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Future Internet
Volume 14
Issue 9
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Future Internet, Volume 14, Issue 9 (September 2022) – 25 articles

Cover Story (view full-size image): Here, reinforcement learning (RL) is proposed in an IRS-assisted D2D underlay cellular network where D2D pairs opportunistically use a licensed cellular spectrum. The objective is to maximize network spectrum efficiency by jointly optimizing the resource reuse indicators, the transmit power of both cellular and D2D users, and the IRS reflection coefficients. This is carried out under strict SINR constraints to ensure the QoS for both cellular and D2D links is met. The RL approach proves efficient to solve this mixed integer non-convex nonlinear optimization problem. A scalable deep Q-learning solution with experience replay and an A2C-based deep deterministic policy gradient (DDPG) are proposed to learn the optimal policy. Simulation outcomes reveal significant sum rate enhancement. View this paper
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17 pages, 3404 KiB  
Article
Low Power Blockchained E-Vote Platform for University Environment
by Faten Chaabane, Jalel Ktari, Tarek Frikha and Habib Hamam
Future Internet 2022, 14(9), 269; https://doi.org/10.3390/fi14090269 - 19 Sep 2022
Cited by 13 | Viewed by 2522
Abstract
With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected [...] Read more.
With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources. Full article
(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)
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18 pages, 1269 KiB  
Article
Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications
by Majd Nafeh, Arash Bozorgchenani and Daniele Tarchi
Future Internet 2022, 14(9), 268; https://doi.org/10.3390/fi14090268 - 17 Sep 2022
Viewed by 1965
Abstract
Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to [...] Read more.
Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality. Full article
(This article belongs to the Special Issue Key Enabling Technologies for Beyond 5G Networks)
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21 pages, 2780 KiB  
Article
An Efficient Blockchain Transaction Retrieval System
by Hangwei Feng, Jinlin Wang and Yang Li
Future Internet 2022, 14(9), 267; https://doi.org/10.3390/fi14090267 - 15 Sep 2022
Cited by 4 | Viewed by 1825
Abstract
In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data [...] Read more.
In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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18 pages, 2808 KiB  
Article
Assistance System for the Teaching of Natural Numbers to Preschool Children with the Use of Artificial Intelligence Algorithms
by William Villegas-Ch., Angel Jaramillo-Alcázar and Aracely Mera-Navarrete
Future Internet 2022, 14(9), 266; https://doi.org/10.3390/fi14090266 - 15 Sep 2022
Cited by 2 | Viewed by 1436
Abstract
This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the [...] Read more.
This research was aimed at designing an image recognition system that can help increase children’s interest in learning natural numbers between 0 and 9. The research method used was qualitative descriptive, observing early childhood learning in a face-to-face education model, especially in the learning of numbers, with additional data from literature studies. For the development of the system, the cascade method was used, consisting of three stages: identification of the population, design of the artificial intelligence architecture, and implementation of the recognition system. The method of the system sought to replicate a mechanic that simulates a game, whereby the child trains the artificial intelligence algorithm such that it recognizes the numbers that the child draws on a blackboard. The system is expected to help increase the ability of children in their interest to learn numbers and identify the meaning of quantities to help improve teaching success with a fun and engaging teaching method for children. The implementation of learning in this system is expected to make it easier for children to learn to write, read, and conceive the quantities of numbers, in addition to exploring their potential, creativity, and interest in learning, with the use of technologies. Full article
(This article belongs to the Special Issue Artificial Neural Networks for Educational Data Mining in Higher Education)
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30 pages, 13547 KiB  
Review
Approaches and Challenges in Internet of Robotic Things
by Aqsa Sayeed, Chaman Verma, Neerendra Kumar, Neha Koul and Zoltán Illés
Future Internet 2022, 14(9), 265; https://doi.org/10.3390/fi14090265 - 14 Sep 2022
Cited by 7 | Viewed by 4628
Abstract
The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human [...] Read more.
