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Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization.

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Summary
This summary is machine-generated.

This study presents a smart waste management system for rural areas using low-cost sensors and optimized collection routes. The system significantly reduces costs, time, and workforce needs compared to traditional methods.

Keywords:
CVRPLoRaWANVRPdata analyticsdecision-makinglow powered sensorsopen datasmart waste collectionwireless sensor networks

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Smart City and Internet of Things (IoT) advancements offer new waste management solutions.
  • Rural waste management requires cost-effective, low-consumption sensor networks adaptable to various applications.

Purpose of the Study:

  • To introduce a waste monitoring and management platform for rural environments.
  • To develop a low-consumption wireless sensor node for waste container data collection.
  • To create an optimized waste collection route system.

Main Methods:

  • Developed a low-consumption wireless node using LoRa and SAM L21 microcontrollers to measure waste container weight, volume, and temperature.
  • Implemented a platform for data analysis, alert generation, and dynamic route optimization.
  • Utilized a mobile application for guiding collection fleets along optimized routes.
  • Conducted a case study in Salamanca using open data and regional waste management reports.

Main Results:

  • The developed node demonstrated a long operational lifetime and extensive coverage with minimal antenna deployment.
  • The route optimization system, utilizing sensor data, achieved significant savings in costs, time, and workforce.
  • The system proved efficient and viable for rural waste management implementation.

Conclusions:

  • The proposed smart waste management platform is effective for rural environments.
  • Low-cost, low-consumption IoT solutions are viable for improving waste collection efficiency and sustainability.
  • Dynamic route optimization based on real-time data offers substantial operational benefits.