Trustworthy authorization method for security in Industrial Internet of Things
روش مجوز معتبر برای امنیت در اینترنت اشیا صنعتی-2021
Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer inter- action (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.
Keywords: Industrial Internet of Things (IIoT) | Human–computer interaction (HCI) | Biometric recognition | Comprehensive texture | Security system
Towards resilient and sustainable supply of critical elements from the copper supply chain: A review
به سمت تأمین انعطاف پذیر و پایدار عناصر حیاتی از زنجیره تامین مس: یک مرور-2021
The highly specialized materials needed for the de-carbonization of energy, smart devices and the internet of things have created supply concerns of critical elements used in these applications. Several critical elements are produced as by-products from base metal mining and processing. Increasing the capture of critical elements from existing operations should lead to a more resilient and sustainable supply of these elements. Towards this goal, this paper presents a review of the distribution behavior of five critical elements (selenium, tellurium, arsenic, antimony and bismuth) through the primary copper pyrometallurgical supply chain. This review identifies gaps in the distribution/concentration data of these elements in deposits and during mineral processing. Smelter dusts, refinery slimes and electrolyte are points of enrichment that can be targeted for additional recovery of these elements. Using published data, copper smelter dusts appear to contain enough arsenic and bismuth to meet the world’s supply needs. Industrial data collected from 29 refineries and represents ~46% of the worlds electrorefining production was extrapolated to examine the contained annual content of these five elements. Copper anodes contain 7900 tones/yr of selenium, 2300 tonnes/yr of tellurium, 24,000 tones/yr arsenic, 7100tonnes/yr of antimony and 5100 tones/yr of bismuth. The selenium and tellurium contents are 2–3 times and 4–5 times more than the current world’s annual production of these elements, respectively. While technology development in the processing of smelter dusts and refinery slimes could provide important breakthroughs, government and corporate collaboration are likely needed to encourage increased recovery of selenium, tellurium, arsenic, antimony and bismuth from the primary copper pyrometallurgical supply chain.
Keywords: Critical elements | Copper | Ore | Flotation | Smelting | Refining
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network
یک مسیریابی فرصت طلبانه جغرافیایی معتبر مبتنی بر بیومتریک برای برنامه های IoT با استفاده از شبکه حسگر بی سیم امن-2021
The applications of Wireless Sensor Networks (WSNs) are been broadly utilized in the field of Internet of Things (IoT) under communication framework. Notwithstanding services gave by the WSNs, numerous IoT-related applications necessitate reliable and secure delivery of data over unsteady remote connec tions. In-order to ensure secure and reliable delivery of data, many existing paper works accomplish authentication based routing algorithms with numerous forwarders within the Wireless Sensor Networks. Be that as it may; these types of approaches are vulnerable to genuine attacks like Denial of Service (DoS), where countless duplicate data packets are intentionally dispatched to destination node which disturbs the typical activities of wireless sensor networks. So, here we propose a new scheme of security algorithm for the wireless sensor networks. Our method, Biometric based-Authenticated Geographic Opportunistic Routing (BAGOR) algorithm depends on the user biometrics to shield the violation of DoS attacks, in order to meet out the validness requirements and reliability in the network. By examining biometric and statistic state information (SSI) of remote connections, BAGOR uses a trust model as statistic state information to get better proficiency of packet delivery. Dissimilar to past pioneering routing algorithm, BAGOR guarantees data honesty by building up an entropy-deployed selective validation algorithm and can detach DoS aggressors and reduce the computational expense. Thus, the eveloped procedure is assessed and compared with already existing security techniques. The simulations show that BAGOR decreasing system traffic, shielding against Denial of Service attacks, and expanding the lifetime of a sensor node in the network. Thus, the usefulness and execution of the whole system is enhanced.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Biometric authentication | BAGOR algorithm | Denial-of-Service attacks | Geographic opportunistic routing | Statistic state information
Supply chain financial service management system based on block chain IoT data sharing and edge computing
سیستم مدیریت خدمات مالی زنجیره تامین مبتنی بر اشتراک داده های اینترنت اشیا زنجیره بلاکچین و محاسبات لبه ای-2021
The implementation of the ‘‘Internet +” policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises.
KEYWORDS: Block chain | Internet of things | Edge computing | Supply chain finance | Commercial bank
Multifactor authentication scheme using physically unclonable functions
طرح احراز هویت چند عاملی با استفاده از توابع غیرقابل تنظیم فیزیکی-2021
We propose a secure telehealth system using multifactor authentication for the mobile de- vices as well as the IoT edge devices in the system. These two types of devices constitute the weakest link in telehealth systems. The mobile devices and edge devices are typically unsecured and contain vulnerable processors. The mobile devices use the healthcare professional’s biometric and endowing the edge device with biometrics is accomplished by using physically unclonable functions (PUFs). The embedded PUF acts as a means of enabling mutual authentication and key exchange. Evaluating the security of the proposed authentication scheme is conducted using three approaches: (a) formal analysis based on Burrows–Abadi–Needham logic (BAN); (b) informal security analysis for protection against many attack types.; (c) model checking using automated validation of internet security protocols and applications (AVISPA) tool.© 2020 Elsevier B.V. All rights reserved.1.
Keywords: Internet of Things | Device authentication | Hardware security | Embedded systems | PUF | AVISPA | BAN | Three-factor authentication
Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic
استفاده از اینترنت اشیا برای طراحی شبکه زنجیره تأمین امداد برای همه گیری SARS-COV-2-2021
The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient’s condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.© 2021 Elsevier B.V. All rights reserved.
Keywords: Supply chain design | Epidemic outbreaks | Industry 4.0 | COVID-19 | SARS-COV-2
Uncovering research streams on agri-food supply chain management: A bibliometric study
کشف جریانهای تحقیق در مورد مدیریت زنجیره تأمین مواد غذایی کشاورزی: یک مطالعه کتابشناختی-2021
This study carried out a bibliometric analysis to critically review the evolution of the agri-food supply chain (AFSC) research field over the period of 2008–2019. A set of 1236 articles was analyzed from the Web of Science database. Besides using different analytical scientometric tools (topic mapping, co-citation, co-authorship and overlay visualization networks), this study identified frequently-used keywords, new and hot research topics and frequently-studied supply chain management (SCM) practices. Frequently used keywords are food supply chain, food waste, sustainability, food safety, SCM, food industry, and food security. New research themes include contract, blockchain, internet of things, resilience, and short food supply chain, a topic that demands further research especially due to the international COVID-19 pandemic and the need of farmers to be closer to the consumers. Hot research topics, that is, subjects that have been studied in highly cited papers were also identified include life cycle assessment, environmental impact, packaging, water use, food waste prevention, food waste generation, blockchain and carbon footprint. Among SCM practices, this study observed that risk and sustainable SCM are frequently used keywords. Procurement and reverse logistics were observed in fewer studies. SCM, food waste, food quality, GHG emissions and risk management are sustainable SCM practices frequently observed.
Keywords: Agri-food supply chain | Bibliometric analysis | Co-authorship | Co-citation analysis | Scientometrics | Supply chain management practices
Research on the financing income of supply chains based on an E-commerce platform
تحقیق در مورد درآمد تأمین مالی زنجیره های تأمین براساس یک بستر تجارت الکترونیکی-2021
Rapid economic development has brought about the expansion of the supply chain. In the context of the demand for finance and emerging financial technology tools, supply chain finance on e-commerce platforms is developing rapidly. It not only strengthens the ability to serve the real economy, but also brings market risks caused by excessive supply chains. In the Internet era, IoT technology promotes the exchange of information, while it also has certain risk characteristics. This research implements the peaks over threshold (POT) model to investigate the value at risk (VaR) and expected loss (ES) in the supply chain of e-commerce platforms under the risk of un- expected changes in the market. The study finds that the supply chain of e-commerce platforms based on Internet of Things (IoT) technology suffers less risk in losses. The application and expansion of this technology will effectively lower the market risk of supply chain finance and better serve economic development.
Keywords: E-commerce platform | Supply chain | Market risk | POT model
The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions
انقلاب آرام در بینایی ماشین-مقاله ای پیشرفته مروری، شامل مرور تاریخی ، چشم اندازها و جهت های آینده-2021
Over the past few years, what might not unreasonably be described as a true revolution has taken place in the ﬁeld of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efﬁcacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.© 2021 Elsevier B.V. All rights reserved.
Keywords: Machine vision | Machine learning | Deep learning | State-of-the-art