ReTMiC: Reliability-Aware Thermal Management in Multicore Mixed-Criticality Embedded Systems
As the number of cores in multicore platforms increases, temperature constraints may prevent powering all cores simultaneously at maximum voltage and frequency level. Thermal hot spots and unbalanced temperatures between the processing cores may degrade the reliability. This paper introduces a reliability-aware thermal management scheduling (ReTMiC) method for mixed-criticality embedded systems. In this regard, ReTMiC meets Thermal Design Power as the chip-level power constraint at design time. In order to balance the temperature of the processing cores, our proposed method determines balancing points on each frame of the scheduling, and at run time, our proposed lightweight online re-mapping technique is activated at each determined balancing point for balancing the temperature of the processing cores. The online mechanism exploits the proposed temperature-aware factor to reduce the system’s temperature based on the current temperature of processing cores and the behavior of their corresponding running tasks. Our experimental results show that the ReTMiC method achieves up to 12.8°C reduction in the chip temperature and 3.5°C reduction in spatial thermal variation in comparison to the state-of-the-art techniques while keeping the system reliability at a required level.
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AD-VILS: Implementation and Reliability Validation of Vehicle-in-the-Loop Simulation Platform for Evaluating Autonomous Driving Systems
Vehicle-in-the-loop simulation (VILS) is a vehicle-testing technique that integrates high-fidelity simulation environments with real-world vehicles. Among existing simulation approaches for evaluating autonomous driving systems (ADS), VILS is particularly noteworthy because it faithfully reflects the dynamic characteristics of real-world vehicles and ensures repeatable and reproducible testing in diverse virtual scenarios. While researchers strive to implement a VILS platform that closely approximates real-world vehicle-testing environments, the performance of vehicles in VILS testing may differ from that observed in real-world testing, depending on the platform’s reliability. Therefore, methods must be established to validate the reliability of VILS platforms. Herein, we present the essential components of a VILS platform for evaluating ADS (AD-VILS) and propose a metho dology to validate the reliability of the implemented AD-VILS platform. This methodology includes scenario definition, techniques for VILS testing and real-world vehicle testing, and procedures for evaluating consistency and correlation based on statistical and mathematical comparisons between the datasets from virtual and real-world tests. Moreover, we empirically derive reliability evaluation criteria through iterative testing. This methodology aims to enhance the precision and reliability of ADS evaluations conducted on AD-VILS platforms.
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A Distributed Framework for Minimizing the Asymmetrical Power Request in Multi-Agent Microgrids With Unbalanced Integration of DERs
Microgrids (MGs) are initiated in power systems to speed up the integration of the independently operated distributed energy resources (DERs) into the network. In this regard, in multi-agent microgrids (MAMGs), independent agents aim to operate their resources, while the MG operator (MGO) coordinates independent agents to address the operational issues and ensures reliability of the system. In an MAMG, the high integration of single-phase DERs as well as their independent operational scheduling could result in the asymmetrical power flow in the upper-level system. Respectively, addressing the asymmetrical power request of the MAMGs by exploiting the scheduling of DERs seems to be essential due to the limited flexibility capacity in the upper-level power network, which would finally improve the operating condition of the power system. Consequently, this paper aims to develop a transactive-based scheme to minimize the conceived asymmetrical operation of MAMGs. Accordingly, MGO employs transactive energy signals to minimize the asymmetrical power request of the MAMG by exploiting the scheduling of DERs, while ensuring the privacy of independent agents. Eventually, the proposed framework is applied on an MAMG test system to study its efficacy in alleviating the asymmetrical power request from the upper-level system.
