ABSTRACT
Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
ABSTRACT
In developing countries, funding is a significant obstacle to receiving higher education. Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries' economies are poor. As a result, the students' talents are not harnessed to their full potential. In order to help students obtain higher education and harness their full potential, governments provide student loans to students in higher education. The government provides loans to students through the ministry of education. The students pay back the loan with interest when they start working. Governments have been the sole funders of student loans. The emergence of COVID-19 and the Russia-Ukraine war have resulted in a global economic crisis. Because of the global economic crisis, the government's spending has increased. In order to help reduce the burden of government and thereby reduce spending, we intend to revolutionize the student loan program through blockchain and crowdsourcing. This work presents a blockchain-based crowdsourcing decentralized loan platform where investors will be brought on board to provide funds for students in higher education. The platform will allow students to apply for loans from investors through registered financial institutions. The students will pay back the loans with interest when they enter the workforce. The proposed platform will allow students to fund their education, investors will get interest on the money they invest, and governments can channel the money they put into student loan programs into other avenues. We perform a thorough security analysis and back the efficiency of our work with numerical results. © 2013 IEEE.
ABSTRACT
Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services have proven to be essential measures to counteract the ensuing disrupting effects in social and working interactions. In such scenarios, Nausica@DApp, the comprehensive solution proposed in this paper, eases compatibility of the in-presence activities of a campus-based corporation with the organizational constraints posed by the virus during the pandemic, or at a later endemic stage. This is accomplished throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure, proving to be encouragingly accurate in most cases. Absolute localization, on the other hand, entails a certain amount of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in the handling of sensitive personal information, in spite of the centralized aspects of the localization system. This is accomplished by decentralizing contact tracing matching operations, which are entirely carried out locally, by apps running on the users' mobile devices. Contact data are pseudonymized and their authenticity is guaranteed by a blockchain. Furthermore, the proposed novel solution improves privacy preservation by eschewing recourse to the Bluetooth app-to-app channel for user data exchange, in fact a typical choice of most current contract tracing solutions. Thanks to a sensible use of the blockchain features, integrated into Nausica@DApp's microservice-based back-end, a higher degree of operation transparency can be relied upon, thus boosting the user's level of trust and enhancing the availability and reliability of data about people gathering within the campus premises. Moreover, contact tracing only requires the mobile device WiFi interface to be on, so that users are neither forced to adopt new habits, nor to grant additional device access permissions to contact tracing apps (potentially undermining their own privacy). The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.
ABSTRACT
Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms. IEEE
ABSTRACT
File sharing is one of the most common uses in Peer-to-peer (P2P) networks and structured P2P systems, such as BitTorrent (BT), which may overcome the limitations of features such as improved scalability, efficiency, and deterministic data location. Recent research has attempted to reduce inter-ISP P2P traffic by including locality awareness into neighbor-selection strategies of popular P2P apps, which prioritize adjacent peers over distant peers when transferring data, reducing server bandwidth burden, and inter-ISP traffic expense. However, the locality awareness P2P exchanged, handled and coordinated by the network infrastructure to turn around traffic as nearest as possible to the end users was proposed for the passive optical network (PON) because of the large amount of bandwidth in only one single fiber. The PON offers many system architectures;in particular, the NG-PON2 with colorless ONUs, adopting time and wavelength division multiple access technology is often recognized as the best access network solution for simplifying network operation, lowering installation costs, and maintenance costs under control. Software-defined networking (SDN) has sparked interest in various fields as a viable research topic, promising better agility, improved automation, security, and lower capital and operating costs. Taking advantage of the SDN's OpenFlow protocol in NG-PON2, centralized control renders more flexible control over the BT traffic (P2P intra-traffic) file sharing application provided by different ISPs. Finally, we evaluated our proposed scheme in real-time traffic share collected by Sandvine's report on 2020 despite the Covid-19 pandemic for different geographic regions, for example;Global, APAC, EMEA, and AMERICA.