The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human development by a very significant percentage. IoRT allows robots to transmit and receive data to and from other devices and users. In this paper, IoRT is reviewed in terms of the related techniques, architectures, and abilities. Consequently, the related research challenges are presented. IoRT architectures are vital in the design of robotic systems and robotic things. The existing 3–7-tier IoRT architectures are studied. Subsequently, a detailed IoRT architecture is proposed. Robotic technologies provide the means to increase the performance and capabilities of the user, product, or process. However, robotic technologies are vulnerable to attacks on data security. Trust-based and encryption-based mechanisms can be used for secure communication among robotic things. A security method is recommended to provide a secure and trustworthy data-sharing mechanism in IoRT. Significant security challenges are also discussed. Several known attacks on ad hoc networks are illustrated. Threat models ensure integrity confidentiality and availability of the data. In a network, trust models are used to boost a system’s security. Trust models and IoRT networks play a key role in obtaining a steady and nonvulnerable configuration in the network. In IoRT, remote server access results in remote software updates of robotic things. To study navigation strategies, navigation using fuzzy logic, probabilistic roadmap algorithms, laser scan matching algorithms, heuristic functions, bumper events, and vision-based navigation techniques are considered. Using the given research challenges, future researchers can get contemporary ideas of IoRT implementation in the real world. Full article
(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)
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27 pages, 6047 KiB  
Article
A VPN Performances Analysis of Constrained Hardware Open Source Infrastructure Deploy in IoT Environment
by Antonio Francesco Gentile, Davide Macrì, Floriano De Rango, Mauro Tropea and Emilio Greco
Future Internet 2022, 14(9), 264; https://doi.org/10.3390/fi14090264 - 13 Sep 2022
Cited by 7 | Viewed by 3771
Abstract
Virtual private network (VPN) represents an HW/SW infrastructure that implements private and confidential communication channels that usually travel through the Internet. VPN is currently one of the most reliable technologies to achieve this goal, also because being a consolidated technology, it is possible [...] Read more.
Virtual private network (VPN) represents an HW/SW infrastructure that implements private and confidential communication channels that usually travel through the Internet. VPN is currently one of the most reliable technologies to achieve this goal, also because being a consolidated technology, it is possible to apply appropriate patches to remedy any security holes. In this paper we analyze the performances of open source firmware OpenWrt 21.x compared with a server-side operating system (Debian 11 x64) and Mikrotik 7.x, also virtualized, and different types of clients (Windows 10/11, iOS 15, Android 11, OpenWrt 21.x, Debian 11 x64 and Mikrotik 7.x), observing the performance of the network according to the current implementation of the various protocols and algorithms of VPN tunnel examined on what are the most recent HW and SW for deployment in outdoor locations with poor network connectivity. Specifically, operating systems provide different performance metric values for various combinations of configuration variables. The first pursued goal is to find the algorithms to guarantee a data transmission/encryption ratio as efficiently as possible. The second goal is to research the algorithms capable of guaranteeing the widest spectrum of compatibility with the current infrastructures that support VPN technology, to obtain a connection system secure for geographically scattered IoT networks spread over difficult-to-manage areas such as suburban or rural environments. The third goal is to be able to use open firmware on constrained routers that provide compatibility with different VPN protocols. Full article
(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)
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15 pages, 5074 KiB  
Article
Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan
by Saeed A. Astaneh, Shahram Shah Heydari, Sara Taghavi Motlagh and Alireza Izaddoost
Future Internet 2022, 14(9), 263; https://doi.org/10.3390/fi14090263 - 13 Sep 2022
Cited by 2 | Viewed by 1227
Abstract
We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path [...] Read more.
We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path survivability probability may drop, i.e., its risk may rise. The main objective is to reroute at-risk end-to-end flows with the minimum number of flow operation so that a fast flow recovery is guaranteed. We provide various formulations for optimizing network risk versus operational costs and examine the trade-offs in flow recovery and the connections between operational cost, path risk, and path survival probability. We present our suboptimal dynamic flow restoration methods and evaluate their effectiveness against the Lagrangian relaxation approach. Our results show a significant improvement in operational cost against a shortest-path approach. Full article
(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
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18 pages, 1662 KiB  
Article
Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs
by Renaud Fabre, Otmane Azeroual, Patrice Bellot, Joachim Schöpfel and Daniel Egret
Future Internet 2022, 14(9), 262; https://doi.org/10.3390/fi14090262 - 07 Sep 2022
Cited by 2 | Viewed by 2164
Abstract
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified [...] Read more.
