The rapid growth of artificial intelligence in the smart home and industrial automation is facilitated by creating a burst of data that were moving across the edge-cloud integrated network environment. In order to store and process the data for early prediction and analysis, all the IoT-based data need to be transferred to a nearby edge or cloud server. Moreover, the technologies like wireless networks, edge computing, fog computing, cloud computing, and machine learning become the inevitable bond inimplementing the automation process in real-world applications.An IoT-based Edge Analytics is imperative in all the smart home and industry automation due to its gained attention as an IoT model of interconnected devices. This approach could automate the computation and analytic capability across the data generated at the sensors, edge devices, routers, and switches. Moreover, it provides several convincing benefits like near real-time analysis, scalability, cost optimization, and improved security.
IoT-based Edge Analytics can be achieved through the edge and fog computing potentials that can move the computing capability closer to the nearby network edge and source of data generation. This type of edge computing can be applicable for processing and analyzing less amount of data. In the case of processing, huge data related to machine learning and deep learning like stuff require the support of an edge-cloud integrated environment. In addition, huge data processing may lead to high network bandwidth consumption, and latencies, and minimizes the quality of service (QoS). Consequently, the hybrid combination of the edge-cloud integrated platform can leverage the IoT-based edge analytics to a greater extent. The real-time smart home and industrial automation and its pervasiveness can be obtained through the improvement ofQoS parameters in the aforementioned aspects.In order to improvise the performance of a system, novel feature extraction and feature selection techniques can be applied to minimize the data transfer and processing done at the cloud level. Moreover, IoT-based edge analytics is operated to satisfy the QoS-specific service requirements that pertain to human-centric and industrial automation applications. The major aim of these applications is to improvise the user QoS through the optimization of response time, and cost factors of the edge-cloud integrated platform. So many other parameters like service capacity, latency, energy consumption, workload, reliability, security,and throughput also need to be improved for bringing a better intelligent environment.Since the IoT-based devices and their supporting components are operated from a remote environment, continuous monitoring is required with proper sensing and actuation events that are mandatory to avoid tragic situations. Moreover, it can be enabled with high intelligence and analytics by providing high QoS facilities for real-time applications. The IoT-based applications are applied in various fields like smart city establishment, healthcare, traffic control, home surveillance, agriculture monitoring, and defensethat are resulted in pioneering embedded devices with edge analytics. It will result in offering high computational facility and robust performance without much latency and delay.
Some of the popular industrial use-cases are like Healthcare Industry, Retail Industry, Automobiles, and other Manufacturing Industries. In today’s competitive world, the number of vendors and their problems is exponentially increasing day by day. When it comes to the manufacturing industry would positively need Artificial Intelligence concepts, starting from graphic examination for defects to robotic assembly controllers. So, one deploys the Artificial Intelligence capabilities at the edge level could significantly reduce the cost and improve data processing rapidly. Therefore, IoT-based Edge Analytic devices with Artificial Intelligence have grabbed buyers’ attention and becomethe most demanding technology trends in the past 2-to 3 years with innovative potential. An Azure IoT Edge is a most popular and mature platform that can function as a protocol gateway, transparent gateway, and fully-fledged edge layer with offline functionality.
This call for papers welcomes the submissions of all the original research work in the aspects of experimental and empirical analysis from the researchers, academicians, industrial professionals, and research students working across various engineering fields and innovative projects.
This Special Issue pursues original contributions in, but not limited to:
IoT-based Smart City Development
Security and trust scheme in Human-centric and Industrial Automation Applications
Fault-tolerant and reliability analysis in IoT-based Applications
Efficient data processing and clustering approaches in IoTApplications
Smart home surveillance and elderly patient monitoring using edge computing
Remote patient monitoring and diagnosis using an edge-cloud integrated platform
IoT-based traffic analysis using edge and fog computing technique
Smart agricultural monitoring using IoT and Drone.
Data-driven IoT Implementation for Industries
IoT in Logistics and Transportation
IoT Applications for Sustainable Practices in Agriculture
IoT-based Remote Monitoring in Manufacturing Industries
IoT platform for Industrial Equipment Management and Monitoring
IoT-based Predictive Maintenance in Industries
Open submission: 10 April 2022
Submission deadline: 30 September 2022
Author notification: within 4 weeks after submission
Revised manuscript due: within 2 weeks after notification
Notification of acceptance: within 2 weeks after revision submission
Tentative accepted paper publication date: within 2 months after the final version
Tentative SI paper collection and its web open: 1stQuarter, 2023 (TBA)
All submitted papers must be clearly written in excellent English and contain only original work. All papers must be submitted in an electronic format, e.g., PDF format (preferred) or MS Word. Manuscripts should follow the formatting of the sample manuscript and references. You can refer to the details in the submission menu http://hcisj.com/submission/preparing_manuscript.php
All papers and some supplementary materials should be submitted through ScholarOne Manuscripts. The authors must select “SI2022-03IoT-based Edge Analytics”. when they reach the “Article Type” step in the submission process https://mc04.manuscriptcentral.com/hcis.
Dr. Venkatachalam K [Lead Guest Editor]
University of Hradec Králové, Czech Republic
Dr. Yizhang Jiang
Dr. Senthilkumar Mohan
Vellore Institute of Technology, India
Dr. Mohamed Abouhawwash
The University of Texas at Austin, Austin, TX, USA