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Special Issue "Big Data Driven IoT for Smart Cities"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Dr. Kaoru Ota

Department of Information and Electronic Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan
Website | E-Mail
Interests: wireless networks; cloud computing; cyber–physical systems
Guest Editor
Dr. Jun Wu

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD. Minhang District, Shanghai 200240, China
Website | E-Mail
Interests: software-defined networks; mobile networks; cyber security; fog computing

Special Issue Information

Dear Colleagues,

Recent years have witnessed billions of sensors, devices, and vehicles being connected to our cyberspace. Every day, all these smart things are generating massive data calculated in ZettaBytes. As our cities become smarter, we are also facing brand new opportunities and challenges. Multimedia data sensing and processing, together with resource allocation, quality of service (QoS) optimization, security and privacy, platforms, tools, etc., have been a major objective, and critical for big-data-driven IoT. Emerging technologies and paradigms including fog/edge computing, deep learning, network function virtualization, mobile crowdsensing, and 5G are in urgent need of playing roles on this new stage.

This Special Issue aims to report the state-of-the-art technologies in big-data-driven IoT for smart cities. Topics of interest include, but are not limited to, the following:

  • Multimedia big-data-driven IoT for smart cities
  • Resource allocation and data management for smart cities
  • QoS/QoE-aware optimization in big data driven IoT for smart cities
  • Cloud computing and big-data-driven IoT for smart cities
  • Software-defined networking and big-data-driven IoT for smart cities
  • Fog/edge computing and big-data-driven IoT for smart cities
  • Network functions virtualization in big-data-driven IoT for smart cities
  • Machine learning and big-data-driven IoT for smart cities
  • Mobile crowdsensing and big-data-driven IoT for smart cities
  • Security and privacy in big-data-driven IoT for smart cities
  • Green communications and networking in big-data-driven IoT for smart cities
  • 5G applications and big-data-driven IoT for smart cities
  • Novel algorithms, models, frameworks, platforms in big-data-driven IoT for smart cities

Dr. Kaoru Ota
Dr. Jun Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data
  • Internet of things
  • Smart cities
  • Multimedia
  • Cloud computing
  • Software-defined networking
  • Fog/edge computing
  • Network function virtualization
  • Machine learning
  • Crowdsensing

Published Papers (4 papers)

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Research

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Open AccessArticle
Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model
Sensors 2019, 19(9), 2156; https://doi.org/10.3390/s19092156
Received: 26 March 2019 / Revised: 27 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
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Abstract
Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using [...] Read more.
Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data
Sensors 2019, 19(5), 1059; https://doi.org/10.3390/s19051059
Received: 16 January 2019 / Revised: 24 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
Cited by 1 | PDF Full-text (295 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of things (IoT) has become a significant part of our daily life. Composed of millions of intelligent devices, IoT can interconnect people with the physical world. With the development of IoT technology, the amount of data generated by sensors or devices [...] Read more.
The Internet of things (IoT) has become a significant part of our daily life. Composed of millions of intelligent devices, IoT can interconnect people with the physical world. With the development of IoT technology, the amount of data generated by sensors or devices is increasing dramatically. IoT-based big data has become a very active research area. One of the key issues in IoT-based big data is ensuring the utility of data while preserving privacy. In this paper, we deal with the protection of big data privacy in the data storage phase and propose a searchable encryption scheme satisfying personalized privacy needs. Our proposed scheme works for all file types including text, audio, image, video, etc., and meets different privacy needs of different individuals at the expense of high storage cost. We also show that our proposed scheme satisfies index indistinguishability and trapdoor indistinguishability. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
A Decentralized Privacy-Preserving Healthcare Blockchain for IoT
Sensors 2019, 19(2), 326; https://doi.org/10.3390/s19020326
Received: 12 December 2018 / Revised: 2 January 2019 / Accepted: 10 January 2019 / Published: 15 January 2019
Cited by 3 | PDF Full-text (654 KB) | HTML Full-text | XML Full-text
Abstract
Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have [...] Read more.
Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology—remote patient monitoring—is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient’s life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Other

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Open AccessErratum
Erratum: Li, S.; Miao, L.; Xu, H.; Zhou, X. Searchable Encryption Scheme for Personalized Privacy in IoT-Based Big Data. Sensors 2019, 19, 1059
Sensors 2019, 19(10), 2327; https://doi.org/10.3390/s19102327
Received: 17 May 2019 / Accepted: 17 May 2019 / Published: 20 May 2019
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Abstract
The Sensors Editorial Office wishes to report the following erratum to this paper [...] Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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