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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12 pages, 1770 KiB  
Article
A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
by Jie Yang, Jiajia Zhu and Ziyu Pan
Future Internet 2022, 14(9), 261; https://doi.org/10.3390/fi14090261 - 31 Aug 2022
Viewed by 1627
Abstract
Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power [...] Read more.
Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness. Full article
(This article belongs to the Special Issue 6G Wireless Communication Systems: Applications, Opportunities and Challenges II)
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15 pages, 339 KiB  
Article
Use of Data Augmentation Techniques in Detection of Antisocial Behavior Using Deep Learning Methods
by Viera Maslej-Krešňáková, Martin Sarnovský and Júlia Jacková
Future Internet 2022, 14(9), 260; https://doi.org/10.3390/fi14090260 - 31 Aug 2022
Cited by 8 | Viewed by 2018
Abstract
The work presented in this paper focuses on the use of data augmentation techniques applied in the domain of the detection of antisocial behavior. Data augmentation is a frequently used approach to overcome issues related to the lack of data or problems related [...] Read more.
The work presented in this paper focuses on the use of data augmentation techniques applied in the domain of the detection of antisocial behavior. Data augmentation is a frequently used approach to overcome issues related to the lack of data or problems related to imbalanced classes. Such techniques are used to generate artificial data samples used to improve the volume of the training set or to balance the target distribution. In the antisocial behavior detection domain, we frequently face both issues, the lack of quality labeled data as well as class imbalance. As the majority of the data in this domain is textual, we must consider augmentation methods suitable for NLP tasks. Easy data augmentation (EDA) represents a group of such methods utilizing simple text transformations to create the new, artificial samples. Our main motivation is to explore EDA techniques’ usability on the selected tasks from the antisocial behavior detection domain. We focus on the class imbalance problem and apply EDA techniques to two problems: fake news and toxic comments classification. In both cases, we train the convolutional neural networks classifier and compare its performance on the original and EDA-extended datasets. EDA techniques prove to be very task-dependent, with certain limitations resulting from the data they are applied on. The model’s performance on the extended toxic comments dataset did improve only marginally, gaining only 0.01 improvement in the F1 metric when applying only a subset of EDA methods. EDA techniques in this case were not suitable enough to handle texts written in more informal language. On the other hand, on the fake news dataset, the performance was improved more significantly, boosting the F1 score by 0.1. Improvement was most significant in the prediction of the minor class, where F1 improved from 0.67 to 0.86. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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31 pages, 4149 KiB  
Systematic Review
Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis
by Stephen Afrifa, Tao Zhang, Peter Appiahene and Vijayakumar Varadarajan
Future Internet 2022, 14(9), 259; https://doi.org/10.3390/fi14090259 - 30 Aug 2022
Cited by 27 | Viewed by 3683
Abstract
With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help [...] Read more.
With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes. Full article
(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)
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17 pages, 2913 KiB  
Article
Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
by Hongtao Zhu, Huahu Xu, Xiaojin Ma and Minjie Bian
Future Internet 2022, 14(9), 258; https://doi.org/10.3390/fi14090258 - 30 Aug 2022
Cited by 2 | Viewed by 1976
Abstract
Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In [...] Read more.
Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise, due to expression-independent intra-class differences. In this paper, we propose a Dual Path Stacked Attention Network (DPSAN) to better cope with the above challenges. Firstly, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences. Secondly, by providing the global image and segmented local image regions as training data for the integrated dual path model, the overfitting problem of the deep network due to a lack of data can be effectively mitigated. Finally, this paper also designs a stacked attention module to weight the fused feature maps according to the importance of each part for expression recognition. For the cropping scheme, this paper chooses to adopt a cropping method based on the fixed four regions of the face image, to segment out the key image regions and to ignore the irrelevant regions, so as to improve the efficiency of the algorithm computation. The experimental results on the public datasets, CK+ and FERPLUS, demonstrate the effectiveness of DPSAN, and its accuracy reaches the level of current state-of-the-art methods on both CK+ and FERPLUS, with 93.2% and 87.63% accuracy on the CK+ dataset and FERPLUS dataset, respectively. Full article
(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)
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23 pages, 682 KiB  
Article
A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance
by Shiva Raj Pokhrel and Carey Williamson
Future Internet 2022, 14(9), 257; https://doi.org/10.3390/fi14090257 - 29 Aug 2022
Viewed by 1453
Abstract
There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. [...] Read more.
There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. We accomplish this by defining a new utility function for MPTCP by employing Tullock’s rent-seeking paradigm from game theory. We investigate the convergence of no-regret learning in the underlying network games with continuous actions. Based on our understanding of the design space, we propose an original MPTCP algorithm that generalizes existing algorithms and strikes a good balance among the important properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally. Full article
(This article belongs to the Special Issue 5G Wireless Communication Networks)
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18 pages, 793 KiB  
Article
Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach
by Ajmery Sultana and Xavier Fernando
Future Internet 2022, 14(9), 256; https://doi.org/10.3390/fi14090256 - 29 Aug 2022
Cited by 15 | Viewed by 2348
Abstract
Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, [...] Read more.
Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes. Full article
(This article belongs to the Special Issue AI, Machine Learning and Data Analytics for Wireless Communications)
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15 pages, 4106 KiB  
Article
Smart Classroom Teaching Strategy to Enhance Higher Order Thinking Skills (HOTS)—An Agile Approach for Education 4.0
by Sitalakshmi Venkatraman, Fahri Benli, Ye Wei and Fiona Wahr
Future Internet 2022, 14(9), 255; https://doi.org/10.3390/fi14090255 - 28 Aug 2022
Cited by 7 | Viewed by 3416
Abstract
The development of Industry 4.0 revolutionising the concept of automation and digitisation in an organisation poses a huge challenge in employee knowledge and skills to cope with the huge leap from Industry 3.0. The high-level digitisation of an organisation requires the workforce to [...] Read more.
The development of Industry 4.0 revolutionising the concept of automation and digitisation in an organisation poses a huge challenge in employee knowledge and skills to cope with the huge leap from Industry 3.0. The high-level digitisation of an organisation requires the workforce to possess higher order thinking skills (HOTS) for the changing job roles matching the rapid technological advancements. The Education 4.0 framework is aimed at supporting the Industry 4.0 skills requirement not only in digital technologies but more towards soft skill development such as collaboration and lifelong learning. However, the education sector is also facing challenges in its transition from Education 3.0 to Education 4.0. The main purpose of the paper is to propose an Agile approach for developing smart classroom teaching strategies that foster employee adaptability with the new learning paradigm of upskilling in line with Industry 4.0. By adopting an exploratory research methodology, the pilot study investigates the implementation of the proposed Agile approach in a higher education setting for graduates to achieve HOTS using smart classroom teaching strategies. This study uses learning theories such as experiential learning in smart classroom environments to enhance students’ HOTS individually as well as collaboratively in an Agile iterative manner. This is the first empirical study carried out for graduates specialising in the Business Analytics skillset required for Industry 4.0. The findings of the pilot study show promising results that pave the way for further exploration and pedagogical insights in this research direction. Full article
(This article belongs to the Section Smart System Infrastructure and Applications)
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16 pages, 16085 KiB  
Article
Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
by Urmila Pilania, Rohit Tanwar, Mazdak Zamani and Azizah Abdul Manaf
Future Internet 2022, 14(9), 254; https://doi.org/10.3390/fi14090254 - 25 Aug 2022
Cited by 7 | Viewed by 1580
Abstract
In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and [...] Read more.
