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Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 38751

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Special Issue Editor

Dr. Roberto Saia

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Guest Editor

Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy
Interests: data mining; machine learning; anomaly detection; recommender systems; security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern societies are currently experiencing an increasing adoption of interconnected wireless devices, a revolutionary technological paradigm named the Internet of Things (IoT). This represents an ever-growing scenario that offers enormous opportunities in multiple areas of our societies, especially if we combine the IoT with another emerging technology named blockchain, since it allows us to public certify any data transactions without the need to use central authorities or intermediaries. This Special Issue aims to bring together scientists from different areas, with the goal to both present their recent research findings and exchange ideas related to the exploitation of the opportunities of these technologies, also when their exploitation involves other powerful technologies, such as those based on Artificial Intelligence (AI).

This Special Issue welcomes research papers showing fundamental and applied research on the aforementioned research scenario and high-quality survey papers.

Potential topics include but are not limited to:

Internet of Things security;
Wireless data exchange models;
Wireless multimedia sensor networks;
Localization of people and things;
User clustering and profiling;
Artificial Intelligence;
Blockchain;
Machine learning;
Deep neural networks;
Big data.

Dr. Roberto Saia
Guest Editor

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. 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 2400 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

internet
Internet of Things
Artificial Intelligence
blockchain
machine learning
security

Published Papers (14 papers)

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Research

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23 pages, 9822 KiB

Open AccessArticle

Wire and Cable Quality Traceability System Based on Industrial Internet of Things and Blockchain

by Jintao Zhao, Wenlei Sun, Cheng Lu, Xuedong Zhang, Lixin Wang and Dajiang Wang

Appl. Sci. 2024, 14(2), 943; https://doi.org/10.3390/app14020943 - 22 Jan 2024

Viewed by 529

Abstract

Wire and cable are important industrial products involving the national economy and people’s livelihood, which are hailed as the “blood vessel” and “nerve” of the national economy, providing the basic guarantee for the normal operation of modern economy and society. The data traceability of their production and circulation process is a key factor in ensuring their quality and safety management. We aim to solve the problems of unsafe data transmission, weak quality control, and information islands in the process of wire and cable quality traceability in order to improve the production management efficiency of wire and cable manufacturing enterprises and to reduce the cost of consumer quality traceability of wire and cable products. We analyzed the technical characteristics and advantages of Industrial Internet of Things (IIoT) identity resolution and the blockchain. Key technologies are introduced, a traceability method that integrates the two is proposed, and a quality traceability framework based on the IIoT identity resolution system and blockchain technology is constructed. By analyzing the quality information composition of the wire and cable supply chain, a new quality traceability model based on the wire and cable supply chain is established. Finally, through the verification of the developed quality traceability system, the quality traceability function and quality information of each production link of wire and cable are successfully realized. This paper fills a gap in the field of cable product quality traceability using the combination of IIoT and blockchain technology. According to this model, it also has some potential for the traceability of other industrial products. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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25 pages, 7876 KiB

Open AccessArticle

Securing Construction Workers’ Data Security and Privacy with Blockchain Technology

by Alvina Ekua Ntefua Saah, Jurng-Jae Yee and Jae-Ho Choi

Appl. Sci. 2023, 13(24), 13339; https://doi.org/10.3390/app132413339 - 18 Dec 2023

Viewed by 1232

Abstract

The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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17 pages, 1073 KiB