ABSTRACT
Purpose>The present paper is an attempt to study Education 4.0 supported by Industry 4.0 tools and techniques. The main purpose of the study is to examine the acceptance and use of one of the internet of things (IoT)-based learning management systems, i.e. videoconferencing application (Google Meet, Microsoft Teams, Zoom, GoToMeeting, WebEx), by academicians of higher education using the unified theory of acceptance and use of technology (UTAUT) model.Design/methodology/approach>The study comprises 218 responses of academicians associated with higher education in the Sultanate of Oman. Descriptive and factor analysis of the collected data are employed using SPSS-26. Further, using Amos-21, the fit and validity indices of the measurement model are computed. Various relationships of the UTAUT structural model along with moderation effects of gender and nationality are tested.Findings>The results suggest that performance expectancy, effort expectancy and social influence significantly predict behavioral intention. In turn, behavioral intention and facilitating conditions also significantly predict the use behavior of academicians for videoconferencing in higher education. Finally, gender moderates two out of four UTAUT relations, but nationality does not moderate any of these relations.Originality/value>A lot of prior studies investigate several models to use technology-enabled pedagogy from educators' or students' perspectives. There are very limited studies that examine IoT-based learning tools within the UTAUT environment. Additionally, no study is available that considers UTAUT relations for the use of videoconferencing in higher education. Also, in the present study, one more moderator, i.e. nationality, is tested.
ABSTRACT
Scholars are increasingly examining how the distributed blockchain technology can counter specific supply chain and operations management challenges. Various research approaches emerge from different scholarly backgrounds, but the interrelation of research areas and current trends has not been adequately considered in a systematic review. We employ a data-driven content analysis approach to examine previous research on blockchain technology in operations management and supply chain management. We investigate the extent to which blockchain technology was considered in scholarly works, structure the research efforts, and identify trends, interrelated themes, and promising research opportunities. Quantitative and qualitative content analysis is conducted on an extensive literature sample of 410 articles. Results indicate an optimistic attitude due to potentials such as tracking and tracing abilities, efficiency increases, and trust-building. Conceptual studies dominate the literature set, with increasing qualitative research efforts. Grand theories are seldomly addressed in the studies. Blockchain technology is outlined as particularly useful when combined with other technologies like IoT. We also identified sustainability implications of the technology, such as enabling transparency for SC stakeholders. Cryptocurrencies can facilitate further efficiency gains if legal uncertainties are reduced. The study is concluded with managerial and theoretical implications and future research opportunities.
ABSTRACT
Robot Operating System (ROS) has received widespread utilization with the development of robotics, self-driving, etc., recently. Meanwhile, the other technology blockchain is frequently applied to various fields with its trustworthy characteristics and immutability in data storage. However, ROS has no ability to interact with the blockchain, which hinders research in related fields. Therefore, we wonder if we can develop a convenient tool to bridge ROS and blockchain. Inspired by this, we propose ROS-Ethereum. It bridges ROS and Ethereum, a widely used blockchain platform. ROS-Ethereum is based on the User Datagram Protocol (UDP) communication mechanism and the SM algorithm family along with Ethereum technology. Simply put, ROS-Ethereum allows users to invoke the contract when interacting with the blockchain, which makes this process easier and safer. We conduct experiments in real robots to verify the effectiveness of ROS-Ethereum and evaluate it from the following metrics: (1) the encryption efficiency and stability of the algorithm and (2) ROS-Ethereum transaction response time and packet loss rate.
ABSTRACT
Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations. Author
ABSTRACT
Education is the foundation of basic human rights and the peace of mankind as well as the pursuit of sustainable development. In this article, we have developed a blockchain-centered educational program contributing to Goal 4, Quality Education, among the Sustainable Development Goals (SDGs) and Education for SD (ESD). The education program featured herein consisted of three lessons in total and has been developed to accommodate gamification techniques. We implemented the education program on 704 elementary school students in Korea for three years from 2019 to 2021. The effectiveness of the education program was primarily analyzed by paired t-test and technical statistics analysis. In a pre-and post-education survey, digital literary, literacy, and numeracy were all significantly improved, with satisfaction level rated at 3.91 out of 5 points. We hope that the blockchain-themed education program proposed herein will provide implications for the promotion of inclusive and equitable quality education.
