# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
## 1 Introduction
Addressing this challenge in urban planning is imperative, necessitating strategic measures to effectively handle growing municipal waste and minimize adverse environmental effects [1]. Numerous studies have investigated both cost and route optimization problems to enhance the performance of operational efficiency. Cost optimization in solid waste management involves identifying and implementing strategies to minimize expenses while maintaining effective waste collection and disposal services. The first step is designing collection routes that minimize travel distances, reduce fuel consumption, and optimize the use of collection vehicles. Next, optimal allocating resources are required, such as personnel, vehicles, and equipment, effectively to enhance operational efficiency and reduce idle time. Another important factor in cost optimization is the implementation of technology, such as route optimization software, sensors, and monitoring systems, to streamline operations and identify areas for cost savings. Waste can also be efficiently sorted at the residential areas so that waste collection and processing cost can be reduced. Vehicle route optimization is important part of solid waste management and requires the designing of efficient and cost-effective paths for waste collection. There are many important factors that should be considered while designing vehicle routes for waste collection. These include consideration of geographical layout of the city area, population of the area, traffic density, and current distribution of waste bins around the city [7]. Furthermore, vehicle routes should be dynamic and change with real-time data to maximize the waste collection efficiency. The optimization process extends to vehicle capacity planning, ensuring that collection vehicles are assigned routes aligning with their capacity to minimize unnecessary trips and vehicle idling. Predictive analytics play a crucial role by anticipating changes in waste generation, allowing for proactive adjustments to collection routes and schedules. Furthermore, the integration of advanced technology, including GPS tracking, sensors, and communication systems on collection vehicles, facilitates ongoing monitoring of route performance, vehicle status, and adherence to planned routes. Valuable insights are also drawn from customer feedback, enabling the identification of areas for route adjustments based on shifts in waste generation or specific collection requirements. This holistic approach to route optimization aims to enhance the overall effectiveness, efficiency, and sustainability of waste collection operations. In this study, our primary focus revolves around addressing the route optimization problem within the domain of solid waste management. Recognizing the multifaceted nature of decision-making in this context, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm is employed [8]. TOPSIS proves invaluable in situations where decision makers are confronted with the need to evaluate alternatives based on multiple criteria, each carrying varying degrees of importance. The key features of the proposed algorithm and major contributions of the work are as follows:  
* An IoT- and TOPSIS-based solution is provided to improve the waste collection process and route optimization of waste vehicles. * Several key waste-related metrics are considered to design an effective route optimization algorithm for waste collection. These metrics include waste toxicity, waste volume, duration of waste in the bins, and distance of bins from other bins in the area. * The TOPSIS technique is utilized to consider multiple factors in order to select routes that maximize the collection of old, highly toxic, and large-volume waste and minimize the travel distance of vehicles. * Extensive simulations are performed to show that the proposed technique outperforms other techniques to maximize the efficiency of waste collection.