Open AccessArticle

Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem

by Javier Carbo and Jose Manuel Molina

Appl. Sci. 2023, 13(15), 8681; https://doi.org/10.3390/app13158681 - 27 Jul 2023

Viewed by 663

Abstract

In this paper, we propose a trust model to be used in a hypothetical mixed environment where humans and unmanned vehicles cooperate. We address the inclusion of emotions inside a trust model in a coherent way to investigate the practical approaches to current psychological theories. The most innovative contribution of this work is the elucidation of how privacy issues play a role in the cooperation decisions of the emotional trust model. Both emotions and trust were cognitively modeled and managed with the beliefs, desires and intentions (BDI) paradigm in autonomous agents implemented in GAML (the programming language of the GAMA agent platform), that communicate using the IEEE FIPA standard. The trusting behavior of these emotional agents was tested in a cooperative logistics problem wherein agents have to move objects to destinations and some of the objects and places are associated with privacy issues. Simulations of the logistic problem show how emotions and trust contribute to improving the performance of agents in terms of both time savings and privacy protection. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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19 pages, 674 KiB

Open AccessArticle

Online Voting Scheme Using IBM Cloud-Based Hyperledger Fabric with Privacy-Preservation

by Ross Clarke, Luke McGuire, Mohamed Baza, Amar Rasheed and Maazen Alsabaan

Appl. Sci. 2023, 13(13), 7905; https://doi.org/10.3390/app13137905 - 05 Jul 2023

Cited by 2 | Viewed by 1490

Abstract

The current traditional paper ballot voting schemes suffer from several limitations such as processing delays due to counting paper ballots, lack of transparency, and manipulation of the ballots. To solve these limitations, an electronic voting (e-voting) scheme has received massive interest from both governments and academia. In e-voting, individuals can cast their vote online using their smartphones without the need to wait in long lines. Additionally, handicapped voters who face limited wheelchair access in many polling centers could now participate in elections hassle-free. The existing e-voting schemes suffer from several limitations as they are either centralized, based on public blockchains, or utilize local private blockchains. This results in privacy issues (using public blockchains) or large financial costs (using local/private blockchains) due to the amount of computing power and technical knowledge needed to host blockchains locally. To address the aforementioned limitations, in this paper, we propose an online voting scheme using IBM cloud-based Hyperledger Fabric. Our scheme allows voters to cast their encrypted votes in a secure manner. Then any participant can obtain the ballot results in a decentralized and transparent manner, without sacrificing the privacy of individual voters. We implement the proposed scheme using IBM cloud-based Hyperledger Fabric. The experimental results identify the performance characteristics of our scheme and demonstrate that it is feasible to run an election consisting of thousands of participants using cloud-based Fabric. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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16 pages, 4461 KiB

Open AccessArticle

Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

by Ericmoore Ngharamike, Li-Minn Ang, Kah Phooi Seng and Mingzhong Wang

Appl. Sci. 2023, 13(8), 5039; https://doi.org/10.3390/app13085039 - 17 Apr 2023

Cited by 1 | Viewed by 1369

Abstract

The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (

T_{r o}

) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the

T_{r o}

to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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15 pages, 2204 KiB

Open AccessArticle

Machine Learning Prediction of the Long-Term Environmental Acoustic Pattern of a City Location Using Short-Term Sound Pressure Level Measurements

by Juan M. Navarro and Antonio Pita

Appl. Sci. 2023, 13(3), 1613; https://doi.org/10.3390/app13031613 - 27 Jan 2023

Cited by 5 | Viewed by 1626

Abstract

To manage noise pollution, cities use monitoring systems over wireless acoustic sensor networks. These networks are mainly composed of fixed-location sound pressure level sensors deployed in outdoor sites of the city for long-term monitoring. However, due to high economic and human resource costs, it is not feasible to deploy fixed metering stations on every street in a city. Therefore, these continuous measurements are usually complemented with short-term measurements at different selected locations, which are carried out by acoustic sensors mounted on vehicles or at street level. In this research, the application of artificial neural networks is proposed for estimation of the long-term environmental acoustic pattern of a location based on the information collected during a short time period. An evaluation has been carried out through a comparison of eight artificial neural network architectures using real data from the acoustic sensor network of Barcelona, Spain, showing higher accuracy in prediction when the complexity of the model increases. Moreover, time slots with better performance can be detected, helping city managers to deploy temporal stations optimally. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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18 pages, 1554 KiB

Open AccessArticle

Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

by Fatemeh Stodt, Jan Stodt and Christoph Reich

Appl. Sci. 2023, 13(2), 782; https://doi.org/10.3390/app13020782 - 05 Jan 2023

Cited by 5 | Viewed by 1686

Abstract

ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML pipeline. The development of ML pipelines is usually conducted by several and changing external stakeholders because they are very complex constructs, and confidence in their work is not always clear. Thus, end-to-end trust in the ML pipeline is not granted automatically. This is because the components and processes in ML pipelines are not transparent. This can also cause problems with certification in areas where safety is very important, such as the medical field, where procedures and their results must be recorded in detail. In addition, there are security challenges, such as attacks on the model and the ML pipeline, that are difficult to detect. This paper provides an overview of ML security challenges that can arise in production environments and presents a framework on how to address data security and transparency in ML pipelines. The framework is presented using visual quality inspection as an example. The presented framework provides: (a) a tamper-proof data history, which achieves accountability and supports quality audits; (b) an increase in trust by protocol for the used ML pipeline, by rating the experts and entities involved in the ML pipeline and certifying legitimacy for participation; and (c) certification of the pipeline infrastructure, the ML model, data collection, and labelling. After describing the details of the new approach, the mitigation of the previously described security attacks will be demonstrated, and a conclusion will be drawn. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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19 pages, 1602 KiB

Open AccessArticle

A Knowledge Sharing and Individually Guided Evolutionary Algorithm for Multi-Task Optimization Problems

by Xiaoling Wang, Qi Kang, Mengchu Zhou, Zheng Fan and Aiiad Albeshri

Appl. Sci. 2023, 13(1), 602; https://doi.org/10.3390/app13010602 - 01 Jan 2023

Viewed by 1381

Abstract

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm. It focuses on solving multiple optimization tasks concurrently while improving optimization performance by utilizing similarities among tasks and historical optimization knowledge. To ensure its high performance, it is important to choose proper individuals for each task. Most MTO algorithms limit each individual to one task, which weakens the effects of information exchange. To improve the efficiency of knowledge transfer and choose more suitable individuals to learn from other tasks, this work proposes a general MTO framework named individually guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones, and each individual is fully explored to learn experience from the execution of other tasks. By using the concept of skill membership, individuals with higher solving ability are selected. Besides, to further improve the effect of knowledge transfer, only inferior individuals are selected to learn from other tasks at each generation. The significant advantage of IMTO over the multifactorial evolutionary framework and baseline solvers is verified via a series of benchmark studies. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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11 pages, 1983 KiB

Open AccessCommunication

Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

by Chencheng Zhou, Liudong Xing, Qisi Liu and Honggang Wang

Appl. Sci. 2023, 13(1), 422; https://doi.org/10.3390/app13010422 - 29 Dec 2022

Cited by 5 | Viewed by 2194

Abstract

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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17 pages, 910 KiB

Open AccessArticle

LAN Intrusion Detection Using Convolutional Neural Networks

by Hanan Zainel and Cemal Koçak

Appl. Sci. 2022, 12(13), 6645; https://doi.org/10.3390/app12136645 - 30 Jun 2022

Cited by 7 | Viewed by 2097

Abstract

The world’s reliance the use of the internet is growing constantly, and data are considered the most precious parameter nowadays. It is critical to keep information secure from unauthorized people and organizations. When a network is compromised, information is taken. An intrusion detection system detects both known and unexpected assaults that allow a network to be breached. In this research, we model an intrusion detection system trained to identify such attacks in LANs, and any computer network that uses data. We accomplish this by employing neural networks, a machine learning technique. We also investigate how well our model performs in multiclass categorization scenarios. On the NSL-KDD dataset, we investigate the performance of Convolutional Neural Networks such as CNN and CNN with LSTM. Our findings suggest that utilizing Convolutional Neural Networks to identify network intrusions is an effective strategy. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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Review

