Segmentation and Abstraction of an IoT Enabled Distributed Sensor Network

Yuanhang Shao, Suman Kumar, Takahiro Kawakami
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Abstract

We propose an area segmentation algorithm which is completely distributed, highly responsive, and exhibits a wide range of application scenarios. The proposed algorithm segments the area based on similarity of local sensor data and therefore, it requires a similarity measure parametrized with selected system indicators. In addition, algorithm creates an energy efficient data aggregation tree with a local highest energy node as a root. The resulting segmented sub-areas represents a level of spatial diversity and an abstraction of the sensor field which has a wide range of large scale distributed applications. Through simulation, the application and working of our scheme is demonstrated.
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支持物联网的分布式传感器网络的分割和抽象
我们提出了一种完全分布式、高响应、应用场景广泛的区域分割算法。该算法基于局部传感器数据的相似度对区域进行分割,因此需要一个与选定的系统指标参数化的相似度度量。此外,算法以局部能量最高的节点为根,创建了一棵节能的数据聚合树。由此产生的分段子区域代表了一定程度的空间多样性和对传感器场的抽象,具有广泛的大规模分布式应用。通过仿真验证了该方案的应用和工作原理。
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