Call for Papers -- Edge Computing for Real Time Data Analytics in the Internet of Things

2024-08-26

The Internet of Things, or IoT, is becoming more and more well known. The Internet of Things generates enormous amounts of data. Through data analytics, information records offer essential data that might be very helpful for Internet of Things systems. IoT services, including smart transportation, ecological tracking, and intelligent medical care, differ from common uses in that they have additional needs and include flexibility, real-time reaction, and geographic intelligence. These technologies have been made possible by the development of virtualization techniques, which offer software, systems, and platforms as services. Network response time is, nevertheless, a significant barrier to instantaneous cloud-based Internet of Things applications. Here, a revolutionary technique integrates real-time data analytics enabled by the cloud into edge computing solutions. However, the needs of these applications are too great for conventional computer techniques to handle. Unfortunately, because of its centralised computation and remote location behind the device, the typical cloud computing model is unable to meet these expectations.

Anything could potentially be integrated with an Internet environment according to the idea of the Internet of Things (IoT).  Several intelligent services and apps have been developed using IoT to improve culture, businesses, and interactions with customers. Modern cloud supported edge-data analytics systems, irrespective of the source of the information, predominantly employ a strict collaborative methodology for real-time data analytics approaches. Conventionally, data are created at the infrastructure's edge and sent to the internet, where conventional real-time data analytics methods are implemented. In order to style, create, and run real-time data analytics software, developers are now compelled to use ad hoc technologies that are especially made for the network that is accessible. To tackle these challenges, edge computing solutions were developed. As consequence, edge computing was developed to handle and save data near the boundary of systems, where it is more profitable and location-aware due to its greater proximity to information sources than the internet. Regretfully, when edge computing is used for data analytics, it poses additional cybersecurity and protection risks.

In this special issue, we discuss the idea and characteristics of edge computing and then analyse possible privacy risks in edge computing that suggest specific needs to ensure its reliable data analytics. In accordance with the suggested needs, we will also provide a thorough analysis of the benefits and drawbacks of the preceding explorations of data analytics in edge computing. Additionally, we highlighted outstanding topics and suggested prospective exploration areas according to the evaluation of the data.

Topics for this special issue include the following:

  • Data analytics on the Global Internet of Things using collaborative edge computing
  • An edge computing technology for cloud-based real-time data analytics
  • Real-time, dependable edge computing for the web of Things with reactive data development
  • Smart networks powered by sophisticated edge computing and based on the Internet of Things
  • Assembling an edge computing system for predictive data analytics in real time
  • Digital data centre sophisticated operational monitoring using an edge computing technology
  • Pervasive edge computing for real-time analytics utilising intelligent data
  • Industrial-scale intelligent context development via edge computing and network IoT
  • Recent developments in intelligent for devices powered by edge computing
  • Edge computing: Interpreting sensitive data via the digital web of things
  • A simulation-based, edge computing-based approach to IoT data processing
  • Development and installation of an edge computing based video data analysis system

 

Guest Editors:
Dr. Uzair Aslam Bhatti, School of Information and Communication Engineering, Hainan University, Haikou, China
Dr. Muhammad Asim Saleem, Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
Dr. Maqbool Khan, Pak-Austria Fachhochschule - Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan
Dr. Sibghat Ullah Bazai, Department of Computer Engineering, Balochistan University of Information Technology Engineering and Management Sciences, Quetta, Pakistan
Dr. Yonis Gulzar, Department of Management Information Systems, King Faisal University, Al-Ahsa, Saudi Arabia

 

Tentative Timeline:
Submission deadline: February 10, 2025
Author notification: April 30, 2025
Revised papers due: Jun 25, 2025
Final notification: July 30, 2025
Publication: As per the policy of journal