智能城市的通信、传感、计算和能源收集

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Smart Cities Pub Date : 2022-09-12 DOI:10.1049/smc2.12041
Yusha Liu, Kun Yang
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引用次数: 3

摘要

智慧城市基于城市中部署的不同设备实时获取的数据,提供多样化的服务。这些设备主要由电池供电,并且被广泛放置。因此,为这些设备提供持续的能源并确保其高效的传感和通信对于智能城市的广泛部署至关重要。为了实现频繁和有效的数据交换,迫切需要先进的信息和通信技术基础设施。未来智慧城市的理想网络应该能够感知物理环境,并智能地映射数字世界。因此,在本文中,我们提出了在智慧城市背景下如何将通信与传感、计算和/或能量收集相结合的设计指南,旨在为发展集成通信、传感、计算和能量收集(ICSCE)提供研究见解,以促进智慧城市ICT基础设施的发展。为了将智慧城市的这四大支柱结合在一起,并利用不断增长的人工智能(AI)技术,作者通过利用数字孪生网络提出了一个有前途的人工智能支持的ICSCE架构。所提出的体系结构在虚拟空间中对物理深度神经网络辅助的ICSCE系统进行建模,在虚拟空间中,通过使用从环境和物理设备收集的实时数据进行离线训练。
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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Communication, sensing, computing and energy harvesting in smart cities

A smart city provides diverse services based on real-time data obtained from different devices deployed in urban areas. These devices are largely battery-powered and widely placed. Therefore, providing continuous energy to these devices and ensuring their efficient sensing and communications are critical for the wide deployment of smart cities. To achieve frequent and effective data exchange, advanced enabling information and communication technology (ICT) infrastructure is in urgent demand. An ideal network in future smart cities should be capable of sensing the physical environment and intelligently mapping the digital world. Therefore, in this paper, we propose design guidelines on how to integrate communications with sensing, computing and/or energy harvesting in the context of smart cities, aiming to offer research insights on developing integrated communications, sensing, computing and energy harvesting (ICSCE) for promoting the development ICT infrastructure in smart cities. To put these four pillars of smart cities together and to take advantage of ever-increasing artificial intelligence (AI) technologies, the authors propose a promising AI-enabled ICSCE architecture by leveraging the digital twin network. The proposed architecture models the physical deep neural network-aided ICSCE system in a virtual space, where offline training is performed by using the collected real-time data from the environment and physical devices.

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来源期刊
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
期刊最新文献
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