Engineering Commons^™

Fog Computing Enabling Industrial Internet Of Things: State-Of-The-Art And Research Challenges, Rabeea Basir, Saad Qaisar, Mudassar Ali, Monther Aldwairi, Muhammad Ikram Ashraf, Aamir Mahmood, Mikael Gidlund

All Works

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a …

Development Of Smart Semiconductor Manufacturing: Operations Research And Data Science Perspectives, Marzieh Khakifirooz, Mahdi Fathi, Kan Wu

Bagley College of Engineering Publications and Scholarship

With advances in information and telecommunication technologies and data-enabled decision making, smart manufacturing can be an essential component of sustainable development. In the era of the smart world, semiconductor industry is one of the few global industries that are in a growth mode to smartness, due to worldwide demand. The important opportunities that can boost the cost reduction of productivity and improve quality in wafer fabrication are based on the simulations of actual environment in Cyber-Physical Space and integrate them with decentralized decision-making systems. However, this integration faced the industry with novel unique challenges. The stream of the data from …

Msg-Fast: Metagenomic Shotgun Data Fast Annotation Using Microbial Gene Catalogs, Stuart M. Brown, Hao Chen, Yuhan Hao, Bobby P. Laungani, Thahmina A. Ali, Changsu Dong, Carlos Lijeron, Baekdoo Kim, Claudia Wultsch, Zhiheng Pei, Konstantinos Krampis

Publications and Research

Background: Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.

Results: We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this …