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ردیف | عنوان | نوع |
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1 |
The “Cyber Security via Determinism” Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT)
پارادایم «امنیت سایبری از طریق جبرگرایی» برای اینترنت اشیا قطعی (IoT) ایمن صفر کوانتومی-2022 The next-generation Internet of Things (IoT) will control the critical infrastructure of the 21st
century, including the Smart Power Grid and Smart Cities. It will also support Deterministic Communications, where ‘deterministic traffic flows’ (D-flows) receive strict Quality-of-Service (QoS) guarantees.
A ‘Cybersecurity via Determinism’ paradigm for the next-generation ‘Industrial and Tactile Deterministic
IoT’ is presented. A forwarding sub-layer of simple and secure ‘deterministic packet switches’ (D-switches)
is introduced into layer-3. This sub-layer supports many deterministic Software Defined Wide Area Networks
(SD-WANs), along with 3 new tools for improving cyber security: Access Control, Rate Control, and
Isolation Control. A Software Defined Networking (SDN) control-plane configures each D-switch (ie FPGA)
with multiple deterministic schedules to support D-flows. The SDN control-plane can embed millions of
isolated Deterministic Virtual Private Networks (DVPNs) into layer 3. This paradigm offers several benefits:
1) All congestion, interference, and Distributed Denial-of-Service (DDOS) attacks are removed; 2) Buffer
sizes in D-switches are reduced by 1000C times; 3) End-to-end IoT delays can be reduced to ultra-low
latencies, i.e., the speed-of-light in fiber; 4) The D-switches do not require Gigabytes of memory to store
large IP routing tables; 5) Hardware support is provided in layer 3 for the US NIST Zero Trust Architecture;
6) Packets within a DVPN can be entirely encrypted using Quantum Safe encryption, which is impervious
to attacks by Quantum Computers using existing quantum algorithms; 7) The probability of an undetected
cyberattack targeting a DVPN can be made arbitrarily small by using long Quantum Safe encryption keys;
and 8) Savings can reach $10s of Billions per year, through reduced capital, energy and operational costs.
INDEX TERMS: Cyber security | deterministic, the Internet of Things (IoT) | quantum computing, zero trust | encryption | privacy | Software Defined Networking (SDN) | industrial internet of things (IIoT) | tactile Internet of Things | FPGA | Industry 4.0 | deterministic Internet of Things. |
مقاله انگلیسی |
2 |
Resource Allocation in Time Slotted Channel Hopping (TSCH) Networks Based on Phasic Policy Gradient Reinforcement Learning
تخصیص منابع در شبکه های گام کانال با شکاف زمانی (TSCH) بر اساس یادگیری تقویت گرادیان خط مشی فازی-2022 The concept of the Industrial Internet of Things (IIoT) is gaining prominence due to its lowcost solutions and improved productivity of manufacturing processes. To address the ultra-high
reliability and ultra-low power communication requirements of IIoT networks, Time Slotted
Channel Hopping (TSCH) behavioral mode has been introduced in IEEE 802.15.4e standard.
Scheduling the packet transmissions in IIoT networks is a difficult task owing to the limited
resources and dynamic topology. In IEEE 802.15.4e TSCH, the design of the schedule is open
to implementation. In this paper, we propose a phasic policy gradient (PPG) based TSCH
schedule learning algorithm. We construct the utility function that accounts for the throughput,
and energy efficiency of the TSCH network. The proposed PPG based scheduling algorithm
overcomes the drawbacks of totally distributed and totally centralized deep reinforcement
learning-based scheduling algorithms by employing the actor–critic policy gradient method that
learns the scheduling algorithm in two phases, namely policy phase and auxiliary phase. In
this method, we show that the schedule converges quickly compared to any other actor–critic
method and also improves the system throughput performance by 58% compared to the minimal
scheduling function, a default TSCH schedule.
Keywords: Industrial internet of things | IEEE 802.15.4e | Time slotted channel hopping | Deep reinforcement learning | Actor–critic policy gradient methods | Phasic policy gradient |
مقاله انگلیسی |
3 |
Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستمهای تولید هوشمند با استفاده از تجزیه و تحلیل پیشبینیکننده-2022 Smart manufacturing systems (SMS) are one of the most important applications in the Industry
4.0 era, offering numerous advantages over traditional production systems and rapidly being
used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT),
Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more.
SMS is an innovative and popular manufacturing setup that produces increasingly intelligent
production systems; yet, designers must adapt to business tastes and requirements. This study
employs an analytical and descriptive research technique to identify and assess functional and
non-functional, technological, economic, social, and performance evaluation components that
are essential to SMS evaluation. A predictive analytics framework, which is a key component
of many decision support systems, is used to assess corporate needs as well as proposed and
prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system |
مقاله انگلیسی |
4 |
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 |
مقاله انگلیسی |
5 |
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 |
مقاله انگلیسی |
6 |
Data management techniques for Internet of Things
تکنیک های مدیریت داده برای اینترنت اشیاء-2020 Internet of Things (IoT) is a network paradigm in which physical, digital, and virtual objects
are equipped with identification, detection, networking, and processing functions to communicate
with each other and with other devices and services on the Internet in order to
perform the users’ required tasks. Many IoT applications are provided to bring comfort and
facilitate the human life. In addition, the application of IoT technologies in the automotive
industry has given rise to the concept of Industrial Internet of Things (IIoT) which facilitated
using of Cyber Physic Systems, in which machines and humans interact. Due to the
diversity, heterogeneity, and large volume of data generated by these entities, the use of
traditional database management systems is not suitable in general. In the design of IoT
data management systems, many distinctive principles should be considered. These different
principles allowed the proposal of several approaches for IoT data management. Some
middleware or architecture-oriented solutions facilitate the integration of generated data.
Other available solutions provide efficient storage and indexing structured and unstructured
data as well as the support to the NoSQL language. Thus, this paper identifies the
most relevant concepts of data management in IoT, surveys the current solutions proposed
for IoT data management, discusses the most promising solutions, and identifies relevant
open research issues on the topic providing guidelines for further contributions. Keywords: Data management | Internet of Things | IoT applications | Big data | Industrial Internet of Things |
مقاله انگلیسی |
7 |
PROGRAMS project approach to maintenance management
رویکرد پروژه PROGRAMS برای مدیریت تعمیر و نگهداری-2020 Maintenance management is a vital part of the business of a production company. It contributes to determining the long-term success of the company because poorly maintained resources can stop production activities, causing delays, loss of profit and even personal injuries. While the predictive maintenance approach is nowadays a mainstay of modern factory management, usually only big companies with dedicated research department can deploy it, since its application involves scientific knowledge that is not available in smaller production environments. This paper describes how the EU project PROGRAMS answers the needs of small and medium companies that wish to apply an Industrial Internet of Things (IIoT) approach to maintenance management.
Keywords: Maintenance engineering | Preventive maintenance | Decision support systems | Optimization | problems | Sensors systems. |
مقاله انگلیسی |
8 |
Industry 4:0 based process data analytics platform: A waste-to-energy plant case study
پلت فرم تجزیه و تحلیل داده های مبتنی بر فرآیند صنعت 4:0: مطالعه موردی از گیاهان زباله به انرژی-2020 Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions
driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the
several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning
(ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although,
this is an area of increased interest, the information concerning the implementation of data analytics in the
context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data
analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms,
ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process
data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial
grade cloud computing platforms. The industrial applicability of the platform was demonstrated by the
development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied datadriven
soft sensors to predict syngas heating value and hot flue gas temperature. Among the studied data-driven
methods, the neural network-based NARX model demonstrated better performance in the prediction of both
syngas heating value and flue gas temperature. The modeling results showed that, in cases where process
knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive
data analytics. Keywords: Data analytics platform | Industrial internet of things platform | Machine learning | Waste-to-energy | Soft sensor |
مقاله انگلیسی |
9 |
Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry
سیستم انتقال برق بی سیم سبز برای ناوگان هواپیماهای بدون سرنشین که با یادگیری تقویتی در صنعت هوشمند مدیریت می شود-2020 The optimal management of a fleet of drones is proposed in this paper for providing connectivity to sensors and
actuators in Industrial Internet of Things (IIoT) scenarios. The persistent mission without any human intervention
on the battery charge is obtained by means of an on-field wind generator supplying a charge station that
adopts resonant wireless power transfer. The objective of the fleet management is to provide the best connectivity
over the time considering the variability of both the bandwidth request and the wind energy availability.
The optimal management is performed by a system controller adopting reinforcement learning (RL) for
deciding the number of drones to take off and, consequently, the instantaneous provided bandwidth. A constant
charge time of drone battery represents a key element of the system because this enables to strongly reduce the
complexity of the system controller task. To this purpose, an adaptive current control for the charge station is
introduced to compensate charge time variabilities due to the coupling factor changes caused by misalignments
that can occur between a pad and a drone. The results have highlighted that the RL provides good performance
improvement in case of green generation. An important aspect arose from this study is the ability of RL to
increase the saved energy even if it is not considered as a target of the controller. Keywords: Artificial intelligence | Drone | Industry 4.0 | Internet of Things | Wind generator | Wireless power transfer |
مقاله انگلیسی |
10 |
High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things
مدلسازی و تلفیق سطح بالای شبکه های حسگر هوشمند در اینترنت اشیا صنعتی -2017 In this work, we use a high-level design methodology for the rapid hardware synthesis of
a complex smart sensor network (SSN) system. The GRAFCET is then used to model the
individual functional modules and the hierarchical behavior of the system. The behavior
of each module is represented as a sequential–concurrent hybrid discrete event system.
We apply high-level synthesis rules to generate a VHSIC hardware description language
(VHDL)-target efficient hardware for a smart sensor controller and smart gateway con
troller. Finally, these embedded hardware controllers are generated automatically to inte
grate all intelligent functional modules into a complex embedded system, and a hardware
circuit is then synthesized. The experimental results show that the hardware circuit can
meet the definition of an SSN system for Industrial Internet of Things applications. More
over, this methodology enables a coherent design quality, short design period, low devel
opment cost, and short time-to-market for complex industrial applications.
Keywords: Discrete-event modeling | High-level synthesis | Industrial Internet of Things (IIoT) | Smart sensor network |
مقاله انگلیسی |