Edge Computing and Internet of Things Based Platform to Improve the Quality of Life of the Silver Economy on Leisure Cruise Ships

Marta Plaza-Hernández, Inés Sittón-Candanedo, R. Alonso, Luis C. Martínez-de Iturrate, Javier Prieto, K. Kravari, T. Kosmanis, George Katranas, Miguel P. Silva, J. Corchado
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引用次数: 3

Abstract

Today, the Internet of Things, Big Data and Machine Learning are a regular part of people's daily lives, especially for the elderly or the so-called Silver Economy. There are more and more applications in which collecting and storing information about users (and their context) to produce classifications and predictions allows the optimisation of the services offered, improving the life quality of the citizens. In this sense, leisure cruises represent environments where it is necessary to provide quality services for a reasonable price. This requires understandings the behaviour of users both locally (i.e. each cruise) and globally (i.e. all cruises). In this respect, IoT, Big Data and Machine Learning are presented as ideal technologies for prediction-making and the improvement of the quality of services. However, on ships at sea the transfer rate of the Internet connection is limited. In this sense, Edge Computing allows taking part in the application computing to the Edge of the network, closer to the IoT layer. Data can be filtered and pre-processed in the Edge before it is transmitted to the Cloud, reducing its volume and the associated costs of its computing and storage in the Cloud. Machine Learning algorithms can also be applied to recognise patterns and anomalies on the Edge itself, even if the connectivity with the Cloud is lost. In this paper, we propose the design of a platform which aims to improve the quality of life of the elderly on leisure cruises.
基于边缘计算和物联网的平台提升休闲邮轮银色经济的生活质量
如今,物联网、大数据和机器学习已经成为人们日常生活的一部分,尤其是老年人或所谓的“银色经济”。越来越多的应用程序收集和存储有关用户(及其上下文)的信息,以产生分类和预测,从而优化所提供的服务,提高公民的生活质量。从这个意义上说,休闲游轮代表了有必要以合理的价格提供优质服务的环境。这需要了解用户在本地(即每个邮轮)和全局(即所有邮轮)的行为。在这方面,物联网、大数据和机器学习被认为是预测和提高服务质量的理想技术。然而,在海上的船舶上,互联网连接的传输速率是有限的。从这个意义上说,边缘计算允许参与到网络边缘的应用计算,更接近物联网层。数据在传输到云端之前,可以在Edge中进行过滤和预处理,从而减少数据的体积以及在云中计算和存储的相关成本。机器学习算法也可以应用于识别边缘本身的模式和异常,即使与云的连接丢失。在本文中,我们提出了一个旨在提高老年人休闲邮轮生活质量的平台设计。
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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