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28 pages, 7707 KiB

Open AccessReview

Blockchain Integration in the Era of Industrial Metaverse

by Dimitris Mourtzis, John Angelopoulos and Nikos Panopoulos

Appl. Sci. 2023, 13(3), 1353; https://doi.org/10.3390/app13031353 - 19 Jan 2023

Cited by 30 | Viewed by 5070

Abstract

Blockchain can be realized as a distributed and decentralized database, also known as a “distributed ledger,” that is shared among the nodes of a computer network. Blockchain is a form of democratized and distributed database for storing information electronically in a digital format. Under the framework of Industry 4.0, the digitization and digitalization of manufacturing and production systems and networks have been focused, thus Big Data sets are a necessity for any manufacturing activity. Big Data sets are becoming a useful resource as well as a byproduct of the activities/processes taking place. However, there is an imminent risk of cyberattacks. The contribution of blockchain technology to intelligent manufacturing can be summarized as (i) data validity protection, (ii) inter- and intra-organizational communication organization, and (iii) efficiency improvement of manufacturing processes. Furthermore, the need for increased cybersecurity is magnified as the world is heading towards a super smart and intelligent societal model, also known as “Society 5.0,” and the industrial metaverse will become the new reality in manufacturing. Blockchain is a cutting-edge, secure information technology that promotes business and industrial innovation. However, blockchain technologies are bound by existing limitations regarding scalability, flexibility, and cybersecurity. Therefore, in this literature review, the implications of blockchain technology for addressing the emerging cybersecurity barriers toward safe and intelligent manufacturing in Industry 5.0 as a subset of Society 5.0 are presented. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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16 pages, 688 KiB

Open AccessReview

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

by Raymundo Buenrostro-Mariscal, Pedro C. Santana-Mancilla, Osval Antonio Montesinos-López, Juan Ivan Nieto Hipólito and Luis E. Anido-Rifón

Appl. Sci. 2022, 12(12), 6262; https://doi.org/10.3390/app12126262 - 20 Jun 2022

Cited by 7 | Viewed by 1859

Abstract

Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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28 pages, 2725 KiB

Open AccessReview

Review of Offline Payment Function of CBDC Considering Security Requirements

by Yeonouk Chu, Jaeho Lee, Sungjoong Kim, Hyunjoong Kim, Yongtae Yoon and Hyeyoung Chung

Appl. Sci. 2022, 12(9), 4488; https://doi.org/10.3390/app12094488 - 28 Apr 2022

Cited by 13 | Viewed by 8735

Abstract

Due to the growth of the internet and communication technologies, electronic financial systems are becoming popular. Physical cash is losing its preeminence, and digital numbers on computers represent money. However, electronic financial systems, mostly operated by private entities, have defects to be compensated for, such as high charges for using the system, security issues, and the problem of exclusion. As a solution, many countries around the world are considering central bank digital currency. For central bank digital currency to be utilized as a national legal tender, it must be universal and accessible regardless of time and place, similar to physical cash. Therefore, offline payment functions that extend the accessibility of central bank digital currency are becoming attractive. However, due to the characteristics of the electronic financial system, central bank digital currency is vulnerable to possible malicious behaviors in offline situations, such as blackouts and system shutdowns. This paper reviews research studies that deal with security matters related to the offline payment function of central bank digital currency. Offline payment solutions, including central bank digital currency and other electronic financial systems, such as electronic cash and cryptocurrency, are reviewed, and supplemental methods to improve the offline payment solutions of central bank digital currency based on trusted execution environment devices are suggested. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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22 pages, 1434 KiB

