Dynamic route optimization for IoT based intelligent waste collection vehicle routing system

Efficient waste management has been a major problem for municipalities in urban communities. The one important task is to find the optimum route for the waste collection vehicle with the optimum cost of transportation, reduced emissions, and improved city image. The proposed waste management system consists of three major parts, Smart Bins, Smart vehicles, and a cloud network for communication. The smart bin is embedded with sensors to measure the level of garbage in the bin. On overshooting the overflow threshold, the bins become active in the network and send signals to the server through the cloud network. The active bins are clustered based on their geographical locations. A dynamic optimum path is generated for each vehicle within each cluster. The research proposes a clustering-based efficient waste collection vehicle routing system. For Dynamic path optimization, a weighted multiple heuristic-based Optimum A*(Op-A*) algorithm is proposed which utilizes heuristics like distance, traffic, road quality, fuel status, the waste amount in the smart bin, and vehicle load capacity. This research focuses on implementing and testing the proposed Op-A* algorithm with a synthetic dataset. The results are compared with existing state-of-art algorithms. The improved result generated by the proposed algorithm demonstrates the significantly outperformed state-of-the-art benchmarks from the literature in terms of total transportation cost and path selection time for the waste collection vehicle routing system.