Dynamic Data Aggregation Approach for Sensor-Based Big Data
Dynamic Data Aggregation Approach for Sensor-Based Big Data
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DOI：
10.14569/ijacsa.2018.090710
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发表时间：
2018
影响因子：
0.9
通讯作者：
M. Al-kahtani;Lutful Karim
中科院分区：
文献类型：
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作者：
M. Al-kahtani;Lutful Karim
传感器被用于成千上万的应用，如农业，健康监测，空气和水污染监测，交通监测和控制。由于这些应用程序每天收集ZB的数据，传感器在大数据中发挥着不可或缺的作用。然而，这些数据中的大多数是冗余的，无用的。因此，有效的数据聚合和处理对于减少基于传感器的大数据框架中的冗余和无用数据非常重要。目前对大数据分析的研究并不关注在大数据框架的多个层上聚合和过滤数据，特别是在数据收集节点（传感器）的较低级别上，这减少了上层的处理开销，即，大数据服务器因此，本文介绍了基于传感器的大数据框架的多层数据聚合技术。而这项工作更侧重于传感器网络的数据聚合。为了实现能源效率，它还表明，在较低层（传感器）的有效数据处理显着降低了网络的整体能耗和数据传输延迟。
Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.