ABSTRACT
Pornography has become a major problem of today cyberspace, extended through the Internet at the planetary scale. The phenomenon has deep social and economic implications affecting a huge number of children under the age of eighteen who are subjects of sexual abuse and exploitation. Child pornography generally refers to images or films depicting sexually explicit activities involving a child. Because child pornography involves children, the test of acceptability is much stricter than for regular pornography, as consent cannot be given either by the child or guardian. Child welfare professionals are worried that paedophiles will use the third-generation mobile technology to access child sex sites, take pictures of sex with children and trade in images of abuse.
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The paper presents a distributed approach to online cyber risk assessment across the country, taking into account cyber threats and vulnerabilities identified by local services operators. It consists in distributed, asynchronous calculations of possible failure scenarios. They are a solution of a set of nonlinear, nonsmooth equations with locally assessed risk activation functions as inputs. These functions indicate whether a given threat is expected in some future period. The convergence condition of the mentioned algorithm is given in the theorem form. At the end, a case study concerning a system consisting of four entities is presented.
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Lifelong learning approaches that include digital, transversal, and practical skills (i.e., critical thinking, communication, collaboration, information literacy, analytical, metacognitive, reflection, and other research skills) are required in order to be equitable and inclusive and stimulate personal development. Realtime interaction between teachers and students and the ability for students to choose courses from curricula are guaranteed by decentralized online learning. Moreover, through blockchain, it is possible to acquire skills regarding the structure and content while also implementing learning tools. Additionally, documentation validation should be equally crucial to speeding up the process and reducing costs and paperwork. Finally, blockchains are open and inclusive processes that include people and cultures from all walks of life. Learning in Higher Education Institutions (HEI) is facilitated by new technologies, connecting blockchain to sustainability, which helps understand the relationship between technologies and sustainability. Besides serving as a secure transaction system, blockchain technology can help decentralize, provide security and integrity, and offer anonymity and encryption, therefore, promoting a transaction rate increase. This study investigates an alternative in which HEI include a blockchain network to provide the best sustainable education system. Students’ opinions were analyzed, and they considered that blockchain technology had a very positive influence on learning performance.
ABSTRACT
Cryptocurrencies have transgressed ever-changing economic trends in the global economy, owing to their conveyance, security, trust, and the ability to make transactions without the aid of formal institutions and governing bodies. However, the adoption of cryptocurrency remains low among stakeholders, including e-retailers. Thus, the current work explores the intentions of e-retailers in the Asia and Pacific region to adopt cryptocurrencies. This study considers the TAM-based SOR, with a combination of non-cognitive attributes (compatibility and convenience) proposed as stimuli for e-retailers to adopt the examined cryptocurrencies. The findings indicate that the proposed non-cognitive attributes are critical in determining e-retailers’ technostress (emotional state). Moreover, it was found that technostress among e-retailers profoundly impacts their intentions to adopt cryptocurrency in business settings. Meanwhile, regulatory support communication can be used to help regulatory bodies and governing institutions control the future economy worldwide. The proposed study offers significant theoretical and practical contributions through its investigation of e-retailers’ intentions to adopt cryptocurrency for the first time in the particular context of technostress and regulatory support.
ABSTRACT
In current situation, Coronavirus Disease-2019 (COVID-19) becomes a very serious health concern to the human life throughout the world. The Internet of Medical Things(IoMT) allows to deploy several wearable Internet of Things(IoT) enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated Fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of Fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new Fog-based private blockchain-enabled home monitoring framework. Next, we discuss how Artificial Intelligence(AI)-enabled Big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system. IEEE
ABSTRACT
As strategies for the development of creative tourism multiply in contexts ranging from global cities to small communities, the need for a clearer conceptual framework, capable of better differentiating existing approaches, has become more evident. Building upon existing knowledge on cultural and creative tourism, and on a prior review of the literature on creative tourism on islands, this paper proposes a typology of creative tourism initiatives, and after examining this new categorization, develops the concept of sustainable creative tourism. Through highlighting the need for creative tourism developers, policymakers, and practitioners to deepen their engagement with more holistic and integrated approaches oriented towards sustainable development in all its dimensions (economic, environmental, social, and cultural), the authors systematize a set of international principles and policy recommendations meant to promote sustainable creative tourism development strategies, duly adjusted to different places and realities, with an emphasis on vulnerable islandscapes.