Smart Waste Management with IoT: An Optimized Triple Memristor Hopfield Neural Network Approach

Smart waste management systems are interdisciplinary approaches now recog nized as critical to combating the escalating issues with waste in metropolitan regions. Such systems rely on high-technology computational and communication platforms for purposes of effectiveness, expandability, and eco-friendliness. This paper presents a refined Triple Memristor Hopfield Neural Network (TM-HNN) scheme incorporating an IoT smart WM system. Exploiting the advantages of memristor-based architectures, the offered solution improves energy consump tion optimization, waste categorization precision, and system interactions. IoT integration enhances continual monitoring of the system, data collection, and decision making the system quickly responsive to the dynamism of urban waste scenarios. Studies made when comparing this developed approach to the conven tional ones indicate marked enhancements in operational parameters like classi fication accuracy and computational complexity. This paper provides a detailed system flow that focuses on data collection through IoT-connected sensors, TM-HNN-based neural network training and tuning, and system performance calculation and modeling using appropriate mathematical models. These results confirm the proposed approach and are based on the mathematical equations and performance plots. Performance is defined based on core power measure ments Integral results showcase an improvement totaling a hundred percent for these metrics hence showing the efficiency of the proposed in real-world utiliza tion. Therefore, the outcomes of this work will fit the approach to the construc tion of intelligent and eco-friendly waste management systems within the context of smart cities.