IoT Enabled Waste Management Optimization Framework (IWMOF)

Globally, the growing amount of waste produced by cities is of great concern due to its negative effects on both the surroundings and the well-being of people. Waste management has emerged as a crucial topic of study to address this problem, with numerous strategies and methods proposed to improve waste collection, transportation, and disposal. In this research, a novel framework for waste management optimization in IoT (Internet of Things) environments, using Deep Reinforcement Learning with Fuzzy Logic Control is proposed. The framework employs IoT sensors to gather real-time data on waste generation, bin levels, and truck locations. Fuzzy logic control is used to transform the sensor data into actionable insights that aid in decision-making. Deep Reinforcement Learning is then utilized to optimize waste collection routes and schedules, reducing transportation and collection costs while mitigating negative environmental impacts. To assess the effectiveness of the suggested framework, simulations are executed using real-world information. The simulation results show that the framework was effective in reducing the total collection time, minimizing the distance travelled by the trucks, and maximizing the waste collection efficiency. The proposed framework also demonstrated significant improvements in reducing the negative impacts of waste management on the environment, such as reducing greenhouse gas emissions and minimizing the number of waste transportation trips. The proposed framework offers an innovative approach to optimize waste management using the power of Fuzzy logic control and Deep Reinforcement Learning. The use of IoT sensors and real-time data makes it possible to achieve a more efficient and sustainable waste management system. The results of simulations demonstrate the potential of the proposed framework to be implemented in real-world waste management systems to enhance their efficiency and effectiveness.