信息工程最新文献

The proliferation of Internet of Things (IoT) devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased security. We argue that digital twins of IoT devices, with the right design, can enhance the security, reliability, auditability, and interoperability of IoT systems. The salient features of digital twins have made them key elements for the IoT and Industry 4.0. In this paper, we leverage advances in W3C's Web of Things (WoT) standards and distributed ledger technologies (DLTs) to present a novel design of the smart contract-based digital twins with enhanced security, transparency, interoperability, and reliability. We provide two different variations of that general design using two different blockchains (one public and one private, permissioned blockchain), and we present design trade-offs. Furthermore, we introduce an architecture for accessing and controlling IoT devices securely and reliably, providing full auditability, while at the same time using the proposed digital twins as an indirection mechanism (proxy). The proposed architecture leverages the blockchain to offer notable properties, namely,decentralization, immutability, auditability, non-repudiation, availability, and reliability. Moreover, it introduces mass actuation, easier management of IoT devices, and enhanced security to the IoT gateways, enables new business models, and makes consumer devices (vendor-)agnostic.

Laboratory wastewater has been suggested as an important escape route for microorganisms from research environments. Likely reasons for the unintentional release of laboratory organisms are shortcomings in the handling of infectious liquid biological waste (LBW) and inadequate inactivation procedures. We developed an analytical approach to investigate the use of chemical inactivation (CI) procedures in Swiss research laboratories by on-site random sampling of presumably inactivated infectious LBW and testing it for the presence of infectious lentiviruses (HIV-1) and adenoviruses (AdV). In addition, standard operating procedures (SOPs) for CI were collected and evaluated, and laboratory-staff knowledge of CI processes was assessed using a questionnaire. Although we found several deficiencies in the technical knowledge and training of laboratory staff on the CI of LBW, as documented by 27 returned questionnaires, no infectious viruses were detected in the eight LBW samples collected. Whilst we acknowledge that the number of LBW samples and SOPs is small, we conclude that CI of LBW containing infectious lentiviruses and adenoviruses does not result in the systematic release of considerable amounts of infectious viruses into the environment from research laboratories in Switzerland.

Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using blockchain and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a blockchain prevention module and an ML detection module. The blockchain prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.

Web3, the next generation of the Internet, represents a decentralized and democratized web. Although it has garnered significant public interest and found numerous real-world applications, there is a limited understanding of people's perceptions and experiences with Web3. In this study, we conducted an empirical study to investigate the categories of Web3 applications and their popularity, as well as the potential challenges and opportunities within this emerging landscape. Our research was carried out in two phases. In the first phase, we analyzed 200 popular Web3 projects associated with 10 leading Web3 venture capital firms. In the second phase, we collected and examined code-related data from GitHub and market-related data from blockchain browsers (e.g., Etherscan) for these projects. Our analysis revealed that the Web3 ecosystem can be categorized into two groups, i.e., Web3 infrastructure and Web3 applications, with each consisting of several subcategories or subdomains. We also gained insights into the popularity of these Web3 projects at both the code and market levels and pointed out the challenges in the Web3 ecosystem at the system, developer, and user levels, as well as the opportunities it presents. Our findings contribute to a better understanding of Web3 for researchers and developers, which in turn promotes further exploration and advancement in this innovative field.

In recent years, there has been a growing interest in real estate investments that utilize blockchain technology. Traditional real estate investments usually involve third-party intermediaries for verifying and recording informal real estate transactions. This paper proposes a blockchain-based real estate investment model and presents a detailed description of the real estate register authentication aspect of the model. The model uses blockchain technology to create tamper-evident records of real estate transactions and provide secure authentication and verification for informal real estate transactions. Meanwhile, each real estate transaction is recorded in a block, and all transaction records are kept on the blockchain. This means that inventors can access these transaction records and verify their authenticity and validity. The system can also use smart contracts to automate the process of real estate transactions, which further improves transaction efficiency and reduces costs. Furthermore, the model's timestamp and authentication mechanism can eliminate third-party intermediaries and ensure the authenticity and validity of real estate transactions through distributed ledgers and verification mechanisms. Overall, blockchain-based real estate systems offer advantages of security, transparency, efficiency, and cost reduction. With ongoing blockchain advancements, these systems are expected to play a crucial role in future real estate investment transactions.

The monkeypox epidemic has become a global health issue due to its rapid transmission involving nonhuman-to-human transmission in nonendemic areas. Various actions, such as quarantine, vaccination, and hospitalization, have been implemented by worldwide governments. Given the relatively high cost and strict implementation of vaccination, our focus lies on quarantine and hospitalization. In this paper, we study the monkeypox epidemic involving quarantine and hospitalization through fractional-order mathematical modeling. The proposed model considers six classes of human populations (susceptible, exposed, infected, quarantined, hospitalized, and recovered) and three classes of nonhuman populations (susceptible, exposed, and infected). The basic properties of the model have been investigated, and its equilibrium points have been obtained, namely monkeypox-free, nonhuman-free endemic, and endemic. We have derived the basic reproduction numbers for human-to-human and nonhuman-to-nonhuman transmissions, denoted as $R_{0h}$ and $R_{0n}$ respectively. The existence and stability (both locally and globally) of each equilibrium point depend on $R_{0h}$ and $R_{0n}$ relative to unity. We performed calibration and forecasting of the model on the weekly monkeypox case data of the human population in the United States of America from June 1 to September 23, 2022. Research findings indicate that the fractional-order model shows better calibration and forecasting compared to the corresponding first-order model based on the root mean square error. Furthermore, the best-fitting model calibration indicates $R_0=\max \{R_{0h},R_{0n}\}>1$, suggesting the potential for endemic conditions in humans. However, the best forecasting shows $R_0<1$, possibly due to various policies such as vaccination. Given the relative cost and stringency of vaccination implementation for monkeypox control, we perform numerical simulations and sensitivity analyses on the basic reproduction number, particularly focusing on the impact of quarantine and hospitalization rates. Simulations and sensitivity analysis indicate that simultaneous increases in quarantine and hospitalization rates can reduce the basic reproduction number $R_{0h}$