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Big Data and Internet of Things
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Big Data and Internet of Things

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 35038

Special Issue Editors

Prof. Dr. Vijayakumar Varadarajan

E-Mail Website
Guest Editor
School of Computing Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Interests: grid computing; security; networking; cloud computing; big data
Special Issues, Collections and Topics in MDPI journals
Dr. Simon Fong

E-Mail Website
Guest Editor
Department of Computer and Information Science, University of Macau, Room 4023, E11, FST Building, Taipa, Macau 999078, China
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xudong Jiang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering Nanyang Technological University, Singapore
Interests: Pattern recognition; computer vision; machine learning; image processing; biometrics

Special Issue Information

Dear Colleagues,

The Special Issue on ‘Big Data and Internet of Things’ will contain selected and extended papers from the 4th International Conference on BDIOT' 2020, 22–24 August, Singapore http://www.bdiot.org/index.html. Internet of Things (IOT) is a platform and a phenomenon that allows everything to process information, communicate data, analyze context collaboratively, and in the service of individuals, organizations, and businesses. In the process of doing so, a large amount of data with different formats and content has to be processed efficiently, quickly, and intelligently through advanced algorithms, techniques, models, and tools. This new paradigm is enabled by the maturity of several different technologies, including the internet, wireless communication, cloud computing, sensors, big data analytics, and machine learning algorithms.

Big Data is another paradigm to describe the processing of data to help it 'make sense' to people using IoT. Big Data has five characteristics: volume, velocity, variety, veracity, and value. There are reports that Big Data skills can provide businesses and research communities with additional incentives, opportunities, funding, and innovation for their long-term strategies. New knowledge, tools, practices, and infrastructures will enable breakthrough discoveries and innovation in physical science, engineering, mobile services, medicine, business, education, earth science, security, and risk analysis.

Prof. Dr. Vijayakumar Varadarajan
Prof. Dr. Simon Fong
Prof. Dr. Xudong Jiang
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 submissions that pass pre-check are 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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 fundamentals – services computing, techniques, recommendations, and frameworks
  • Modeling, experiments, sharing technologies, and platforms
  • SQL/NoSQL databases, data processing techniques, visualization, and modern technologies
  • Analytics, intelligence, and knowledge engineering
  • Data center-enabled technologies
  • Sensor, wireless technologies, and APIs
  • Networking and social networks
  • Data management for large data
  • Security, privacy, and risk
  • Software frameworks (MapReduce, Spark, etc.) and simulations
  • Modern architecture Volume, velocity, variety, veracity, and value
  • Social science and implications for Big Data
  • Big Data as a service (BDaaS) including frameworks, empirical approaches, and data
  • Sensor networks, remote diagnosis, and development
  • Transportation management
  • Pattern recognition and behavioral investigations for vehicles, green systems, and smart city
  • 3D printing
  • Artificial intelligence
  • Biotechnology
  • Communication
  • Data Processing
  • Electronic technologies for in-vehicle
  • Internet of things
  • Mode-to-mode systems
  • Nanotechnology
  • Sensors
  • Transport safety and mobility
  • Vehicle-to-infrastructure
  • Vehicle-to-vehicle

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Published Papers (4 papers)

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21 pages, 4883 KiB  
Article
Earthquake Insurance in California, USA: What Does Community-Generated Big Data Reveal to Us?
by Fabrizio Terenzio Gizzi and Maria Rosaria Potenza
Big Data Cogn. Comput. 2022, 6(2), 60; https://doi.org/10.3390/bdcc6020060 - 20 May 2022
Cited by 5 | Viewed by 6664
Abstract
California has a high seismic hazard, as many historical and recent earthquakes remind us. To deal with potential future damaging earthquakes, a voluntary insurance system for residential properties is in force in the state. However, the insurance penetration rate is quite low. Bearing [...] Read more.
California has a high seismic hazard, as many historical and recent earthquakes remind us. To deal with potential future damaging earthquakes, a voluntary insurance system for residential properties is in force in the state. However, the insurance penetration rate is quite low. Bearing this in mind, the aim of this article is to ascertain whether Big Data can provide policymakers and stakeholders with useful information in view of future action plans on earthquake coverage. Therefore, we extracted and analyzed the online search interest in earthquake insurance over time (2004–2021) through Google Trends (GT), a website that explores the popularity of top search queries in Google Search across various regions and languages. We found that (1) the triggering of online searches stems primarily from the occurrence of earthquakes in California and neighboring areas as well as oversea regions, thus suggesting that the interest of users was guided by both direct and vicarious earthquake experiences. However, other natural hazards also come to people’s notice; (2) the length of the higher level of online attention spans from one day to one week, depending on the magnitude of the earthquakes, the place where they occur, the temporal proximity of other natural hazards, and so on; (3) users interested in earthquake insurance are also attentive to knowing the features of the policies, among which are first the price of coverage, and then their worth and practical benefits; (4) online interest in the time span analyzed fits fairly well with the real insurance policy underwritings recorded over the years. Based on the research outcomes, we can propose the establishment of an observatory to monitor the online behavior that is suitable for supporting well-timed and geographically targeted information and communication action plans. Full article
(This article belongs to the Special Issue Big Data and Internet of Things)
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13 pages, 4985 KiB  
Article
An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence
by Dweepna Garg, Priyanka Jain, Ketan Kotecha, Parth Goel and Vijayakumar Varadarajan
Big Data Cogn. Comput. 2022, 6(1), 9; https://doi.org/10.3390/bdcc6010009 - 11 Jan 2022
Cited by 8 | Viewed by 4224
Abstract
In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still [...] Read more.
In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS). Full article
(This article belongs to the Special Issue Big Data and Internet of Things)
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45 pages, 10033 KiB  
Article
AI Based Emotion Detection for Textual Big Data: Techniques and Contribution
by Sheetal Kusal, Shruti Patil, Ketan Kotecha, Rajanikanth Aluvalu and Vijayakumar Varadarajan
Big Data Cogn. Comput. 2021, 5(3), 43; https://doi.org/10.3390/bdcc5030043 - 9 Sep 2021
Cited by 46 | Viewed by 16349
Abstract
Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. [...] Read more.
Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. Such crucial insights cannot be completely obtained by doing AI-based big data sentiment analysis; hence, text-based emotion detection using AI in social media big data has become an upcoming area of Natural Language Processing research. It can be used in various fields such as understanding expressed emotions, human–computer interaction, data mining, online education, recommendation systems, and psychology. Even though the research work is ongoing in this domain, it still lacks a formal study that can give a qualitative (techniques used) and quantitative (contributions) literature overview. This study has considered 827 Scopus and 83 Web of Science research papers from the years 2005–2020 for the analysis. The qualitative review represents different emotion models, datasets, algorithms, and application domains of text-based emotion detection. The quantitative bibliometric review of contributions presents research details such as publications, volume, co-authorship networks, citation analysis, and demographic research distribution. In the end, challenges and probable solutions are showcased, which can provide future research directions in this area. Full article
(This article belongs to the Special Issue Big Data and Internet of Things)
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9 pages, 258 KiB  
Opinion
Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring
by Tatyana G. Krupnova, Olga V. Rakova, Kirill A. Bondarenko and Valeria D. Tretyakova
Big Data Cogn. Comput. 2022, 6(3), 75; https://doi.org/10.3390/bdcc6030075 - 5 Jul 2022
Cited by 11 | Viewed by 5544
Abstract
The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications [...] Read more.
The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice. Full article
(This article belongs to the Special Issue Big Data and Internet of Things)
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