Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds
اختلالات مربوط به تأمین و سایبر در شرکت های زنجیره تامین ابر: تعیین بهترین سرعت بازیابی-2021
This study investigated the speeds (i.e., radical, incremental, relaxed benchmarking, rigorous benchmarking, matching, and market-driven) of firms’ recovery from supply- and cyber-related disruptions in cloud supply chains (SCs). Supply-related disruptions downgrade the firm’s operational capabilities (e.g., production capacity and labor supply), and cyber-related disruptions reduce its intangible capabilities (e.g., reputation, brand image, and public trust). This study introduced a cellular automata (CA) simulation model to determine the best recovery speeds following the loss of operational and intangible capabilities. Furthermore, to investigate the impact of cloud adoption on an SC firm’s best speeds of recovery from supply-related disruptions, we compared firms that had adopted the cloud with those using the on-site data centers.
Keywords: Supply chain | Cloud computing | Disruption | Recovery | Cellular automata simulation
A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions
چارچوب تصمیم ترکیبی مبتنی بر فازی برای مدور بودن در زنجیره های تامین لبنیات از طریق راه حل های داده های بزرگ-2021
This study determines the potential barriers to achieving circularity in dairy supply chains; it proposes a framework which covers big data driven solutions to deal with the suggested barriers. The main contribution of the study is to propose a framework by making ideal matching and ranking of big data solutions to barriers to circularity in dairy supply chains. This framework further offers a specific roadmap as a practical contribution while investigating companies with restricted resources. In this study the main barriers are classified as ‘eco- nomic’, ‘environmental’, ‘social and legal’, ‘technological’, ‘supply chain management’ and ‘strategic’ with twenty-seven sub-barriers. Various big data solutions such as machine learning, optimization, data mining, cloud computing, artificial neural network, statistical techniques and social network analysis have been suggested. Big data solutions are matched with circularity focused barriers to show which solutions succeed in overcoming barriers. A hybrid decision framework based on the fuzzy ANP and the fuzzy VIKOR is developed to find the weights of the barriers and to rank the big data driven solutions. The results indicate that among the main barriers, ‘economic’ was of the highest importance, followed by ‘technological’, ‘environmental’, ‘strategic’, ‘supply chain management’ then ‘social and legal barrier’ in dairy supply chains. In order to overcome circularity focused barriers, ‘optimization’ is determined to be the most important big data solution. The other solutions to overcoming proposed challenges are ‘data mining’, ‘machine learning’, ‘statistical techniques’ and ‘artificial neural network’ respectively. The suggested big data solutions will be useful for policy makers and managers to deal with potential barriers in implementing circularity in the context of dairy supply chains.
Keywords: Dairy supply chain | Barriers | Circular economy | Big data solution | Fuzzy ANP - VIKOR | Group decision making system
Framework of Data Analytics and Integrating Knowledge Management
چارچوب تجزیه و تحلیل داده ها و ادغام مدیریت دانش-2021
Big data is significantly dependent on technologies such as cloud computing, machine learning and statistical models. However, its significance is becoming more dependent on human qualities e.g. judgment, value, intuition and experience. Therefore, the human knowledge presents a basis for knowledge management and big data, which are a major element of data analytics. This research contribution applies the process of Data, Information, Knowledge and Perception hierarchy as a structure to evaluate the end-users’ process. The framework in incorporating data analytics and display a conceptual data analytics process (with three phases) evaluated as knowledge management, including the creation, discovery and application of knowledge. Knowledge conversion theories are applicable in data analytics to emphasize on the typically overlooked organizational and human aspects, which are critical to the efficiency of data analytics. The synergy and alignment between knowledge management and data analytics is fundamental in fostering innovations and collaboration.
keywords: تحلیل داده ها | مدیریت دانش | داده های بزرگ | هوش تجاری | کشف داده ها | Data analytics | Knowledge management | Big data | Business intelligence | Data discovery
Multimodal biometric authentication for mobile edge computing
Multimodal biometric authentication for mobile edge computing-2021
In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) sys- tem designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing.© 2021 Elsevier Inc. All rights reserved.
Keywords: Privacy Preserving Biometric Authentication (PPBA) | Mobile Edge Computing (MEC) | Multimodal Biometrics | Hill Climbing Attacks (HCA) | Euclidean distance | Malicious security
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
MISS-D: A fast and scalable framework of medical image storage service based on distributed file system
MISS-D: یک چارچوب سریع و مقیاس پذیر از خدمات ذخیره سازی تصویر پزشکی بر اساس سیستم فایل توزیع شده-2020
Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging
Automatic underground space security monitoring based on BIM
نظارت بر امنیت خودکار فضای زیرزمینی بر اساس BIM-2020
Traditional underground space safety monitoring is ineffective as data continuity is weak, systematic and random errors are prominent, data quantification is difficult, data stability is scarce (especially in bad weather), and it is difficult to guarantee human safety. In this study, BIM technology and multi-data wireless sensor network transmission protocol, cloud computing platform are introduced into engineering monitoring, real-time online monitoring equipment, cloud computing platform and other hardware and software are developed, and corresponding online monitoring system for structural safety is developed to realize online monitoring and early diagnosis of underground space safety. First, the shape of the underground space, the surrounding environment, and various monitoring points are modeled using BIM. Then, the monitoring data collected from sensors at the engineering site are transmitted to the cloud via wireless transmission. Data information management is then realized via cloud computing, and an actual state-change trend and security assessment is provided. Finally, 4D technology (i.e., 3D model + time axis) that leverages a deformation chromatographic nephogram is used to facilitate managers to view deformation and safety of their underground spaces. To overcome past shortcomings, this system supports the management of basic engineering project data and storage of historical data. Furthermore, the system continuously reflects the fine response of each monitoring item under various working conditions all day, which has significant theoretical value and application.
Keywords: BIM technology | Deformation monitoring | Automation information | Management model
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
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
Adaptive AI-based auto-scaling for Kubernetes
مقیاس گذاری خودکار مبتنی بر هوش مصنوعی تطبیقی برای Kubernetes-2020
Kubernetes, the prevalent container orchestrator for cloud-deployed web applications, offers an automatic scaling feature for the application provider in order to meet the everchanging amount of demand from its clients. This auto-scaling service, however, requires a seemingly difficult parameter set to be customized by the application provider, and those management parameters are static while incoming web request dynamics often change, not to mention the fact that scaling decisions are inherently reactive, instead of being proactive. Therefore we set the ultimate goal of making cloud-based web applications’ management easier and more effective. We propose a Kubernetes scaling engine that makes the auto-scaling decisions apt for handling the actual variability of incoming requests. In this engine various AI-based forecast methods compete with each other via a short-term evaluation loop in order to always give the lead to the method that suits best the actual request dynamics, as soon as possible. We also introduce a compact management parameter for the cloud-tenant application provider in order to easily set their sweet spot in the resource over-provisioning vs. SLA violation trade-off. The multi-forecast scaling engine and the proposed management parameter are evaluated both in simulations and with measurements on our collected web traces to show the improved quality of fitting provisioned resources to service demand.We find that with just a few competing forecast methods, our auto-scaling engine, implemented in Kubernetes, results in significantly less lost requests with slightly more provisioned resources compared to the default baseline.
Keywords: cloud computing | artificial intelligence | autoscaling | Kubernetes | forecast | resource management