IEEE Access (Jan 2020)

Optimization of Sports Fitness Management System Based on Internet of Health Things

  • Yongquan Tang,
  • Dehua Wang

DOI
https://doi.org/10.1109/ACCESS.2020.3039508
Journal volume & issue
Vol. 8
pp. 209556 – 209569

Abstract

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Due to the huge scale of the Internet of Health Things (IoHT), various research methods currently applied in the field of Internet of Health Things are limited to the technical problems themselves, which lack a macro grasp of the entire Internet of Things (IoT) system. The verification of the proposed technical scheme requires the purchase of a large number of hardware devices, but as a result, only relatively limited research models can be constructed, which makes the entire system long management cycle and high management cost. Based on the research of particle swarm optimization algorithm, this paper finds its shortcomings and makes corresponding improvements. A new improved method of particle swarm optimization algorithm is proposed. By introducing the method of average particle distance and population distribution entropy in the algorithm search process, the inertia weight is dynamically changed. This is conducive to the improvement of the optimization efficiency of the particle swarm optimization algorithm, and has a good optimization accuracy. A IoHTs modeling method for component collaboration is proposed, and the IoHTs component library is designed according to the proposed modeling method. At the same time, this article discusses the XModel modeling simulation platform, and builds a IoHTs component model based on this platform, and has completed the verification of the validity of the proposed modeling method and the feasibility of the IoHTs IPv6 communication scheme. The component library designed in the XModel platform is based on the functions of IoT devices, and the components have more complete network communication features. The XModel platform specifically customizes the CMIoT component library, and supports the continuous expansion of the component library and the continuous refinement of component functions, providing a new method for communication research in the Internet of Health Things field.

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