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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: closed (31 December 2019).

Special Issue Editors

Dr. Kaoru Ota
Website
Guest Editor
Department of Information and Electronic Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan
Interests: wireless networks; cloud computing; cyber–physical systems
Dr. Jun Wu
Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD. Minhang District, Shanghai 200240, China
Interests: software-defined networks; mobile networks; cyber security; fog computing
Special Issues and Collections in MDPI journals

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 2000 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 (9 papers)

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Open AccessArticle
Location-Aware Wi-Fi Authentication Scheme Using Smart Contract
Sensors 2020, 20(4), 1062; https://doi.org/10.3390/s20041062 - 15 Feb 2020
Abstract
Advanced wireless technology in Internet of Things (IoT) devices is increasing and facing various security threats. The authentication of IoT devices is the first line of defense for the wireless network. Especially in a Wi-Fi network, the existing authentication methods mainly use a [...] Read more.
Advanced wireless technology in Internet of Things (IoT) devices is increasing and facing various security threats. The authentication of IoT devices is the first line of defense for the wireless network. Especially in a Wi-Fi network, the existing authentication methods mainly use a password or digital certificate, these methods are inconvenient to manage due to certificate issuance or prone to be attacked because passwords are easily cracked. In this paper, we propose a location-aware authentication scheme using smart contracts to ensure that IoT devices can securely perform Wi-Fi network authentication. The scheme adopts the concept of secondary authentication and consists of two phases: the registration phase, which is mainly designed to complete the generation of the public and private keys, and to link the device information with its related device information; the authentication phase, which is mainly designed to determine whether the requesting device is within a legal location range. We use the smart contract to ensure the credibility and irreparability of the authentication process. Analysis of the attack model and the attacks at different stages proves that this certification scheme is assured, and the simulation results show that the overhead introduced by this scheme is acceptable, this scheme can provide greater security for the Wi-Fi authentication of IoT devices. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities
Sensors 2020, 20(4), 973; https://doi.org/10.3390/s20040973 - 12 Feb 2020
Abstract
With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices [...] Read more.
With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
Using Machine Learning for the Calibration of Airborne Particulate Sensors
Sensors 2020, 20(1), 99; https://doi.org/10.3390/s20010099 - 23 Dec 2019
Cited by 1
Abstract
Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments [...] Read more.
Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities
Sensors 2019, 19(15), 3443; https://doi.org/10.3390/s19153443 - 06 Aug 2019
Cited by 1
Abstract
People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, [...] Read more.
People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
A Node Density Control Learning Method for the Internet of Things
Sensors 2019, 19(15), 3428; https://doi.org/10.3390/s19153428 - 05 Aug 2019
Cited by 4
Abstract
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on [...] Read more.
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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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 - 09 May 2019
Cited by 1
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 - 01 Mar 2019
Cited by 2
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 - 15 Jan 2019
Cited by 61
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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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 - 20 May 2019
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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