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Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

机译：通过Voronoi图进行多维数据索引和范围查询处理

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In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time. (C) 2018 Elsevier B.V. All rights reserved.
机译：在诸如智能城市和工业4.0之类的典型物联网（IoT）部署中，从物理世界收集的传感数据量很大且范围广泛。处理来自各种物联网设备的大量实时数据具有挑战性。例如，在物联网环境中，无线传感器网络（WSN）通常用于监视和收集某些地理区域中的数据。在此类应用程序中，传统上在此类应用程序中使用具有位置约束的空间范围查询来促进数据索引，这允许基于SQL结构查询和管理数据。一个特殊的挑战是在多维数据索引方法中最小化通信成本和存储要求。在本文中，我们提出了一种节能高效的多维数据索引方案，该方案旨在回答范围查询。具体来说，我们提出了使用分级索引结构的数据索引方法，该方法使用了二进制空间分区（BSP），例如kd-tree，quad-tree，k-means聚类和基于Voronoi的方法，以提供更有效的路由且延迟更少。仿真结果表明，基于Voronoi图的算法可最大程度地减少平均能耗和查询响应时间。 （C）2018 Elsevier B.V.保留所有权利。

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