International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 3
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Intelligent Waste Collection in Smart Cities: An IoT-Integrated Multi-Agent Deep Reinforcement Learning Approach

Author(s) Shrabani Sutradhar, Mr. Rameshwar Singh
Country India
Abstract Cities are becoming smarter in need of advanced, responsive and energy efficient waste-collection solutions to consider increasing municipal solid waste and the variation in waste generation trends. The framework proposed in this paper constitutes a broad Multi-Agent Deep reinforced learning (MADRL) framework that incorporates IoT-connected smarter bins, an edge-level fill-level predictor and cooperative learning among the autonomous waste-collection vehicles. The system is optimized to determine the dynamic vehicle movement based on uncertainty using centralized training with decentralized execution (CTDE), attention based critic and predictive bin level demand modeling. The proposed MADRL framework pares down greatly traveling distance, energy consumption, overflowing and delays of service compared to classical DVRP heuristics and greedy strategies. Small, medium and large urban layouts operate with acceptable efficiency in terms of sensor noise, burst events of waste-generation, and locally varying spatial demand. The research paper adds operationally feasible, scalable and learning enabled routing paradigm to future infrastructures of smart-cities waste-management.
Keywords Smart Waste Management, Internet of Things (IoT), Multi-Agent Deep Reinforcement Learning (MADRL), Centralized Training Decentralized Execution (CTDE), Dynamic Vehicle Routing Problem (DVRP), Edge Computing
Field Engineering
Published In Volume 4, Issue 3, May-June 2022
Published On 2022-05-12
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E-ISSN 2582-2160
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