least Common Ancestor Algorithm for Aggregate Query Processing in Energy Aware Wireless Sensor Network
Wireless Personal Communications ( IF 2.2 ) Pub Date : 2021-01-03 , DOI: 10.1007/s11277-020-07938-3
Reeta Bhardwaj , Dinesh Kumar

Wireless sensor network (WSN) is developed as a network of sensors, which engage in sensing and transmitting the data to the sink node. The constraints, such as energy, memory, and bandwidth insist the researchers to develop an efficient method for data transmission in WSN. Accordingly, this paper introduces a data aggregation mechanism based on query processing, Wavelet-based Least Common Ancestor-Sliding window (WLCA-SW). The energy-loss and memory-crisis is well addressed using the proposed WLCA-SW through the successive steps of query processing, duplicate detection, data compression using the wavelet transformation, and data aggregation. The proposed WLCA-SWA is developed with the integration of the weighed sliding window and Least Common Ancestor (LCA), which enables the energy-aware aggregate query processing and de-duplication such that the duplicate records are detected potentially prior to the communication of the sensed data to the sink node. It is prominent that the weighed sliding window is the extension of the existing time-based sliding windows. The effectiveness of the proposed aggregate processing approach is evaluated based on the metrics, such as number of alive nodes, data reduction rate, data-loss percentage, and residual energy, which is found to be 33, 85%, 8.222%, and 0.0610 J at the end of 1000 rounds using 150 nodes for analysis. Moreover, the proposed method has the minimum aggregation error of 0.03, when the analysis is performed using 50 nodes.



中文翻译:

能量感知无线传感器网络中基于小波的最小公祖算法进行聚合查询

无线传感器网络(WSN)被开发为传感器网络,用于感应数据并将其传输到接收器节点。诸如能量,内存和带宽等约束条件迫使研究人员开发一种有效的WSN数据传输方法。因此,本文介绍了一种基于查询处理的数据聚合机制,即基于小波的最小公祖滑动窗(WLCA-SW)。通过提议的WLCA-SW,通过查询处理,重复检测,使用小波变换进行数据压缩和数据聚合的连续步骤,可以很好地解决能耗和内存危机。拟议的WLCA-SWA是将称重的滑动窗口和最小公祖(LCA)集成在一起而开发的,这使得能量感知聚合查询处理和重复数据删除成为可能,从而可以在将感测到的数据传输到接收器节点之前潜在地检测到重复记录。突出的是,加权滑动窗口是现有基于时间的滑动窗口的扩展。基于活动节点数,数据减少率,数据丢失百分比和剩余能量等指标评估了所提出的聚合处理方法的有效性,发现这些指标分别为33、85%,8.222%和0.0610 J在使用150个节点进行分析的1000回合末尾。此外,当使用50个节点执行分析时,所提出的方法的最小聚集误差为0.03。突出的是,加权滑动窗口是现有基于时间的滑动窗口的扩展。基于活动节点数,数据减少率,数据丢失百分比和剩余能量等指标评估了所提出的聚合处理方法的有效性,发现这些指标分别为33、85%,8.222%和0.0610 J在使用150个节点进行分析的1000回合末尾。此外,当使用50个节点执行分析时,所提出的方法的最小聚集误差为0.03。突出的是,加权滑动窗口是现有基于时间的滑动窗口的扩展。基于活动节点数,数据减少率,数据丢失百分比和剩余能量等指标评估了所提出的聚合处理方法的有效性,发现这些指标分别为33、85%,8.222%和0.0610 J在使用150个节点进行分析的1000回合末尾。此外,当使用50个节点执行分析时,所提出的方法的最小聚集误差为0.03。在1000回合结束时使用150个节点进行分析,得出0610J。此外,当使用50个节点执行分析时,所提出的方法的最小聚集误差为0.03。在1000回合结束时使用150个节点进行分析,得出0610J。此外,当使用50个节点执行分析时,所提出的方法的最小聚集误差为0.03。

更新日期:2021-01-03
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