An Optimal Path Recommendation Algorithm for Waste Carrier Vehicle using IoT based Smart Bins

Introduction: Smart cities demand efficient and sustainable solutions for urban waste management due to rising populations and environmental concerns. Traditional waste collection methods are inefficient, costly, and environmentally harmful. This research addresses these challenges by proposing a Smart Garbage Collection System (SGCS) that uses IoT-enabled Smart Garbage Bins (SGBs) and a heuristic-driven route optimization algorithm to dynamically schedule and guide Garbage Collection Vehicles (GCVs) for efficient municipal solid waste (MSW) collection. Methods: A modified A* algorithm, called Op-A*, was developed to generate dynamic optimal routes for GCVs. This algorithm integrates multiple heuristics such as distance, traffic conditions, road quality, bin fill levels, and vehicle capacity. The system architecture includes real-time data acquisition from ultrasonic sensors in SGBs, transmission to cloud servers, and route recalculation when environmental parameters change. Experimental scenarios were simulated on both a small graph (manual test) and a synthetic dataset of 500 nodes (city-scale model). Results: The Op-A* algorithm significantly outperformed traditional A* and other metaheuristic algorithms (e.g., Ant Colony Optimization, Genetic Algorithm, PSO) in terms of route optimality, measured by a novel Optimal Index (ratio of distance to waste collected). Results showed that incorporating real-time traffic and road quality heuristics reduced total route cost and improved waste collection efficiency. Optimal route decisions dynamically adapted to changes in node activity and vehicle constraints. Discussion: Experimental findings validate that the Op-A* algorithm's multi-heuristic strategy ensures timely waste collection, minimizes vehicle travel time and fuel consumption, and scales effectively to large, real-world datasets. However, limitations were identified such as high computational complexity for large datasets and sensitivity to heuristic weights (traffic and road quality). The reliance on robust IoT infrastructure and real-time data availability also presents challenges for practical deployment. Conclusion: The proposed Op-A* algorithm offers a robust and scalable solution for dynamic waste collection in smart cities by integrating multiple real-world heuristics into a single optimization framework. It outperforms existing algorithms by producing more efficient and responsive routing solutions. Future work will incorporate machine learning and reinforcement learning to further optimize dynamic pathfinding and predictive bin-level analysis, enhancing adaptability and efficiency.