Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm

Authors

  • Shaobo Li
  • Chenglong Zhang
  • Jinglei Qu

DOI:

https://doi.org/10.3991/ijoe.v14i11.9544

Keywords:

intelligent manufacturing, wireless sensor network, node location, fruit fly optimization, location error

Abstract


The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.

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Published

2018-11-10

How to Cite

Li, S., Zhang, C., & Qu, J. (2018). Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm. International Journal of Online and Biomedical Engineering (iJOE), 14(11), pp. 202–211. https://doi.org/10.3991/ijoe.v14i11.9544

Issue

Section

Short Papers