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Interference-Aware Intelligent Scheduling for Virtualized Private 5G Networks
Private Fifth Generation (5G) Networks can quickly scale coverage and capacity for diverse industry verticals by using the standardized 3rd Generation Partnership Project (3GPP) and Open Radio Access Network (O-RAN) interfaces that enable disaggregation, network function virtualization, and hardware accelerators. These private network architectures often rely on multi-cell deployments to meet the stringent reliability and latency requirements of industrial applications. One of the main challenges in these dense multi-cell deployments is the interference to/from adjacent cells, which causes packet errors due to the rapid variations from air-interface transmissions. One approach towards this problem would be to use conservative modulation and coding schemes (MCS) for enhanced reliability, but it would reduce spectral efficiency and network capacity. To unlock the utilization of higher efficiency schemes, in this paper, we present our proposed machine-learning (ML) based interference prediction technique that exploits channel state information (CSI) reported by 5G User Equipments (UEs). This method is integrated into an in-house developed Next Generation RAN (NG-RAN) research platform, enabling it to schedule transmissions over the dynamic air-interface in an intelligent way. By achieving higher spectral efficiency and reducing latency with fewer retransmissions, this allows the network to serve more devices efficiently for demanding use cases such as mission critical Internet-of-Things (IoT) and extended reality applications. In this work, we also demonstrate our over-the-air (OTA) testbed with 8 cells and 16 5G UEs in an Industrial IoT (IIoT) Factory Automation layout, where 5G UEs are connected to various industrial components like automatic guided vehicles (AGVs), supply units, robotics arms, cameras, etc. Our experimental results show that our proposed Interference-aware Intelligent Scheduling (IAIS) method can achieve up to 39% and 70% throughput gains in low and high interference scenarios, respectively, compared to a widely adopted link-adaptation scheduling approach.
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A Simple Sum of Products Formula to Compute the Reliability of the KooN System
Reliability block diagram (RBD) is a well-known, high-level abstract modeling method for calculating systems reliability. Increasing redundancy is the most important way for increasing Fault-tolerance and reliability of dependable systems. K-out-of-N (KooN) is one of the known redundancy models. The redundancy causes repeated events and increases the complexity of the computing system’s reliability, and researchers use techniques like factorization to overcome it. Current methods lead to the cumbersome formula that needs a lot of simplification to change in the form of Sum of the Products (SoP) in terms of reliabilities of its constituting components. In This paper, a technique for extracting simple formula for calculating the KooN system’s reliability in SoP form using the Venn diagram is presented. Then, the shortcoming of using the Venn diagram that is masking some joints events in the case of a large number of independent components is explained. We proposed the replacement of Lattice instead of Venn diagrams to overcome this weakness. Then, the Lattice of reliabilities that is dual of power set Lattice of components is introduced. Using the basic properties of Lattice of reliabilities and their inclusion relationships, we propose an algorithm for driving a general formula of the KooN system’s reliability in SoP form. The proposed algorithm gives the SoP formula coefficients by computing elements of the main diagonal and elements below it in a squared matrix. The computational and space complexity of the proposed algorithm is θ ((n – k) 2 /2) that n is the number of different components and k denotes the number of functioning components. A lemma and a theorem are defined and proved as a basis of the proposed general formula for computing coefficients of the SoP formula of the KooN system. Computational and space complexity of computing all of the coefficients of reliability formula of KooN system using this formula reduced to $\theta (n-k)$ . The proposed formula is simple and is in the form of SoP, and its computation is less error-prone.
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Reliability in Sensor-Cloud Systems and Applications (SCSA)
Submission Deadline: 28 February 2021
IEEE Access invites manuscript submissions in the area of Reliability in Sensor-Cloud Systems and Applications (SCSA).
The sensor-cloud system (SCS) integrates sensors, sensor networks, and cloud for managing sensors, collecting data, and decision-making. The integration provides flexible, low-cost, and reconfigurable platforms for monitoring and controlling the applications, including emerging applications of the Internet of things (IoT), machine to machine, and cyber-physical systems. Though the SCS has received tremendous attention from both academia and industry because of its numerous exciting applications, it suffers from issues with reliability.
