Energy efficient waste collection vehicle routing system using time series prediction

The focus of this research is on addressing the inefficient management of Municipal Solid Waste (MSW), which often results in large quantities of waste being dumped into garbage bins. Previous approaches have struggled to effectively manage MSW promptly. To improve this process, we propose a time-series-based, energy-efficient waste prediction model. Initially, we convert the Bins Historical Time Series Dataset into a Comma-Separated Values (CSV) file format. The model, named Exponential Auto-Regressive Integrated Moving Average-Nesterov-accelerated Adaptive Moment estimation-Long-Short Term Memory (EARIMA-LSTM) using NADAM optimizer, is trained and tested using numeralized features extracted from the dataset. Smart bins equipped with Internet of Things (IoT) technology are then deployed to predict waste levels using the EARIMA-NADAM-LSTM model. After waste level detection, we employ the Scramble Mutation Coral Reefs Optimization-Haversine distance-based K-Means (SMCRO-HK-Means) clustering technique to group the bins. Next, we establish multiple paths between bins nearing capacity using the Ad-hoc On-Demand Distance Vector (AODV) protocol. Finally, the Optimal Path (OP) is determined using the modified A* algorithm based on multi-weight heuristics (Op A*). This approach effectively manages waste collection by preventing bin overflow. Our model achieves good accuracy for waste prediction model with a Mean Absolute Error (MAE) of 0.138%, and 42,518 ms for clustering 500 waste bins, outperforming existing methods. Also, it finds the dynamic optimal route for waste collection with optimal cost.