Open AccessReview

Blockchain Applications in Forestry: A Systematic Literature Review

by Zhaoyuan He and Paul Turner

Appl. Sci. 2022, 12(8), 3723; https://doi.org/10.3390/app12083723 - 07 Apr 2022

Cited by 9 | Viewed by 6130

Abstract

Blockchain applications have received a lot of attention in recent years. They provide enormous benefits and advantages to many different sectors. To date, there have not been any systematic studies comprehensively reviewing current blockchain-based applications in the forestry sector. This paper examines published work on blockchain-based applications in the forestry sector. A systematic review was conducted to identify, analyze, and discuss current literature on current blockchain applications deployed (and/or proposed) in the forestry sector, grouping results into three domains of forest management, traceability of forest-based products, and forest fire detection based on content analysis. The analyses highlight reported benefits, opportunities, and challenges of blockchain applications in the forestry sector. The study results show that blockchain has great potential in sustainable forestry, minimizing illegal logging, conserving biodiversity, and many other areas in forestry. It also shows that blockchain in forestry is still immature and complex, since it requires specialists to adopt. This paper contributes towards filling the existing research gap through this systematic review on blockchain applications in forestry. This review offers insights into a deep understanding of blockchain applications for managers, practitioners, and consultants interested in forestry. The paper identifies existing research gaps on related topics of blockchain applications in forestry and makes recommendations on potential future directions for research into blockchain in forestry. Full article

(This article belongs to the Special Issue Internet of Things, Artificial Intelligence, and Blockchain Infrastructure: Applications, Security, and Perspectives)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A holistic research on Incentivised Consensus and non-incentivised consensus
Authors: Aniruddha Bhattacharjya; Venkatram Nidumolu
Affiliation: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522502, Guntur, Andhra Pradesh, India; [email protected]； Department of Electronic Engineering, Tsinghua University, Beijing, China (As Chinese Government Scholarship (CGS) holder for PhD study from 2015 to 2019 October)； Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522502, Guntur, Andhra Pradesh, India;
Abstract: Here we have done research on dissimilar incentivised consensus algorithms. These algorithms can be clustered into three major types: Proof of Work (PoW), Proof of Stake (PoS), and Hybrid Con-sensus. Also we have done deep research on the present non-incentivised consensusalgorithms that are being used in private blockchain systems which are very useful for non-crypto-currency ap-plications. We have found that these algorithms are mostly depend on classical consensus algo-rithms. But they are having very distinctive features sothat they can be added in the related blockchain systems. Security comparisons are done here for future research. Their structural properties and performances are also compared to highlight better one for future research and re-al-time usage. Here we have we have shown an exemplary decision tree-based figure for utilizing it to filter out or to filter in consensus algorithms that is perfect match for the specified criteria. This type of a figure will be a very essential tool for those who is eager to test the appropriateness of a specific consensus algorithm under specific conditions. Keywords: PoW, PoS, Chained POS, BFT POS, Delegated POS, PEERCOIN, Casper The Friendly Ghost (CTFG), OUROBOROS, Proof of Research (POR), Proof of Stake-Velocity (POSV).

Title: Use of Deep Learning to improve player engagement in a video game through a dynamic difficulty adjustment based on skills classification
Authors: dwin Alejandro Romero Mendez; Pedro C. Santana-Mancilla; Osval A. Montesinos-López; Miguel Garcia-Ruiz; Luis E. Anido-Rifón
Affiliation: School of Telematics, Universidad de Colima, Colima 28040, Mexico School of Computer Science and Technology, Algoma University, Sault Ste. Marie, ON P6A 2G4, Canada atlanTTic Research Center, School of Telecommunications Engineering, University of Vigo, 36310 Vigo, Spain

Title: Data Science Applied to the Analysis of Points of Interest in the Lisbon City
Authors: Joao C. Ferreira; Adriana Preuss; Beatriz Lopes; Ricardo Mororó; Bruno Francisco; Jose A. Afonso
Affiliation: Department of Information Sciences, Technologies and Architecture (ISTAR), ISCTE-Instituto Universitário de Lisboa, 1649-026 Lisboa, Portugal; CMEMS/LABBELS, University of Minho, 4800-058 Guimarães, Portugal

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