无线传感器网络中时延最小化的高能效数据聚合

Huu Nghia Le, V. Zalyubovskiy, Hyunseung Choo
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引用次数: 20

摘要

数据聚合是近年来备受关注的无线传感器网络中的一个基本问题。在设计数据聚合方案时,延迟和能量效率是需要考虑的两个关键问题。在本文中,我们提出了一种分布式、节能的算法,以最小的延迟从所有传感器节点收集数据,称为延迟最小化节能数据聚合算法(DEDA)。DEDA算法通过构建一个延迟高效的网络结构来最小化数据聚合延迟。同时,还考虑了网络节点之间的距离,以节省传感器传输功率和网络能量。传感器之间的能量消耗也很好地平衡,以实现可接受的网络寿命。仿真结果表明,与其他方法相比,该方案可以显著降低数据聚合延迟,并获得合理的网络生存时间。
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Delay-minimized Energy-efficient Data Aggregation in Wireless Sensor Networks
Data aggregation is a fundamental problem in wireless sensor networks that has attracted great attention in recent years. To design a data aggregation scheme, delay and energy efficiencies are two crucial issues that require much consideration. In this paper, we propose a distributed, energy-efficient algorithm for collecting data from all sensor nodes with minimum latency called Delay-minimized Energy-efficient Data Aggregation algorithm (DEDA). The DEDA algorithm minimizes data aggregation latency by building a delay-efficient network structure. At the same time, it also considers the distances between network nodes for saving sensor transmission power and network energy. Energy consumption is also well-balanced between sensors to achieve an acceptable network lifetime. The simulation results show that the scheme could significantly decrease data aggregation delay and obtain a reasonable network lifetime compared with other approaches.
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