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

## TL;DR

This paper presents an energy-efficient IoT framework for smart waste management using dynamic scheduling and adaptive power control in LoRaWAN networks.

## Contribution

A novel framework combining dynamic scheduling and adaptive power control to significantly reduce energy consumption in LoRaWAN-based waste management systems.

## Key findings

- The system reduces energy usage by over 84% while maintaining reliable data transmission.
- Battery life is extended, reducing maintenance interventions in smart waste bins.
- The framework is scalable and suitable for resource-constrained urban environments.

## Abstract

What are the main findings?
Dynamic scheduling combined with adaptive data rate and power control significantly reduces LoRa node energy consumption.The optimized system maintains reliable data transmission while extending battery lifetime.

Dynamic scheduling combined with adaptive data rate and power control significantly reduces LoRa node energy consumption.

The optimized system maintains reliable data transmission while extending battery lifetime.

What are the implications of the main findings?
Reduce energy usage.Reduce overflow and operational costs in smart waste management systems.

Reduce energy usage.

Reduce overflow and operational costs in smart waste management systems.

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments.