Dynamic Scheduling and Adaptive Power Control for LoRaWAN-Based Waste Management: An Energy-Efficient IoT Framework

Area of Science:

• Environmental Science
• Computer Science
• Engineering

Background:

• Efficient waste management is crucial for urban sustainability.
• Smart bin systems leverage IoT for better waste collection.
• Energy efficiency is key to the viability of IoT waste management.

Purpose of the Study:

• To optimize power consumption in LoRa-based smart bin systems.
• To enhance the energy efficiency and sustainability of IoT waste management.
• To reduce operational costs and maintenance interventions in smart waste systems.

Main Methods:

• Utilized LoRa (long-range) communication technology for IoT waste management.
• Implemented advanced optimization techniques to minimize LoRa node energy usage.
• Integrated dynamic scheduling and adaptive data rate/power algorithms based on bin usage.

Main Results:

• Achieved over 84% reduction in energy usage for LoRa nodes.
• Demonstrated significantly longer battery life for smart bins.
• Reduced the frequency of maintenance interventions through optimized system performance.

Conclusions:

• Developed a scalable and energy-efficient framework for smart waste management.
• Validated the effectiveness of optimization techniques through simulations and real-world deployment.
• The proposed system offers a sustainable solution for resource-constrained environments.