IoT-Enabled Waste Management Solutions: A Cross-System Analysis for Sustainable

Abstract

The global solid waste crisis, projected to reach 3.88 billion tons annually by 2050, requires efficient, scalable, and inclusive management solutions. Traditional systems are inefficient, costly, and environmentally harmful, while IoT-enabled approaches offer real-time monitoring, predictive analytics, and optimized routing—yet they remain predominantly urban-focused, with rural and low-resource areas underserved and lacking direct comparative studies. This research bridges this gap through a systematic cross-system analysis of recent publications, synthesizing IoT technologies (sensors, LoRaWAN/NB-IoT, AI integration), urban and rural applications, pilots, efficiency outcomes, costs, sustainability impacts, and barriers. Findings show urban systems deliver 30–40% efficiency gains, reduced emissions, and enhanced recycling via dense networks and smart city integration, while rural adaptations (solar-powered bins, edge computing) achieve only 15–25% improvements due to connectivity gaps, power limitations, and scalability issues. The study’s novelty is the proposed hybrid model architecture, which integrates urban AI sophistication (predictive analytics, blockchain security) with rural resilience features (solar energy, edge processing, context-aware switching) to enable adaptive performance across contexts. This unified framework demonstrates potential for 25–35% overall efficiency gains, stronger data security, and equitable adoption through modular, low-cost designs and community interfaces. Outcomes indicate significant potential to reduce landfill emissions, advance circular economy principles, and support SDGs 11 and 12, provided future efforts prioritize rural field trials, policy incentives, and advanced AI integration for truly inclusive, sustainable waste management worldwide.