Call for papers, Data generation in IoT
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
Deadline
23rd February 2024
How to Submit
Open call.
Editor
Dr. Mohammed I Younis, mhd.issamyounis@gmail.com
Dr. Abdul Rahman A. Alsewari
Dr. Kamal Zuhairi bin Zamli