Call for Manuscripts:
Special Issue on Fault Tolerance in Cloud/Edge/Fog Computing in Future Internet, an international peer-reviewed open access monthly journal published by MDPI.
Informational Flyer
https://blogs.mtu.edu/icc/files/2021/04/ali-ebnenasir-call-for-papers-032521-sm.pdf
Deadline
April 20, 2021
Author Notification
June 10, 2021
Website
mdpi.com/journal/futureinternet/special_issues/FT_CEFC
Collection Editors
- Dr. Ali Ebnenasir, Michigan Technological University
- Dr. Sandeep S. Kulkarni, Michigan State University
Keywords
- Fault tolerance
- Cloud computing
- Edge computing
- Resource-constrained devices
- Distributed protocols
- State replication
Topics
Including, but not limited to:
- Faults and failures in cloud and edge computing.
- State replication on edge devices under the scarcity of resources.
- Fault tolerance mechanism on the edge and in the cloud.
- Models for the predication of service latency and costs in distributed fault-tolerant protocols on the edge and in the cloud.
- Fault-tolerant distributed protocols for resource management of edge devices.
- Fault-tolerant edge/cloud computing.
- Fault-tolerant computing on low-end devices.
- Load balancing (on the edge and in the cloud) in the presence of failures.
- Fault-tolerant data intensive applications on the edge and the cloud.
- Metrics and benchmarks for the evaluation of fault tolerance mechanisms in cloud/edge computing.
Background
The Internet of Things (IoT) has brought a new era of computing that permeates in almost every aspect of our lives. Low-end IoT devices (e.g., smart sensors) are almost everywhere, monitoring and controlling the private and public infrastructure (e.g., home appliances, urban transportation, water management system) of our modern life. Low-end IoT devices communicate enormous amount of data to the cloud computing centers through intermediate devices, a.k.a. edge devices, that benefit from stronger computational resources (e.g., memory, processing power).
To enhance the throughput and resiliency of such a three-tier architecture (i.e., low-end devices, edge devices and the cloud), it is desirable to perform some tasks (e.g., storing shared objects) on edge devices instead of delegating everything to the cloud. Moreover, any sort of failure in this three-tier architecture would undermine the quality of service and the reliability of services provided to the end users.
Scope
Theoretical and experimental methods that incorporate fault tolerance in cloud and edge computing, which have the potential to improve the overall robustness of services in three-tier architectures.
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website (https://www.mdpi.com/user/login/). Once you are registered, click here to go to the submission form (https://susy.mdpi.com/user/manuscripts/upload/?journal=futureinternet).
Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page.
Please visit the Instructions for Authors page before submitting a manuscript.
The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English.
Authors may use MDPI’s English editing service prior to publication or during author revisions.
eLife, a prestigious journal in cell biology, has published a paper co-written by Sangyoon Han, “Pre-complexation of talin and vinculin without tension is required for efficient nascent adhesion maturation.”
Dr. Han is an assistant professor in the Biomedical Engineering department, and a member of the Data Sciences research group of the Institute of Computing and Cybersystems (ICC).
eLife is a non-profit organization created by funders and led by researchers. Their mission is to accelerate discovery by operating a platform for research communication that encourages and recognizes the most responsible behaviors.
A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.
The paper discusses the implementation of deep learning for medical hyperspectral imaging.
Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.
Abstract
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.
This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.
DOI: 10.1109/ACCESS.2021.3068392
IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.
A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, has been published in the April 2021 issue of ISPRS Journal of Photogrammetry and Remote Sensing, published by Science Direct.
The title of the paper is, “Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning.”
Paheding is a member of the Institute of Computer and Cybersystems’s (ICC) Center for Data Sciences.
Assistant Professor Sidike Paheding, Applied Computing, has been awarded a one-year MSGC Research Seed Grant for his project, “Monitoring Martian landslides using deep learning and data fusion.”
Professor Thomas Oommen, Geological and Mining Engineering and Sciences, is Co-PI of the project. The grant will support part-time employment of two students during the award period.
This grant is supported in part by funding provided by the National Aeronautics and Space Administration (NASA), under award number 80NSSC20M0124, Michigan Space Grant Consortium (MSGC).
The MSGC Research Seed Grant Program supports junior faculty and research scientists at MSGC affiliate institutions. The program also helps mid-career and senior faculty develop new research programs. The objective of this program is to allow award recipients to develop the research expertise necessary to propose research activities in new areas to other federal or nonfederal sources.
Assistant Professor Weihua Zhou, Applied Computing, will present a Chemistry department seminar on Friday, February 12, 2021, from 3:00 to 4:00 p.m. He will present ” Artificial intelligence for medical image analysis: our approaches.”
A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.
The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.
Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).
Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.
Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.
Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.
MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.
Paper Abstract
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.
This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.
Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.
Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.
Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.
Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.
Article Abstract
In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.
APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.
Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.
Thomas Oommen, Timothy C. Havens, Guy Meadows (GLRC), and Himanshu Grover (U. Washington) have been awarded funding in the NSF Civic Innovation Challenge for their project, “Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation.”
The six-month project award is $49,999.
Vision. The vision of the new project is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is transferable to rural communities.
Objective. The objective of the Phase-1 project is to bring together community-university partners to understand the data gaps in addressing flooding and coastal disaster in three Northern Michigan counties.
The Researchers
Thomas Oommen is a professor in the Geological and Mining Engineering and Sciences department. His research efforts focus on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types. Oommen is a member of the ICC’s Center for Data Sciences.
Tim Havens is associate dean for research, College of Computing, the
William and Gloria Jackson Associate Professor of Computer Systems, and director of the Institute of Computing and Cybersystems. His research interests include mobile robotics, explosive hazard detection, heterogeneous and big data, fuzzy sets, sensor networks, and data fusion. Havens is a member of the ICC’s Center for Data Sciences.
Guy Meadows is director of the Marine Engineering Laboratory (Great Lakes Research Center), the Robbins Professor of Sustainable Marine Engineering, and a research professor in the Mechanical Engineering-Engineering Mechanics department. His research interests include large scale field experimentation in the Inland Seas of the Great Lakes and coastal oceans; nearshore hydrodynamics and prediction; autonomous and semi-autonomous environmental monitoring platforms (surface and sub-surface); underwater acoustic remote sensing; and marine engineering.
Himanshu Grover is an asssistant professor at University of Washington. His research focus is at the intersection of land use planning, community resilience, and climate change.
About the Civic Innovation Challenge
The NSF Civic Innovation Challenge is a research and action competition that aims to fund ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities.