The increasing need for green urbanization has surfaced the inefficiencies of conventional waste collection infrastructure. Proposes a smart waste collection system based on IoT sensors and TOPSIS multi-criteria decision support method for route optimization with criteria such as toxicity, volume, and time duration. With multiple influencing variables compared to using one parameter, the system proposed here achieved a reduction of 14% in the overall collection distance. Likewise, it deals with the application of IoT for real-time medical waste monitoring in smart cities. It suggests an Android-based route navigation and real-time bin statistics-supported Medical Waste Collection Management (MWCM) system for ensuring waste collection truck routing. It is designed to minimize labor, cost, and environmental footprints while facilitating sustainable development goals. Drawing upon the past trend of embedding cutting-edge technologies in urban waste management, we investigate the use of Industry 4.0 and cyberphysical systems for the collection of residential waste in downtown Toronto. A mathematical model is formulated to solve routing, scheduling, and assignment problems, with optimization objectives aimed at cost-effectiveness, environmental sustainability, and public health factors. The model is tested using a binary bat algorithm and scenario analysis and has been shown to be effective in improving operational sustainability and reliability. Combined, all Papers offer varied but complementary solutions to smart waste management, highlighting the importance of IoT, decision algorithms, and smart planning in the transformation of municipal services.
Manikantan et al. (Fri,) studied this question.