Architectural Design of a Low-Power LoRa-Based Smart Waste Bin Management System

Abstract

Inefficient waste management, characterized by fixed collection schedules, leads to overflowing bins, unnecessary collection trips, and increased operational costs. This article presents the architectural design of a Smart Waste Bin Management System (SWBMS) engineered to address these challenges through Internet of Things (IoT) technology. The proposed system is built upon a three-tier architecture: the smart waste bin (SWB) node, a LoRa communication network, and a central data monitoring platform. Each SWB is equipped with infrared sensors for fill-level detection and an ATMEGA328P Microcontroller, optimized for low-power operation. A key design feature is the use of MOSFET switches to completely power down sensors and the LoRa transceiver (RFM98) between active cycles, minimizing energy consumption. Communication is handled via LoRa technology for long-range, low-power data transmission to a gateway based on Raspberry Pi and an SX1302-based concentrator. The server layer, comprising a Python UDP application and a real-time monitoring. This design provides an energy-efficient hardware and software architecture for sustainable waste management.

Introduction

Rapid urbanization and population growth have made municipal solid waste management increasingly challenging. Traditional fixed-schedule waste collection is inefficient, leading to unnecessary trips, overflowing bins, environmental pollution, and public health risks. To address these issues, this study presents the design of a Smart Waste Bin Management System (SWBMS) based on the Internet of Things (IoT), shifting waste collection from a schedule-based to a need-based, data-driven model.

The proposed system emphasizes low power consumption to enable long-term, battery-operated deployment in urban and remote areas. It uses a Low-Power Wide-Area Network (LPWAN), specifically LoRa technology, for real-time waste level monitoring and long-range communication with minimal energy usage.

A comprehensive literature review shows global progress in smart waste management using sensors, IoT, AI, and route optimization. However, many existing systems suffer from high energy consumption and limited battery life. This research addresses these gaps by introducing hardware-level power management using MOSFET switches to eliminate standby power drain and significantly extend system lifetime.

The SWBMS architecture consists of three main components:

1. Smart Waste Bins (SWB): Equipped with infrared sensors for medium and high fill-level detection, an ATMEGA328P microcontroller, and a LoRa module. Sensors and communication modules are powered only when needed to conserve energy.

2. Central Unit / Gateway: A LoRa gateway based on Raspberry Pi and SX1302 concentrator receives data from bins and forwards it to the server over IP networks.

3. Server and Monitoring Platform: A backend system using UDP communication, MySQL database, Node.js REST APIs, and a web dashboard for real-time visualization, alerts, historical analysis, and system management.

The system enables municipalities to monitor bin status, optimize collection routes, reduce fuel consumption, and minimize environmental impact. Overall, the proposed SWBMS provides an energy-efficient, scalable, and cost-effective solution for smart urban waste management, addressing key limitations of existing IoT-based approaches.

Conclusion

This article has detailed the comprehensive architectural design of a Smart Waste Bin Management System based on low-power IoT principles. The design encompasses the hardware of the smart bin with its innovative power-switching mechanism, the robust LoRa communication network using an SX1302 gateway, and the scalable server-side software architecture. By focusing on energy efficiency at the component level and leveraging long-range communication, this design provides a solid foundation for a practical and sustainable waste management solution. The next phase of this research, beyond the scope of this article, involves implementing and testing low-power consumption algorithms (such as dynamic scheduling) on the described hardware, deploying the system in a real-world environment, and quantitatively analysing its performance in terms of energy savings and operational efficiency.

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Copyright

Copyright © 2025 Cedrick Atse, Yongbo Wu, Ping Tan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.