Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.

The Information Technology system use visualization to represent data in different forms. Some new researches in this field working on extract Knowledge, rapid information retrieval from the graphical diagram. Therefore, data visualization now trends to use ontology approach to build a robust knowledge-based system. The proposed of this paper is to developed ontology approach and use software tools to improve visualized knowledge. Moreover, study data visualization subject in term of ontology and how to facilitate understanding it on the end user. Computer networks ontology adopted as a case study to prove the important of this approach. The Object Role Modeling (ORM) is a visualized notation used to build the prototype for ontology, and OntoGraf module in the Protégé tool used to build ontology.

The most important research in the world in these days, research that looking at the internet of thing's (IoT) topics and their applications. Most of these applications depend on RFID system, which includes RFID readers and tags. The important issues in RFID system or network are how we can reduce anti-collision between readers to identify and read tags data. In these paper, we suggest an Improved anti-collision protocol, which can be used to connect RFID readers with RFID tags and reduce the number of RFID tag's collisions. The simulation shows that an Improved Class-1 Generation 2 algorithm is better than Slotted Aloha, Class-1 Generation-2 (Number of Tags Known), Class-1 Generation-2 (Number of Tags Unknown) algorithms.