Ontology Driven Testing Strategies for IoT Applications

Naqvi, Muhammad Raza; Iqbal, Muhammad Waseem; Ashraf, Muhammad Usman; Ahmad, Shafiq; Soliman, Ahmed; Khurram, Shahzada; Shafiq, Muhammad; Choi, Jin-Ghoo

doi:10.32604/cmc.2022.019188

Cited by 19 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For conceptual modeling of IoT systems, issues with stereotype aspects of Object-Process Methodology (OPM) ISO 19450 are studied [56]. Naqvi et al investigated the use of Protégé to design test strategies for IoT systems using an ontological reasoning approach [57] while MATTER is introduced for model-based generation of executable test scripts for IoT systems [58]. For VOLUME 11, 2023 the purpose of effect evaluation in IoT-enabled interactive UI design, a discrete mathematical model is created using discrete Hopfield neural network and seven evaluation indicators are analyzed [59].…”

Section: A Integration With Business and Management Methodologiesmentioning

confidence: 99%

Model-Based System Engineering of the Internet of Things: A Bibliometric Literature Analysis

Ahmad

2023

IEEE Access

View full text Add to dashboard Cite

Model-based System Engineering (MBSE) of the Internet of Things (IoT) literature is broad, and analysis of this literature enables the identification of themes and potential future study topics that will influence system development. This paper reports on bibliometric literature analysis of MBSE of IoT. It considers conference and journal publication trends in the state-of-the-art to identify emerging research themes from the standpoint of trans/multi-disciplinary scholarship and technology. We used Elsevier's Scopus database to find relevant publications from January 2018 to December 2022. Using publication citation ranking and other factors (e.g., publication venues), we selected 110 articles and then analyzed them using BibExcel and VOSviewer software tools. With a modest decline in 2021, this analysis shows an overall increase in publications during the time period. A thematic analysis of the abstracts revealed a strong focus on the introduction of reference architectures and integration of MBSE with business and management methodologies like Agile and BPMN 2.0. Model-driven engineering and machine learning techniques are essential among the enablers for realization of complex heterogeneous IoT systems in the realm of Industry 4.0. We highlight these findings to better understand and meet the enduring challenge of scaling MBSE of IoT across diverse sectors like health, manufacturing, and transportation.INDEX TERMS BibExcel, bibliometrics, Internet of Things, industry 4.0, model-based system engineering, model-driven engineering, thematic analysis, VOSviewer.

show abstract

Section: A Integration With Business and Management Methodologiesmentioning

confidence: 99%

Model-Based System Engineering of the Internet of Things: A Bibliometric Literature Analysis

Ahmad

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The rapid evolution of computing paradigms, coupled with the pervasive integration of Internet of Things (IoT) devices [5], has led to a paradigm shift in network architecture. Edge computing, with its emphasis on decentralized data processing at the network periphery, has emerged as a transformative approach to address the challenges posed by the massive influx of data and the demand for low-latency, high-throughput applications.…”

Section: A Edge Computingmentioning

confidence: 99%

A Secured Intrusion Detection System for Mobile Edge Computing

Alsubhi

2024

Applied Sciences

View full text Add to dashboard Cite

With the proliferation of mobile devices and the increasing demand for low-latency and high-throughput applications, mobile edge computing (MEC) has emerged as a promising paradigm to offload computational tasks to the network edge. However, the dynamic and resource-constrained nature of MEC environments introduces new challenges, particularly in the realm of security. In this context, intrusion detection becomes crucial to safeguard the integrity and confidentiality of sensitive data processed at the edge. This paper presents a novel Secured Edge Computing Intrusion Detection System (SEC-IDS) tailored for MEC environments. The proposed SEC-IDS framework integrates both signature-based and anomaly-based detection mechanisms to enhance the accuracy and adaptability of intrusion detection. Leveraging edge computing resources, the framework distributes detection tasks closer to the data source, thereby reducing latency and improving real-time responsiveness. To validate the effectiveness of the proposed SEC-IDS framework, extensive experiments were conducted in a simulated MEC environment. The results demonstrate superior detection rates compared to traditional centralized approaches, highlighting the efficiency and scalability of the proposed solution. Furthermore, the framework exhibits resilience to resource constraints commonly encountered in edge computing environments.

show abstract

“…Be that as it may, the primary downside of the GoogleNet was its heterogeneous geography that should be modified from module to module. Another constraint of GoogleNet was a portrayal bottleneck that definitely decreases the element feature in the next layer and hence at times may prompt loss of helpful data [41][42][43][44][45][46][47].…”

Section: Related Workmentioning

confidence: 99%

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Aljahdali¹

2023

IJETAE

View full text Add to dashboard Cite

As we know that how rapidly is corona virus spreading, starting from China, to all over the world. There are more than 5 million suspected cases and almost 0.33 million deaths according to the statistics of world meterinfo, and there are limited test kits available in hospitals because of cases are increasing rapidly on the daily bases. So, it is compulsory to build an authentic automatic detection system which gives maximum high performance to check whether the patient is Covid-19 suspect or not as an alternative to slow down the spread of coronavirus among people.In this research, we have used deep learning’s (DL) Convolutional neural network (CNN) and ResNet models. With a critical analysis, we conclude that every ResNet layered model has the high performance with error rate less than 3% on approximately all kinds of datasets of chest Xray images whether it includes rib shadow & clivade or after segmentation. We have proposed a new solution for existing model and to enhance the ResNet model by applying layered architecture style by adding more layers to our ResNet which will help to minimize the error rate. Further, to boost the performance of ResNet by tune up the batch size and learning rate, we achieve the learning rate 0.00001 that has higher accuracy as compared to the other learning rates 0.1, 0.01, 0.001 and 0.0001. The proposed study is promising framework for the covid detection that assist us to deal the COVID decease

show abstract

Ontology Driven Testing Strategies for IoT Applications

Cited by 19 publications

References 25 publications

Model-Based System Engineering of the Internet of Things: A Bibliometric Literature Analysis

Model-Based System Engineering of the Internet of Things: A Bibliometric Literature Analysis

A Secured Intrusion Detection System for Mobile Edge Computing

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Contact Info

Product

Resources

About