Smart Waste Management Using IoT-Driven Sensor Mesh and AI Analytics

Thesis Abstract

Urban waste management faces inefficiencies due to limited visibility into bin capacity, irregular collection routes, and suboptimal resource allocation. This study investigates an IoT-driven sensor mesh integrated with AI analytics to optimize urban waste collection, improve service reliability, and reduce operational costs while minimizing environmental impact. The aim is to design, implement, and evaluate a scalable smart waste management system that leverages real-time bin-level sensing, geospatial routing, and predictive analytics to inform decision-making across municipal services. Specific objectives include (1) to develop a low-power, robust sensor mesh capable of accurate fill-level monitoring in diverse climatic conditions; (2) to create an AI-enabled analytics platform that fuses sensor data, historical collection records, and external variables (weather, events) to forecast fill rates and optimize collection schedules; (3) to compare route optimization performance against conventional schedules using scenario-based simulations and live pilot deployments; (4) to assess economic, environmental, and social performance via a multi-criteria evaluation framework; and (5) to formulate an implementation roadmap addressing interoperability, data governance, and governance of urban IoT ecosystems. The study adopts a mixed-methods design anchored in an interpretive-positivist continuum to evaluate technical feasibility and operational impact. The population comprises city waste management operations in a mid-sized metropolitan area, with a pilot district of 12 municipal collection zones selected for deployment. A stratified sampling approach yields a total sensor deployment of 600 autonomous bins equipped with capacitive fill-level sensors, low-power microcontrollers, LoRaWAN transceivers, and solar panels. Data collection instruments include (i) sensor data streams on bin fill levels, temperature, and transit times; (ii) route and pickup logs from the municipal waste management information system; (iii) semi-structured interviews with 18 frontline operators and 6 logistics managers; and (iv) cost and environmental performance records for pre- and post-deployment periods. Validity and reliability are established via calibration tests of sensors (MAE < 2.5%), pilot corrosion tolerance runs, instrument triangulation with manual site checks, and test-retest reliability for interview protocols (Cronbach’s alpha > 0.85). Analytical methods integrate quantitative and qualitative approaches. Time-series forecasting using ARIMA and gradient boosting (XGBoost) models predicts bin fill rates up to 7 days ahead, with performance assessed by RMSE and MAE. A route optimization module employs integer programming and metaheuristic algorithms (genetic algorithms and ant colony optimization) to minimize total distance and emissions under dynamic demand, evaluated through simulated detours and real-world pilot comparisons. Regression analyses examine relationships between predicted fill rates, collection costs, and missed collection incidents, while ANOVA tests compare performance across zones and seasons. The qualitative strand employs thematic analysis of operator interviews to identify adoption barriers, perceived usefulness, and organizational readiness, integrated with the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovations theory to interpret uptake dynamics. A conceptual model synthesizes sensor data, AI predictions, and route optimization within the broader urban governance context. Key expected findings include (i) high fidelity in fill-level estimation with robust performance under diverse microclimates; (ii) measurable reductions in vehicle-kilometers-traveled (VKT) and fuel consumption, with anticipated decreases of 18–25% in pilot zones; (iii) improved service reliability demonstrated by a 12–20% reduction in late or missed pickups; (iv) cost savings driven by optimized routing and reduced maintenance; and (v) identified organizational enablers and barriers affecting scalable deployment, including data governance, interoperability standards, and stakeholder engagement. The study contributes to knowledge by integrating IoT sensing, AI-driven forecasting, and optimization in a cohesive smart waste management framework, offering a replicable blueprint for mid-sized cities and informing policy on digital urban infrastructure, data stewardship, and sustainable logistics. The main conclusion envisages that IoT-driven sensor meshes combined with AI analytics substantially enhance operational efficiency, environmental outcomes, and citizen satisfaction, provided that interoperable standards, adequate data governance, and change management strategies are embedded. Recommendations include scaling the sensor network with standardized APIs, establishing a seed fund and governance model for ongoing maintenance, and extending the framework to recycling streams and composting programs to broaden sustainability impacts.