Guest editorial: Smart computing for smart cities

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Smart Cities Pub Date : 2022-02-18 DOI:10.1049/smc2.12024
Anish Jindal, Angelos K. Marnerides, Petros Spachos, Amit Dvir
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Please read the full papers if you are interested in more details.</p><p>Nassar and Simon, in their paper ‘Wavelet-attention-based traffic prediction for smart cities’, proposed a wavelet-attention based traffic prediction system to predict the temporal correlations between the traffic flow and the weather factors, so as to reduce the traffic congestion problem in smart cities. Often the traffic is affected by external factors, such as weather, which makes traffic prediction more complicated. 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Abstract

Smart computing has a big role to play in the development of the smart cities. The advanced networking paradigms such as programmable and virtual networks, growth in communication technologies like 5G, and use of advanced computing infrastructure such as fog/edge/cloud computing can all contribute in the applications related to smart cities. With the penetration of Internet of things (IoT) devices in smart city applications, smart computing has become all the more relevant in terms of managing and processing the data. The applications in smart cities can be computing-intensive, network-intensive, disk-intensive, data-intensive etc., which require different solution sets in order to effectively solve the issues pertaining to these applications. The computing and communication aspects in smart computing are of great relevance in the wider domain of smart cities, which consists of smart metering, smart homes, smart building, smart industries, connected vehicles, and tackles various problems ranging from data reporting to providing smart services.

Hence, the aim of this special issue is to bring together researchers working in the broad area of smart cities using smart computing. A number of authors from the 2nd International Workshop on Smart Computing for Smart Cities were invited to expand and submit their papers to this special issue alongside papers received from our open call for papers. The summary of each paper is provided below. Please read the full papers if you are interested in more details.

Nassar and Simon, in their paper ‘Wavelet-attention-based traffic prediction for smart cities’, proposed a wavelet-attention based traffic prediction system to predict the temporal correlations between the traffic flow and the weather factors, so as to reduce the traffic congestion problem in smart cities. Often the traffic is affected by external factors, such as weather, which makes traffic prediction more complicated. Their proposed paper modelled the interactions between traffic and these external factors such as temperature, visibility, wind speed, rain, and humidity, based on which the important features are then calculated and compared to each other to get the attention weights that describe the importance of each external factor on traffic.

Fitwi et al., in their paper ‘Lightweight frame scrambling mechanisms for end-to-end privacy in edge smart surveillance’, investigated a very interesting and timely problem of smart surveillance and provided a lightweight frame scrambling mechanism for ensuring end-to-end privacy in such systems. The authors rightly pointed out that existing cryptographic schemes are computationally expensive if data is to be processed at the resource constrained network edge devices. Keeping this in mind, they designed a lightweight sine-cosine chaotic map solution for enciphering frames at edge cameras and ran dynamic chaotic image enciphering scheme in real time at the edge along with a lightweight region of interest masking scheme to ensure the privacy of sensitive attributes such as face in the surveillance feeds.

Dudeja et al., in their paper ‘An optimal content indexing approach for named data networking in software-defined IoT system’, touched upon another important aspect of smart cities, that is, Internet of things (IoT). To fulfil the demand by allowing the content to be directly addressable and routable, the authors leveraged a content-centric networking-based named data networking technique in the IoT ecosystem. The authors proposed a novel content storage/indexing approach for the selection of appropriate nodes and red-black tree-based content storage and retrieval from the available caching memory to work on the software-defined controller deployed over the named data network.

The papers in this special issue shed light on different aspects of smart computing in smart cities. We would like to thank the authors for submitting their work to this special issue, reviewers for timely review and suggesting improvement comments, and the editorial team of IET Smart Cities for smooth handling of the whole process.

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嘉宾评论:智能城市的智能计算
智能计算在智慧城市的发展中扮演着重要的角色。先进的网络范例,如可编程网络和虚拟网络,通信技术(如5G)的发展,以及先进计算基础设施(如雾/边缘/云计算)的使用,都可以为与智慧城市相关的应用做出贡献。随着物联网(IoT)设备在智慧城市应用中的渗透,智能计算在管理和处理数据方面变得更加重要。智慧城市中的应用可能是计算密集型、网络密集型、磁盘密集型、数据密集型等,需要不同的解决方案集才能有效地解决与这些应用相关的问题。智能计算中的计算和通信方面与智能城市的更广泛领域具有很大的相关性,智能城市包括智能电表,智能家居,智能建筑,智能工业,互联汽车,并解决从数据报告到提供智能服务的各种问题。因此,本期特刊的目的是将使用智能计算的智能城市广泛领域的研究人员聚集在一起。来自第二届智慧城市智能计算国际研讨会的一些作者被邀请扩展并提交他们的论文到这一期特刊,以及从我们的公开论文征集中收到的论文。每篇论文的摘要如下。如果你想了解更多细节,请阅读全文。Nassar和Simon在他们的论文“基于小波注意力的智能城市交通预测”中提出了一种基于小波注意力的交通预测系统,用于预测交通流量与天气因素之间的时间相关性,从而减少智能城市的交通拥堵问题。交通往往受到外界因素的影响,如天气,这使得交通预测更加复杂。他们提出的论文模拟了交通与这些外部因素(如温度、能见度、风速、降雨和湿度)之间的相互作用,然后在此基础上计算并相互比较重要的特征,以获得描述每个外部因素对交通重要性的注意权重。Fitwi等人在他们的论文“边缘智能监控中端到端隐私的轻量级帧扰机制”中,研究了一个非常有趣和及时的智能监控问题,并提供了一种轻量级帧扰机制来确保此类系统中的端到端隐私。作者正确地指出,如果要在资源受限的网络边缘设备上处理数据,现有的加密方案在计算上是昂贵的。考虑到这一点,他们设计了一种轻量级的正弦余弦混沌映射解决方案,用于边缘摄像机的帧加密,并在边缘实时运行动态混沌图像加密方案,同时使用轻量级的感兴趣区域掩蔽方案,以确保监控馈带中的敏感属性(如人脸)的隐私性。Dudeja等人在他们的论文《软件定义物联网系统中命名数据网络的最佳内容索引方法》中谈到了智慧城市的另一个重要方面,即物联网(IoT)。为了满足允许内容可直接寻址和路由的需求,作者在物联网生态系统中利用了一种以内容为中心的基于网络的命名数据网络技术。作者提出了一种新的内容存储/索引方法,用于选择适当的节点和基于红黑树的内容存储和从可用的缓存内存中检索,以在部署在命名数据网络上的软件定义控制器上工作。本期特刊的论文阐述了智慧城市中智能计算的不同方面。感谢作者为本期特刊投稿,感谢审稿人及时审阅并提出改进意见,感谢《IET智慧城市》编辑团队顺利处理整个过程。
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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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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