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
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
Blockchain-based royalty contract transactions scheme for Industry 4:0 supply-chain management
طرح معاملات قرارداد حق امتیاز مبتنی بر بلاکچین برای مدیریت زنجیره تأمین صنعت 4:0-2021
Industry 4.0-based oil and gas supply-chain (OaG-SC) industry automates and efficiently executes most of the processes by using cloud computing (CC), artificial intelligence (AI), Internet of things (IoT), and industrial Internet of things (IIoT). However, managing various operations in OaG-SC industries is a challenging task due to the involvement of various stakeholders. It includes landowners, Oil and Gas (OaG) company operators, surveyors, local and national level government bodies, financial institutions, and insurance institutions. During mining, OaG company needs to pay incentives as a royalty to the landowners. In the traditional existing schemes, the process of royalty transaction is performed between the OaG company and landowners as per the contract between them before the start of the actual mining process. These contracts can be manipulated by attackers (insiders or outsiders) for their advantages, creating an unreliable and un-trusted royalty transaction. It may increase disputes between both parties. Hence, a reliable, cost-effective, trusted, secure, and tamper-resistant scheme is required to execute royalty contract transactions in the OaG industry. Motivated from these research gaps, in this paper, we propose a blockchain-based scheme, which securely executes the royalty transactions among various stakeholders in OaG industries. We evaluated the performance of the proposed scheme and the smart contracts’ functionalities and compared it with the existing state-of-the-art schemes using various parameters. The results obtained illustrate the superiority of the proposed scheme compared to the existing schemes in the literature.
Keywords: Blockchain | Smart contract | Oil and gas industry | Supply chain management | Royalty
A review on speaker recognition: Technology and challenges
مروری بر تشخیص گوینده: فناوری و چالش ها-2021
Voice is a behavioral biometric that conveys information related to a person’s traits, such as the speaker’s ethnicity, age, gender, and feeling. Speaker recognition deals with recognizing the identity of people based on their voice. Although researchers have been working on speaker recognition in the last eight decades, advancements in technology, such as the Internet of Things (IoT), smart devices, voice assistants, smart homes, and humanoids, have made its usage nowadays trendy. This paper provides a comprehensive review of the literature on speaker recognition. It discusses the advances made in the last decade, including the challenges in this area of research. This paper also highlights the system and structure of speaker recognition as well as its feature extraction and classifiers. The use of speaker recognition in applications is also presented. As recent studies showed the possibility of fooling machine learning into giving an incorrect pre-diction; thus, the adversarial attack is also discussed. The aim is to enhance researchers’ under-standing in the area of speaker recognition.
Keywords: Biometric | Open system | Speaker recognition | Text-independent | Feature extraction | Classifier | Machine learning | Adversarial attack
Zero shot augmentation learning in internet of biometric things for health signal processing
یادگیری تقویتی صفر در اینترنت اشیا بیومتریک برای پردازش سیگنال سلامتی-2021
In recent years, the number of Internet of Things (IoT) devices has increased rapidly. The Internet of Biometric Things (IoBT) can process biometrics and health signals, and it will greatly extend the range of biometric applications. The analysis of health signals in the IoBT can use computer-aided diagnosis techniques. However, most of the existing computer-aided diagnosis methods are developed for common diseases and are not suitable for rare diseases. Zero shot learning is a potential method for the computer- aided diagnosis of rare diseases because it can identify objects of unknown categories. However, the ex- isting zero shot learning methods are based on attribute learning and rely on an attribute dataset. There is no attribute dataset for health signal processing. Therefore, the existing zero shot learning methods are not suitable for health signal processing. Based on the above background, we propose a zero shot aug- mentation learning model (ZSAL) in the IoBT for health signal processing. First, an expert doctor identiﬁes the contour of a lesion and selects a background image without a lesion. Second, the computer automatically generates virtual images using zero shot augmentation technology. Finally, the generated virtual dataset is used to train a convolutional classiﬁer, and then we apply the classiﬁer to the computer-aided diagnosis of actual medical images. The experiment shows the eﬃciency and effectiveness of our method.© 2021 Elsevier B.V. All rights reserved.
