From IoT big data to IoT big services

Amirhosein Taherkordi, F. Eliassen, G. Horn
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引用次数: 29

Abstract

The large-scale deployments of Internet of Things (IoT) systems have introduced several new challenges in terms of processing their data. The massive amount of IoT-generated data requires design solutions to speed up data processing, scale up with the data volume and improve data adaptability and extensibility. Beyond existing techniques for IoT data collection, filtering, and analytics, innovative service computing technologies are required for provisioning data-centric and scalable IoT services. This paper presents a service-oriented design model and framework for realizing scalable and efficient acquisition, processing and integration of data-centric IoT services. In this approach, data-centric IoT services are organized in a service integrating tree structure, adhering to the architecture of many large-scale IoT systems, including recent fog-based IoT computing models. A service node in the tree is called a Big Service and acts as an integrator, collecting data from lower level Big Services, processing them, and delivering the result to higher level IoT Big Services. The service tree thereby encapsulates required data processing functions in a hierarchical manner in order to achieve scalable and real-time data collection and processing. We have implemented the IoT Big Services framework leveraging a popular cloud-based service and data platform called Firebase, and evaluated its performance in terms of real-time requirements.
从物联网大数据到物联网大服务
物联网(IoT)系统的大规模部署在数据处理方面带来了一些新的挑战。物联网产生的海量数据需要设计解决方案来加快数据处理速度,随着数据量的增加而扩大规模,提高数据的适应性和可扩展性。除了现有的物联网数据收集、过滤和分析技术之外,还需要创新的服务计算技术来提供以数据为中心和可扩展的物联网服务。本文提出了一种面向服务的设计模型和框架,用于实现以数据为中心的物联网服务的可扩展和高效的获取、处理和集成。在这种方法中,以数据为中心的物联网服务以服务集成树结构组织,遵循许多大型物联网系统的架构,包括最近基于雾的物联网计算模型。树中的服务节点称为大服务,充当集成商,从较低级别的大服务收集数据,对其进行处理,并将结果交付给更高级别的物联网大服务。因此,服务树以分层的方式封装了所需的数据处理功能,以实现可伸缩和实时的数据收集和处理。我们利用一个流行的基于云的服务和数据平台Firebase实现了物联网大服务框架,并根据实时需求评估了其性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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