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Updated: Sep 8, 2025

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Dynamic Resource Allocation and Forecast of Snow Tourism Demand Based on Multiobjective Optimization Algorithm.

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Summary

This study optimizes wireless sensor network mapping for reliable data transmission by minimizing energy consumption using a discrete particle swarm optimization algorithm. It also analyzes factors influencing ice and snow tourism demand.

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Area of Science:

  • Computer Science
  • Engineering
  • Tourism Management

Background:

  • Wireless Sensor Networks (WSNs) face challenges in reliable data transmission and energy efficiency.
  • Dynamic resource allocation is crucial for complex, collaborative tasks in WSNs.
  • Understanding market drivers is essential for the growth of the ice and snow tourism sector.

Purpose of the Study:

  • To map tasks in wireless sensor networks for reliable transmission while minimizing energy consumption.
  • To adapt discrete particle swarm optimization for efficient node mapping and dynamic resource allocation.
  • To analyze key factors influencing the ice and snow tourism market.

Main Methods:

  • Transforming the WSN mapping problem into an energy consumption reduction challenge.
  • Employing a discrete particle swarm optimization algorithm with an improved, linearly minimized inertia coefficient.
  • Integrating the nearest node mapping principle to reduce inter-task communication energy.
  • Utilizing regression analysis and demand-based forecasting for tourism market analysis.

Main Results:

  • The discrete particle swarm optimization algorithm effectively optimizes node mapping, reducing energy consumption.
  • Improved inertia coefficient and nearest node principle enhance mapping efficiency and reliability.
  • Per capita disposable income and urban population size significantly impact ice and snow tourism demand.
  • Identified key decision-making factors for tourism destination marketing.

Conclusions:

  • The proposed discrete particle swarm optimization approach enhances WSN mapping efficiency and reliability.
  • Dynamic resource allocation in WSNs can be effectively managed using this optimization technique.
  • Insights into tourism market drivers provide a basis for strategic marketing and development.
  • The integration of WSN optimization with tourism analysis offers a novel interdisciplinary approach.