Real-Time Information Derivation from Big Sensor Data via Edge Computing
Real-Time Information Derivation from Big Sensor Data via Edge Computing
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DOI：
10.3390/bdcc1010005
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
K. Kang;Liehuo Chen;Hyungdae Yi;Bin Wang;M. Sha
中科院分区：
文献类型：
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作者：
K. Kang;Liehuo Chen;Hyungdae Yi;Bin Wang;M. Sha
在数据密集型实时应用中，例如认知辅助和移动健康（mHealth），传感器数据量呈爆炸式增长。在这些应用中，希望从传感器数据流中实时提取增值信息，例如精神或身体健康状况，而不是向用户提供大量原始数据。然而，由于数据量和复杂的数据分析任务以及严格的时间限制，实现这一目标具有挑战性。大多数现有的大数据管理系统，例如Hadoop，并不能直接应用于实时传感器数据分析，因为它们与时间无关，并且专注于批量处理以前存储的数据，这些数据可能已经过时，并且会受到I/O开销的影响。此外，嵌入式传感器和物联网设备缺乏足够的资源来执行复杂的数据分析。为了解决这个问题，我们设计了一个新的实时大数据管理框架，通过扩展源自函数式编程的map-reduce模型，支持网络边缘的周期性内存实时传感器数据分析，同时根据数据重要性向边缘服务器提供自适应传感器数据传输。本文设计并实现了一个原型系统作为概念验证。在性能评估中，经验表明，重要的传感器数据以首选的方式传递，并及时进行分析。
In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion.