Special Issue "Cyber Security, Smart Cities and Big Data Optimization in Energy Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editors

Prof. Dr. Ugo Fiore
Website
Guest Editor
Department of Management and Quantitative Studies, Parthenope University, Italy
Interests: Data Science; Optimization; Security
Dr. Pandian Vasant
Website
Guest Editor
Universiti Teknologi Petronas, Malaysia
Interests: Computational Intelligence, Hybrid Optimizaiton, Innovative Computing
Special Issues and Collections in MDPI journals
Prof. Dr. Gerhard-Wilhelm Weber
Website
Guest Editor
Poznan University of Technology, Poland
Interests: Bioinformatics; Artificial Intelligence; Energy; Modeling; Machine Learning Prediction
Dr. Joshua Thomas
Website
Guest Editor
UOW Malaysia; KDU Penang University College, Malaysia
Interests: intelligent systems techniques; deep learning algorithms; data science; visual analytics; scheduling and timetabling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

    The purpose of this Special Issue on Cyber Security, Smart Cities, and Big Data Optimization in Energy Applications is to deeply inquire, holistically reflect, and comprehensively expose the current and emerging cutting edge technology in important fields of investigation and facilities for original, innovative, and novel real-world applications of Optimization in the modern world related to the emerging areas of the Big Data, Data-based Cryptography and its emerging applications to individuals, groups, cities, countries, etc. During previous decades, the analytical toolbox and the methodological tools of computer science and applied mathematics, of informatics and statistics, of emerging analytics and information technologies has gained the attention of numerous researchers and practitioners worldwide, providing a strong impact also in natural sciences, engineering, economics and finance, the IT sector and, in recent years, the sector of Security, e.g., in data storage, data handling, data transfer, in traffic, transportation, supply chains, etc. Here, Optimization turns out to be a key technology from an integrated perspective, and it is closely connected with further areas of modern Operational Research such as Development, Societal Complexity and Ethics, in order to appropriately address aspects of living conditions, safety and freedom, in culture and environment with regard to that new and fast growing industry. Big-Data based and Security-oriented techniques are more and more wellknown and employed in research areas of engineering, science and technology. Current trends on application problems in engineering, science and technology are also expanded into the areas of economy, finance and, especially, into the hotel, tourism, travel and urban managements across the globe. This special issue focuses on best and high-quality selected papers in a rich variety of research topics. Well-known and new methodologies of optimization techniques will be used to resolve some of the very complicated and hard problems related to our research subjects.

    Contributing authors of this special issue will be experienced scientists and practitioners from all over the world; they use and further refine the deep model-based methods of mathematics and the less model-based, also so-called smart or intelligent algorithms with their roots in the engineering disciplines, in computer science, and informatics. The second ones are often called as heuristics and model-free; they are less rigorous mathematically, released from firm calculus, in order to integrate nature- and, especially, bio-inspired approaches to efficiently cope with hard problems. The rise of these algorithms from Artificial Intelligence happened in parallel to the powerful progress in mathematics that is model-based mainly. Today, labeled by names like Statistical Learning, Machine Learning, Metaheuristics and Matheuristics, and by Operational Research, model-free and model-based streamlines of traditions and approaches meet and interact in many centers of research, at important congresses, in leading projects and agendas in all over worldwide to overcome misunderstandings and misperceptions between these two academic avenues, but to gain from synergy effects, to jointly advance scientific progress and to provide a common service to the solution of urgent real-life challenges. Those vast problems exist in every area of the modern world and academics, in engineering, economics, social, life and human sciences, in development and the improvement of living conditions and future perspectives. Among the variety of these subjects and, in fact, including them all, we guest editors selected the fields of Cyber Security, Smart Cities, and Big Data Optimization. This special issue provides an innovative cutting edge research methodologies and applications in the research field of cyber security, smart cities, and big data optimization which currently popular across the globe.

Prof. Dr. Ugo Fiore
Dr. Pandian Vasant
Prof. Dr. Gerhard-Wilhelm Weber
Dr. Joshua Thomas
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. Applied Sciences 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

  • Smart City
  • Cyber Security
  • Mobile Ad Hoc Networks
  • Information Network
  • Graph Database
  • Quality of service
  • Internet of Vehicles
  • Internet of Things (IoTs)
  • Block Chain
  • Interoperability
  • Machine Learning
  • Meta-heuristics
  • Supply Chain
  • Path Relinking
  • High Speed Train
  • SMS Security
  • Environmental sustainability
  • Remote SensingATM Replenishment
  • Bit-Vector Encoding
  • Electroencephalogram
  • Social Network Analysis
  • Fuzzy Regulator
  • Mobile Cloud Computing
  • Web System Diagnosis
  • Deep Learning
  • Smart and Autonomous Vehicles
  • Energy Saving
  • Vehicular Network
  • Hybrid Renewable Energy

Published Papers (6 papers)

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Research

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Open AccessArticle
Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
Appl. Sci. 2020, 10(19), 7000; https://doi.org/10.3390/app10197000 - 08 Oct 2020
Abstract
The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle [...] Read more.
The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir. Full article
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Open AccessArticle
New Positive Solutions of Nonlinear Elliptic PDEs
Appl. Sci. 2020, 10(14), 4863; https://doi.org/10.3390/app10144863 - 15 Jul 2020
Abstract
We are concerned with positive solutions of two types of nonlinear elliptic boundary value problems (BVPs). We present conditions for existence, uniqueness and multiple positive solutions of a first type of elliptic BVPs. For a second type of elliptic BVPs, we obtain conditions [...] Read more.
We are concerned with positive solutions of two types of nonlinear elliptic boundary value problems (BVPs). We present conditions for existence, uniqueness and multiple positive solutions of a first type of elliptic BVPs. For a second type of elliptic BVPs, we obtain conditions for existence and uniqueness of positive global solutions. We employ mathematical tools including strictly upper (SU) and strictly lower (SL) solutions, iterative sequence method and Amann theorem. We present our research findings in new original theorems. Finally, we summarize and indicate areas of future study and possible applications of the research work. Full article
Open AccessArticle
The Inventory Routing Problem with Priorities and Fixed Heterogeneous Fleet
Appl. Sci. 2020, 10(10), 3502; https://doi.org/10.3390/app10103502 - 19 May 2020
Cited by 1
Abstract
This paper presents a new combinatorial optimization problem, the inventory routing problem with priorities, and a fixed heterogeneous fleet. In this problem, a particular set of customers has to be served before the rest of the customers using vehicles with different capacities. The [...] Read more.
This paper presents a new combinatorial optimization problem, the inventory routing problem with priorities, and a fixed heterogeneous fleet. In this problem, a particular set of customers has to be served before the rest of the customers using vehicles with different capacities. The problem is inspired by the current situation faced by a specialized gas distribution company in the northeast region of Mexico. The company produces and distributes three main products, although this paper focuses only on the oxygen distribution problem. The company delivers oxygen to industrial customers, as well as hospitals and other medical facilities. Due to Mexican government regulations, the company requires prioritizing deliveries to hospitals and medical facilities over its industrial customers. Therefore, the company is obliged to satisfy the customers demand considering inventory levels and priority constraints while minimizing the inventory and routing cost. An integer programming model is proposed to solve the problem. The model minimizes the total distribution cost while considering inventory level, priority constraints, and a fixed fleet of vehicles with different capacities. Finally, computational experiments were carried out using benchmark instances to validate the correctness of the proposed model and to analyze the effect of priorities on the total distribution cost. Finally, actual customers of the company were selected to show the effectiveness of the proposed model to solve real-world problems. Full article
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Open AccessArticle
Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation
Appl. Sci. 2020, 10(7), 2588; https://doi.org/10.3390/app10072588 - 09 Apr 2020
Abstract
Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and [...] Read more.
Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel–Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm. Full article
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Open AccessArticle
A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting
Appl. Sci. 2020, 10(6), 1949; https://doi.org/10.3390/app10061949 - 12 Mar 2020
Abstract
Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to [...] Read more.
Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on different effects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting. Full article
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Review

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Open AccessReview
Blockchain and IoT Convergence—A Systematic Survey on Technologies, Protocols and Security
Appl. Sci. 2020, 10(19), 6749; https://doi.org/10.3390/app10196749 - 26 Sep 2020
Cited by 1
Abstract
The Internet of Things (IoT) as a concept is fascinating and exciting, with an exponential growth just beginning. The IoT global market is expected to grow from 170 billion USD in 2017 to 560 billion USD by 2022. Though many experts have pegged [...] Read more.
The Internet of Things (IoT) as a concept is fascinating and exciting, with an exponential growth just beginning. The IoT global market is expected to grow from 170 billion USD in 2017 to 560 billion USD by 2022. Though many experts have pegged IoT as the next industrial revolution, two of the major challenging aspects of IoT since the early days are having a secure privacy-safe ecosystem encompassing all building blocks of IoT architecture and solve the scalability problem as the number of devices increases. In recent years, Distributed Ledgers have often been referred to as the solution for both privacy and security problems. One form of distributed ledger is the Blockchain system. The aim of this paper consists of reviewing the most recent Blockchain architectures, comparing the most interesting and popular consensus algorithms, and evaluating the convergence between Blockchain and IoT by illustrating some of the main interesting projects in this research field. Furthermore, the paper provides a vision of a disruptive research topic that the authors are investigating: the use of AI algorithms to be applied to IoT devices belonging to a Blockchain architecture. This obviously requires that the devices be provided with adequate computational capacity and that can efficiently optimize their energy consumption. Full article
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