Keywords: Internet of biometric things | Zero shot learning | Data augmentation | Health signal processing
به سمت لبه هوشمند: ارتباطات بی سیم به یادگیری ماشین میرسد
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 31
احیای هوش مصنوعی در اواخر (AI) تقریباً در هر شاخهای از علم و فناوری، انقلابی ایجاد کرده است. با توجه به گجتهای تلفن همراه هوشمند و همه جا حاضر و دستگاههای اینترنت اشیا (IoT)، انتظار میرود که اکثر برنامههای هوشمند را بتوان در لبهی شبکههای بی سیم استقرار داد. این روند باعث شده است، تمایل قوی برای تحقق «لبه هوشمند» ایجاد شود تا از برنامههای کاربردی مجهز به AI در دستگاههای لبه مختلف استفاده شود. بر این اساس، یک حوزهی پژوهشی جدید به نام یادگیری لبه به ظهور رسیده است که از دو رشته عبور میکند و انقلابی در آنها ایجاد میکند: ارتباطات بی سیم و یادگیری ماشین. یک موضوع اصلی در یادگیری لبه غلبه بر قدرت محاسباتی محدود و همچنین دادههای محدود در هر دستگاه لبه است. این امر با استفاده از پلت فرم محاسبات لبه تلفن همراه (MEC) و استخراج دادههای عظیم توزیع شده در تعداد زیادی دستگاه لبه محقق شده است. در چنین سیستمهایی، یادگیری از داده توزیع شده و برقراری ارتباط بین سرور لبه و دستگاهها دو جنبهی حیاتی و مهم است و همجوشی آنها، چالشهای پژوهشی جدید و زیادی را به همراه دارد. این مقاله از یک مجموعه جدید از اصول طراحی برای ارتباطات بی سیم در یادگیری لبه پشتیبانی میکند که در مجموع ارتباطات یادگیری محور نامیده میشوند. مثالهای گویایی ارائه شدند تا اثربخشی این اصول طراحی مشخص شوند و برای این منظور فرصتهای تحقیقاتی منحصر به فردی شناسایی شدند.
کلمات کلیدی: سرورها | مدل سازی جوی | هوش مصنوعی | پایگاه های داده توزیع شده | ارتباطات بی سیم | یادگیری ماشین | مدل سازی محاسباتی
|مقاله ترجمه شده|
A 2020 perspective on “Transformative value of the Internet of Things and pricing decisions”
چشم انداز 2020 در مورد "ارزش تحول پذیر اینترنت اشیا و تصمیمات قیمت گذاری"-2020
The Internet of Things (IoT) has become increasingly influential, particularly because of the significant new developments in the technologies of big data, cloud computing, 5G, and artificial intelligence. In this paper, we briefly explain how these new developments in the IoT may create a new electronic commerce landscape and opportunities associated with it; these developments pose interesting questions for future research.
Keywords: Internet of Things | Business management | Computational social science (CSS) | Data analytics
Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement
احراز هویت سریع و مجوز پیشرو در اینترنت اشیا با مقیاس بزرگ: نحوه استفاده از هوش مصنوعی برای تقویت امنیت-2020
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.
Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
Digital transformation: Five recommendations for the digitally conscious firm
تحول دیجیتال: پنج توصیه برای شرکت آگاه دیجیتالی-2020
Digital transformation is one of the key challenges facing contemporary businesses. The need to leverage digital technology to develop and implement new business models forces firms to reevaluate existing capabilities, structures, and culture in order to identify what technologies are relevant and how they will be enacted in organizational processes and business offerings. More often than not, these profound changes require firms to revisit old truths as they develop strategies that thread the needle between beneficial innovation and harmful disruption. This article uses the Internet of Things (IoT) as a backdrop to demonstrate the concerns associated with transformative technologies and offers five recommendations as to how firms can develop the strategies needed for digital transformation and become digitally conscious: (1) Start small and build on firsthand benefits; (2) team up and create competitive advantage from brand recognition; (3) engage in standardization efforts; (4) take responsibility for data ownership and ethics; and (5) own the change and ensure organization-wide commitment. As such, this article shows that digital transformation should be a top management priority and a defining trait of corporate business strategy, and that by becoming digitally conscious, firms may get a head start on their transformation journey.
KEYWORDS: Digital transformation | Digitization | Digitalization | Internet of Things | Digital consciousness