The reliability of the SCS is the ability to perform required functions at or below a given failure rate for a given period of time. The required functions are identified as correctly providing measurement results. Sensor data can be faulty due to system faults or security attacks during the sensor data acquisition and collection, and data processing/decision-making on the cloud. There is also the possibility of not receiving the data at all or received data is compromised. Sensor data is also susceptible to errors and interference such as noise. Furthermore, to save sensor resources, end-users are becoming more dependent on cloud processing and decision-making. This enables significant interactions between sensors and between sensors and the cloud. As a result, the required functions can be extended to cover more concerns, including system design and integration, system continuity, real-time responsiveness, data security, and prevention of privacy intrusions, etc. This is why practitioners and engineers must also be able to measure imprecision and true reliability for both the data and the system that deals with the data. When designing and developing the SCS for real applications, the reliability verification at any level is required.
This Special Section aims to collect and publish state-of-art research results to solve the reliability concerns of sensor-cloud systems.
The topics of interest include, but are not limited to:
- Reliability theory, standardization, modeling, and architecture for SCSA
- Reliability in sensor design and integration in SCSA
- Reliability in communication and networking in SCSA
- Reliability in sensor data privacy processing, computing, and control in SCSA
- Reliability in applying machine learning models and methods for SCSA
- Reliability through data security, privacy, and trust for SCSA
- Reliability concerns in monitoring diverse applications of SCSA
- Reliability through QoS in application monitoring in SCSA
- Reliability issues (e.g., cloud storage, computing, decision-making) in SCSA
- Reliability in fog and edge computing in SCSA
- Reliability in mobile sensing, offloading and tracking in SCSA
- Reliability assessment, measures, and testbeds for SCSA
- Reliability levels and relations, metrics, and measures for SCSA
- Data quality assurance and quality control in SCSA
- Effective network design and development of SCSA
- Smartphone-based and heterogeneous design of SCSA
- Reliability through fault-tolerance and reliability in SCSA
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.
Associate Editor: Md Zakirul Alam Bhuiyan, Fordham University, USA
Guest Editors:
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- Arafatur Rahman, University Malaysia Pahang, Malaysia
- Liu Qin, Hunan University, China
- Xuyun Zhang, Macquarie University, Australia
- Taufiq Asyhari, Coventry University, UK
Relevant IEEE Access Special Sections:
- Additive Manufacturing Security
- Advanced Sensor Technologies on Water Monitoring and Modeling
- The convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: zakirulalam@gmail.com.
Uncertainty Quantification in Robotic Applications
Submission Deadline: 01 May 2020
IEEE Access invites manuscript submissions in the area of Uncertainty Quantification in Robotic Applications.
Uncertainty in engineering systems comes from a variety of sources such as manufacturing imprecision, assembly errors, model variation and stochastic operating conditions. Hence the actual performance of an engineering system may deviate from the design target, resulting in a quality loss, customer dissatisfaction, or even catastrophic failures. To ensure robust and reliable system operations, it is imperative to quantify and reduce the uncertainty effects during the system design, manufacturing and field operation.
For the robotic systems, the dynamic and highly nonlinear performance is significantly affected by operating conditions, time-varying load, and other random stresses. This creates new challenges in measuring and characterizing the performance uncertainty with dynamic performance. Uncertainty quantification methods, such as reliability modeling, reliability analysis, reliability-based design optimization, model validation, sensitivity analysis, and robust design are deemed essential in improving the reliability of robotic systems.
This Special Section in IEEE Access invites academic scholars and industry practitioners to submit full-length articles that report the recent advances in theoretical, numerical, and experimental development in uncertainty quantification. The articles are expected to describe original findings or innovative concepts that address different aspects of uncertainty quantification challenges arising for robotic systems. New uncertainty quantification methods are anticipated to address the safety, reliability and quality issues of emerging robotic technologies, and thus move the robotic industry forward.
The topics of interest include, but are not limited to:
- Reliability modeling, analysis and design optimization of robotic systems
- Model verification and validation of robotic systems
- Sensitivity analysis of robotic systems
- Robust design of robotic systems
- Performance reconstruction under uncertainty of robotic systems
- Big data and machine learning in robotic systems
- Internet of Things for robotic systems under uncertainty
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Zhonglai Wang, University of Electronic Science and Technology of China, China
Guest Editors:
- Tongdan Jin, Texas State University, USA
- Om Prakash Yadav, North Dakota State University, USA
- Ningcong Xiao, University of Electronic Science and Technology of China, China
- Yi (Leo) Chen, Glasgow Caledonian University, UK
Relevant IEEE Access Special Sections:
- Advances in Prognostics and System Health Management
- Additive Manufacturing Security
- Artificial Intelligence in Cyber Security
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: wzhonglai@uestc.edu.cn.
