Automated Software Engineering - Call for papers: Special Issue on Exploring software engineering practices for efficient data generation in IoT systems
Guest Editors:
Dr. Mohammed I Younis
Assistant Professor,
Department of Computer Engineering,
College of Engineering, University of Baghdad,
Al Jadriya 10070, Iraq
Email: younismi@coeng.uobaghdad.edu.iq (this opens in a new tab)
Dr. Abdul Rahman A. Alsewari
Senior Lecturer,
School of Computing and Digital Technology,
Birmingham City University,
Birmingham B4 7XG, United Kingdom
Email: rahman.alsewari@bcu.ac.uk (this opens in a new tab)
Dr. Kamal Zuhairi bin Zamli
Faculty of Computing
Universiti Malaysia Pahang
Pekan, Malaysia
Email: kamalz@ump.edu.my (this opens in a new tab)
In any IoT system, the generation of massive volumes of data is a common occurrence due to the interconnected nature of devices and sensors. Efficient data generation practices involve the development of software solutions that enable streamlined data collection, processing, and transmission. This involves implementing robust data acquisition mechanisms that attach to the standard of industry standards and other protocols. One of the key features of software engineering practices for efficient data generation is the use of optimized data collection methodologies. These methodologies involve employing lightweight protocols and data compression techniques to reduce the size of data transmitted across the IoT network. Minimizing the amount of data transferred can enhance network efficiency and also reduces latency by conserving resources.
Beyond efficient data collection and storage, software engineering practices encompass data processing and analytics. Through the use of scalable and distributed computing frameworks like Apache Spark or Hadoop to analyse the vast quantities of generated data promptly. In turn, this empowers us to extract real-time insights, detect anomalies and perform predictive analytics enabling data-driven decision-making and boosting system performance. As the IoT landscape continues to evolve software engineering practices need to adapt to emerging technologies and standards. Continuous research and development in this field are essential to address the evolving challenges and complexities associated with efficient data generation in IoT systems. The Special Issue aims to provide a platform to professionals working in this area for sharing knowledge and insights to advance the state of the art in this field. We welcome them to submit studies that propose novel algorithms, methodologies, frameworks, or practical approaches to optimize the data generation process in IoT systems.
List of Topics:
- Edge Computing Techniques for Efficient Data Generation in IoT Systems
- Machine Learning Approaches for Intelligent Data Generation in IoT
- Security and Privacy Considerations in Data Generation for IoT Systems
- Interoperability Challenges and Standards for Data Generation in Heterogeneous IoT Environments
- Context-Aware Data Generation Techniques in IoT Systems
- Scalability and Performance Optimization of Data Generation in IoT
- Adaptive Data Generation Approaches for Changing IoT Environments
- Data Compression and Reduction Techniques for Efficient Storage and Bandwidth Usage in IoT
- Energy-Efficient Data Generation Methods for IoT Devices
- Real-Time Data Generation and Analysis in IoT Systems
- Distributed Data Generation Architectures for Large-Scale IoT Deployments
- Quality of Service and Quality of Data in IoT Data Generation
Guest Editors for this Special issue:
Dr. Mohammed I Younis
Assistant Professor,
Department of Computer Engineering,
College of Engineering, University of Baghdad,
Al Jadriya 10070, Iraq
Email: younismi@coeng.uobaghdad.edu.iq (this opens in a new tab), mhd.issamyounis@gmail.com (this opens in a new tab)
Google Scholar Page: https://scholar.google.com/citations?user=nzSNl1sAAAAJ&hl=en (this opens in a new tab)
Dr. Abdul Rahman A. Alsewari
Senior Lecturer,
School of Computing and Digital Technology,
Birmingham City University,
Birmingham B4 7XG, United Kingdom
Email: rahman.alsewari@bcu.ac.uk (this opens in a new tab)
Official Page: https://www.bcu.ac.uk/computing/about-us/our-staff/abdulrahman-alsewari (this opens in a new tab)
Google Scholar page: https://scholar.google.com/citations?user=hLypPv8AAAAJ&hl=en (this opens in a new tab)
Dr. Kamal Zuhairi bin Zamli
Faculty of Computing
Universiti Malaysia Pahang
Pekan, Malaysia
Email: kamalz@ump.edu.my (this opens in a new tab)
Google Scholar page: https://scholar.google.com/citations?user=0i6W13cAAAAJ&hl=en (this opens in a new tab)
Manuscript Submission Timeline:
Submission Deadline of Papers: 10.20.2023
Authors Notification Date: 01.20.2024
Revised Papers Due Date: 04.01.2024
Final notification Date: 07.10.2024
Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least two independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
Before submitting, it is also recommended that you visit the following webpages to familiarize yourself with various aspects of the editor role: Springer Nature Code of Conduct (this opens in a new tab) and Springer Nature publishing and editorial policies (this opens in a new tab).