In today’s world of computers everyone is communicating their personal information through the web. So, the security of personal information is the main concern from the research point of view. Steganography can be used for the security purpose of personal information. Storing and forwarding of embedded personal information specifically in public places is gaining more attention day by day. In this research work, the Integer Wavelet Transform technique along with JPEG (Joint Photograph Expert Group) compression is proposed to overcome some of the issues associated with steganography techniques. Video cover files and JPEG compression improve concealing capacity because of their intrinsic properties. Integer Wavelet Transform is used to improve the imperceptibility and robustness of the proposed technique. The Imperceptibility of the proposed work is analyzed through evaluation parameters such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structure Similarity Metric), and CC (Correlation Coefficient). Robustness is validated through some image processing attacks. Complexity is calculated in terms of concealing and retrieval time along with the amount of secret information hidden. Full article
(This article belongs to the Special Issue Distributed Systems and Artificial Intelligence)
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17 pages, 5766 KiB  
Article
Translating Speech to Indian Sign Language Using Natural Language Processing
by Purushottam Sharma, Devesh Tulsian, Chaman Verma, Pratibha Sharma and Nancy Nancy
Future Internet 2022, 14(9), 253; https://doi.org/10.3390/fi14090253 - 25 Aug 2022
Cited by 8 | Viewed by 4234
Abstract
Language plays a vital role in the communication of ideas, thoughts, and information to others. Hearing-impaired people also understand our thoughts using a language known as sign language. Every country has a different sign language which is based on their native language. In [...] Read more.
Language plays a vital role in the communication of ideas, thoughts, and information to others. Hearing-impaired people also understand our thoughts using a language known as sign language. Every country has a different sign language which is based on their native language. In our research paper, our major focus is on Indian Sign Language, which is mostly used by hearing- and speaking-impaired communities in India. While communicating our thoughts and views with others, one of the most essential factors is listening. What if the other party is not able to hear or grasp what you are talking about? This situation is faced by nearly every hearing-impaired person in our society. This led to the idea of introducing an audio to Indian Sign Language translation system which can erase this gap in communication between hearing-impaired people and society. The system accepts audio and text as input and matches it with the videos present in the database created by the authors. If matched, it shows corresponding sign movements based on the grammar rules of Indian Sign Language as output; if not, it then goes through the processes of tokenization and lemmatization. The heart of the system is natural language processing which equips the system with tokenization, parsing, lemmatization, and part-of-speech tagging. Full article
(This article belongs to the Special Issue Trends of Data Science and Knowledge Discovery)
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23 pages, 2280 KiB  
Article
Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach
by Mst. Shapna Akter, Hossain Shahriar, Reaz Chowdhury and M. R. C. Mahdy
Future Internet 2022, 14(9), 252; https://doi.org/10.3390/fi14090252 - 25 Aug 2022
Cited by 9 | Viewed by 1922
Abstract
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this [...] Read more.
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models. Full article
(This article belongs to the Special Issue Machine Learning for Software Engineering)
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19 pages, 20557 KiB  
Article
Leveraging Explainable AI to Support Cryptocurrency Investors
by Jacopo Fior, Luca Cagliero and Paolo Garza
Future Internet 2022, 14(9), 251; https://doi.org/10.3390/fi14090251 - 24 Aug 2022
Cited by 2 | Viewed by 2529
Abstract
In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack [...] Read more.
In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool. Full article
(This article belongs to the Special Issue Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management)
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20 pages, 16086 KiB  
Article
Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
by Rajasekhar Chaganti, Vijayakumar Varadarajan, Venkata Subbarao Gorantla, Thippa Reddy Gadekallu and Vinayakumar Ravi
Future Internet 2022, 14(9), 250; https://doi.org/10.3390/fi14090250 - 24 Aug 2022
Cited by 47 | Viewed by 4355
Abstract
The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision [...] Read more.
The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in the Artificial Intelligence Age)
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18 pages, 1840 KiB  
Article
Determining the Role of Social Identity Attributes to the Protection of Users’ Privacy in Social Media
by Katerina Vgena, Angeliki Kitsiou, Christos Kalloniatis and Stefanos Gritzalis
Future Internet 2022, 14(9), 249; https://doi.org/10.3390/fi14090249 - 24 Aug 2022
Cited by 5 | Viewed by 2672
Abstract
Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on users’ self-representation on SM, we examined the impact [...] Read more.
Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on users’ self-representation on SM, we examined the impact of self-determination and self-disclosure on users’ privacy, and we identified the social attributes (SA) that cause privacy implications. This paper specifies 18 SA that users employ to achieve their optimal level of representation while summarizing possible ways that these attributes provoke users’ identification. In particular, our research has shown that SM users represent their personas by unveiling SA to construct popular, representative, and conversational profiles. As disclosing SA increases privacy implications, we intend to help users build profiles that respect their privacy. Examining users’ SA deepens our understanding of disclosing personal information on SM while leading to a better quantification of identity attributes; furthermore, users’ top five most revealing attributes were summarized. Considering that SSE addresses users’ privacy implications from an early stage of systems designing, our research, identifying the SA, will be helpful in addressing privacy from a socio-technical aspect, aiming at bridging the socio-technical gap by drawing designers’ attention to users’ social aspects. Full article
(This article belongs to the Section Internet of Things)
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14 pages, 1951 KiB  
Article
Design Technology and AI-Based Decision Making Model for Digital Twin Engineering
by Ekaterina V. Orlova
Future Internet 2022, 14(9), 248; https://doi.org/10.3390/fi14090248 - 24 Aug 2022
Cited by 6 | Viewed by 2100
Abstract
This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, [...] Read more.
This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, operational research and artificial intelligence. The study proposes a comprehensive technology (methodological approach) for digital twin design in order to accelerate its engineering. This technology consists of design steps, methods and models, and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty and that is able to reconfigure in response to internal faults or environment changes and perform preventive maintenance. In the technology structure, we develop a simulation model using situational “what-if” analysis and based on fuzzy logic methods. We apply this technology to develop the digital twin prototype for a device at the creation life cycle stage in order to reduce the consequences of unpredicted and undesirable states. We study possible unforeseen problems and device faults during its further operation. The model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. The practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and to support adequate decisions to eliminate their dysfunctions. Full article
(This article belongs to the Special Issue Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management)
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16 pages, 4967 KiB  
Article
A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract
by Mohammad S. Islam, Shahid Husain, Jawed Mustafa and Yuantong Gu
Future Internet 2022, 14(9), 247; https://doi.org/10.3390/fi14090247 - 24 Aug 2022
Cited by 1 | Viewed by 1599
Abstract
The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, [...] Read more.
The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field. Full article
(This article belongs to the Special Issue Deep Learning Techniques Addressing Data Scarcity)
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22 pages, 1464 KiB  
Review
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
by Tanweer Alam and Ruchi Gupta
Future Internet 2022, 14(9), 246; https://doi.org/10.3390/fi14090246 - 23 Aug 2022
Cited by 23 | Viewed by 5163
Abstract
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing [...] Read more.
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results. Full article
(This article belongs to the Section Internet of Things)
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11 pages, 754 KiB  
Review
Artificial Intelligence in Adaptive and Intelligent Educational System: A Review
by Jingwen Dong, Siti Nurulain Mohd Rum, Khairul Azhar Kasmiran, Teh Noranis Mohd Aris and Raihani Mohamed
Future Internet 2022, 14(9), 245; https://doi.org/10.3390/fi14090245 - 23 Aug 2022
Cited by 1 | Viewed by 2187
Abstract
There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to [...] Read more.
There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to a lack of teacher–student interaction, yet online learning has some advantages that are unavailable in traditional classrooms. The architecture of online courses for students is integrated into a system called the Adaptive and Intelligent Education System (AIES). In AIESs, reinforcement learning is often used in conjunction with the development of teaching strategies, and this reinforcement-learning-based system is known as RLATES. As a prerequisite to conducting research in this field, this paper undertakes the consolidation and analysis of existing research, design approaches, and model categories for adaptive and intelligent educational systems, with the hope of serving as a reference for scholars in the same field to help them gain access to the relevant information quickly and easily. Full article
(This article belongs to the Section Big Data and Augmented Intelligence)
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