Security and Privacy in Emerging Decentralized Communication Environments
Submission Deadline: 30 September 2019
IEEE Access invites manuscript submissions in the area of Security and Privacy in Emerging Decentralized Communication Environments.
Modern, decentralized digital communication environments are changing with the availability of new technologies, and the development of new, real-world applications, which lead to novel challenges in security, such as: 5G/6G mobile applications, smart Internet of Things (IoT) devices, big data applications, and cloud systems. Mobile – cloud architecture is emerging as 5G /6G mobile IoT devices are generating large volumes of data, which need cloud infrastructure to process. Many IoT systems and cloud systems are decentralized and Blockchain is emerging in decentralized networks. The increasing interdependence of IT solutions accepted by society has led to a sharp increase in data. As a result, chances of data leakage or privacy infringement also increase, along with the need for new solutions for digital security and privacy protection.
This Special Section in IEEE Access aims to report highlighted security and privacy research in modern, decentralized digital communication environments. The Special Section invites experts and scholars in the fields of digital security, so that readers can keep abreast of the latest developments in the industry, and master the latest security technologies. The Special Section will support industry researchers working with emerging decentralized communication environments to solve real-world security problems. This Special Section will focus on relevant emerging digital security and privacy protection solutions.
The topics of interest include, but are not limited to:
- Security and privacy in 5G /6G mobile / wireless networks
- Security and privacy in the smart mobile Internet of Things
- Security and privacy in Blockchain based decentralized networks
- Security and privacy in 5G vehicular network
- Security and privacy in 5G device to device communications
- Security and privacy for big data in cloud applications
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Xiaochun Cheng, Middlesex University, UK
Guest Editors:
- Zheli Liu, Nankai University, China
- James Xiaojiang Du, Temple University, USA
- Shui Yu, University of Technology Sydney, Australia
- Leonardo Mostarda, Università di Camerino, Italy
Relevant IEEE Access Special Sections:
- Advances in Prognostics and System Health Management
- Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
- D2D Communications: Security Issues and Resource Allocation
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: X.Cheng@mdx.ac.uk.
Artificial Intelligence in CyberSecurity
Submission Deadline: 30 July 2019
IEEE Access invites manuscript submissions in the area of Artificial Intelligence in CyberSecurity.
Recent studies show that Artificial Intelligence (AI) has resulted in advances in many scientific and technological fields, i.e., AI-based medicine, AI-based transportation, and AI-based finance. It can be imagined that the era of AI will be coming to us soon. The Internet has become the largest man-made system in human history, which has a great impact on people’s daily life and work. Security is one of the most significant concerns in the development of a sustainable, resilient and prosperous Internet ecosystem. Cyber security faces many challenging issues, such as intrusion detection, privacy protection, proactive defense, anomalous behaviors, advanced threat detection and so on. What’s more, many threat variations emerge and spread continuously. Therefore, AI-assisted, self-adaptable approaches are expected to deal with these security issues. Joint consideration of the interweaving nature between AI and cyber security is a key factor for driving future secure Internet.
The use of AI in cybersecurity creates new frontiers for security research. Specifically, the AI analytic tools, i.e., reinforcement learning, big data, machine learning and game theory, make learning increasingly important for real-time analysis and decision making for quick reactions to security attacks. On the other hand, AI technology itself also brings some security issues that need to be solved. For example, data mining and machine learning create a wealth of privacy issues due to the abundance and accessibility of data. AI-based cyber security has a great impact on different industrial applications if applied in appropriate ways, such as self-driving security, secure vehicular networks, industrial control security, smart grid security, etc. This Special Section in IEEE Access will focus on AI technologies in cybersecurity and related issues. We also welcome research on AI-related theory analysis for security and privacy.
