Effective Cluster Head Substitution Leach Protocol for Wireless Communication

Michel, Kabukila Kilotwa; Yan, Hao; Immaculate, Njiraini; Yaye, Biaye

doi:10.4236/jcc.2022.105005

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Based on the research results of time-delay systems and networked control systems, it can be observed that network delays will degrade the system performance to some extent [13,14]. At the same time, the complexity of wireless sensor networks and the variable additional delay introduced by multi-hop radio frequency communication make it challenging to analyze and synthesize control systems based on wireless sensor networks [15,16]. The following closed-loop control system can be obtained through calculation:…”

Section: Stability Analysis Of the Modelmentioning

confidence: 99%

Construction of Big Data Analysis Platform for College Students’ Sports Training Driven by Wireless Communication Network

Tian,

Zhu,

2024

Int. J. Emerg. Technol. Learn.

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College students’ physical training is an important aspect of their studies and lives. In light of the existing issues with the accuracy of the physical training evaluation index for college students and other issues. Based on the theory of wireless communication networks, model definition and stability analysis methods are used to optimize the original model. The optimization model of a wireless communication network is obtained by considering the data output control theory, trained by college students. This model can analyze the big data of college students’ physical training and construct the corresponding data platform. Through calculations, the rules for data changes under different indicators can be obtained. Finally, the accuracy of the optimized model is verified by comparing it with the original model. Related studies show that the variable parameters in the control system include model parameters, network upper limit, and corresponding control system parameters. On the whole, the model parameters exhibit a consistent trend of change, while the upper limit of the network displays a characteristic three-stage linear pattern. The control system first increases rapidly and then exhibits a gradual decline in exponential function type. The control system and the switching system can be switched using model calculations. The two different change curves exhibit obvious symmetry characteristics when influenced by the output times. It shows that the two change systems have opposite effects on the model, and the overall volatility is relatively high. The wireless communication network model can analyze and build a platform for collecting college students’ sports training data. According to the model calculation, it can be seen that the changes in different indexes exhibit clear linear characteristics. Among them, the concepts of sports, personalized training, and basic training show linearly increasing changes. However, the training structure and content exhibit a typical linear decline. Finally, the advantages of the wireless communication network model are illustrated with the experimental data. This study can provide guidance and ideas for the construction of a data platform for college students’ physical training.

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