Energy efficient handling of big data in embedded, wireless sensor networks

René Bergelt, M. Vodel, W. Hardt
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引用次数: 18

Abstract

The development of wireless sensor networks has reached a point where each individual node of a network may store and deliver a massive amount of (sensor-based) information at once or over time. In the future, massively connected, highly dynamic wireless sensor networks such as vehicle-2-vehicle communication scenarios may hold an even greater information potential. This is mostly due to the increase in node complexity. Consequently, data volumes will become a problem for traditional data aggregation strategies traffic-wise as well as with regard to energy efficiency. Therefore, in this paper we suggest to call such scenarios big data scenarios as they pose similar questions and problems as traditional big data scenarios. Although the latter focus mostly on business intelligence problems. We then propose an aggregation strategy tied to technological prerequisites which enables the efficient use of energy and the handling of large data volumes. Furthermore, we demonstrate the energy conservation potential based on experiments with actual sensor platforms.
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在嵌入式无线传感器网络中高效处理大数据
无线传感器网络的发展已经达到了这样一个地步:网络的每个单独节点可以一次或一段时间存储和传递大量(基于传感器的)信息。在未来,大规模连接、高度动态的无线传感器网络,如车对车通信场景,可能会拥有更大的信息潜力。这主要是由于节点复杂性的增加。因此,数据量将成为传统数据聚合策略在交通和能源效率方面的一个问题。因此,在本文中,我们建议将这些场景称为大数据场景,因为它们提出了与传统大数据场景相似的问题和问题。尽管后者主要关注商业智能问题。然后,我们提出了一种与技术先决条件相关联的聚合策略,该策略能够有效利用能源和处理大量数据。此外,我们还通过实际传感器平台的实验证明了该方法的节能潜力。
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