Intelligent IoT-Enabled Waste Management System Based on Discrete PID Control and Hybrid Route Optimization

Abstract

With accelerated urbanization, the inefficiency of traditional municipal solid waste management systems—characterized by fixed collection schedules, delayed overflow responses, and high resource consumption—has become a critical bottleneck restricting urban sustainability. This study proposes an intelligent waste management system integrating IoT-based real-time monitoring, discrete PID dynamic control, and hybrid algorithm route optimization. The system adopts a three-tier “perception–decision–execution” architecture: ultrasonic sensors at the perception layer collect hourly waste fill-level data and transmit it to a municipal cloud platform via IoT; the decision layer constructs a waste generation model and a garbage truck dynamic model (second-order transfer function) to optimize scheduling strategies using discrete PID; the execution layer combines simulated annealing (for station location) and ant colony algorithms (for shortest path planning) to minimize travel distance. Experimental results in MATLAB/Simulink show that the system exhibits excellent dynamic performance (critical damping, no overshoot/undershoot) and adaptability to variable input patterns (constant, ramp, sinusoidal), with overflow probability reduced to zero during peak periods. Route optimization reduces the average distance from garbage stations to bins by 36.5% and total travel distance by 28.3%, cutting fuel consumption by 20% and reducing annual CO₂ emissions by 20,000 tons in medium-sized cities. Limitations remain, such as simplified fill-rate models and high initial sensor costs. Future work will integrate LSTM predictive models and NB-IoT technology to enhance refinement and cost-effectiveness. This system shifts waste management from “passive response” to “active prevention,” offering a practical framework for low-carbon, efficient, and sustainable urban sanitation.