The topics of interest include, but are not limited to:
- Reinforcement learning for cybersecurity
- Machine learning for proactive defense
- Big data analytics for security
- Big data anonymization
- Big data-based hacking incident forecasting
- Big data analytics for secure network management
- AI-based intrusion detection and prevention
- AI approaches to trust and reputation
- AI-based anomalous behavior detection
- AI-based privacy protection
- AI for self-driving security
- AI for IoT security
- AI for industrial control security
- AI for smart grid security
- AI for security in innovative networking
- AI security applications
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Chi-Yuan Chen, National Ilan University, Taiwan
Guest Editors:
- Wei Quan, Beijing Jiaotong University, China
- Nan Cheng, University of Toronto, Canada
- Shui Yu, Deakin University, Australia
- Jong-Hyouk Lee, Sangmyung University, Republic of Korea
- Gregorio Martinez Perez, University of Murcia (UMU), Spain
- Hongke Zhang, Beijing Jiaotong University, China
- Shiuhpyng Shieh, National Chiao Tung University, Taiwan
Relevant IEEE Access Special Sections:
- Artificial Intelligence and Cognitive Computing for Communications and Networks
- Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
- Cyber-Physical Systems
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: chiyuan.chen@ieee.org.
Additive Manufacturing Security
Submission Deadline: 30 April 2020
IEEE Access invites manuscript submissions in the area of Additive Manufacturing Security.
Additive Manufacturing (AM), a.k.a. 3D Printing, is a rapidly growing multi-billion dollar industry. This technology is being used to manufacture 3D objects for a broad range of application scenarios such as prototypes in R&D and functional parts in safety critical systems. The benefits of this technology include shorter design-to-product time, just-in-time and on-demand production, and close proximity to assembly lines. Furthermore, AM can produce functional parts with complex internal structures and optimized physical properties with less material waste than subtractive manufacturing.
Due to the numerous technical and economic advantages that this technology promises, AM is expected to become a dominant manufacturing technology in both industrial and home settings. The need to secure physical and cyber-physical systems gives rise to a corresponding need to understand potential attacks via AM systems, and to develop countermeasures that will enable attack prevention, detection, and digital investigation. So far, three major security threat categories have been identified for AM: theft of technical data (or violation of intellectual property, IP), sabotage of AM, and manufacturing of illegal objects. AM Security is a fairly new and highly multi-disciplinary field of research that addresses these threats.
The aim of this Special Section in IEEE Access is to discuss recent advances in AM Security, addressing both offensive and defensive approaches.
The topics of interest include, but are not limited to:
- Compromise of AM systems and environment
- Security threats and attacks in AM context: Theft of technical data (or violation of intellectual property), sabotage of AM, manufacturing of illegal objects
- Technical approaches to detect attacks on/with AM
- Technical approaches to prevent attacks on/with AM
- Digital Investigation and [digital] forensics in the AM context
- Legal aspects of attacks on/with AM
- Economic incentives for AM Security
- Socioeconomic implications of attacks on/with AM
- Comparative analysis of AM vs. CPS/IoT/Industry 4.0/… Securities
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Mark Yampolskiy, Auburn University, USA
Guest Editors:
- Mohammad Al Faruque, University of California Irvine, USA
- Raheem Beyah, Georgia Tech, USA
- William Frazier, Naval Air Systems, USA
- Wayne King, The Barnes Group Advisors, USA
- Yuval Elovici, Ben-Gurion University of the Negev, Israel
- Anthony Skjellum, University of Tennessee at Chattanooga, USA
- Joshua Lubell, National Institute of Standards and Technology (NIST), USA
- Celia Paulsen, National Institute of Standards and Technology (NIST), USA
Relevant IEEE Access Special Sections:
- Cyber-Physical Systems
- Collaboration for Internet of Things
- Towards Service-Centric Internet of Things (IoT): From Modeling to Practice
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access
For inquiries regarding this Special Section, please contact: yampolskiy@southalabama.edu.
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