An IoT based three-tier fog computing architecture for policy-aware Smart Waste Management using fog and edge

Resumo

Introduction In fast-urbanizing areas, municipal solid waste collection still runs on a fixed schedule that ignores real-time bin state, wasting fleet capacity and leaving overflow-prone bins unserviced. The main problems are as much about governance as engineering: service delivery is unlinked from need, with no accountability signal; statutory segregation duties are unenforceable for want of an evidentiary base; enforcement instruments are ungraduated; and the distributional burden is invisible. Smart-bin systems presently address sensing or routing in isolation, funneling telemetry through one cloud tier whose wide-area latency is incompatible with time-critical hazard alerts. Methods We present SWMS, a three-tier edge/fog/cloud system in which smart bins sense fill level, methane, temperature, and weight, district fog nodes evaluate thresholds and compute routes locally, and a cloud tier runs graduated compliance enforcement and an Eco-Score layer. All results are computational, not measurements of a deployment. No physical prototype was deployed, and all bin data are synthetic; the compliance and gamification layers were implemented but not validated by any user study, though the sensor error model was calibrated to real gas-sensor data. Results A seeded Monte Carlo model places the fog-mediated alert path at 30.9 ms against 233.1 ms for a cloud counterfactual, confirmed by two discrete-event simulations. A comparison with a proven optimal solution shows that greedy+2-opt closes to within 5.1%, matching the best tuned metaheuristic at three orders of magnitude lower compute. A service-parity comparison cuts daily collection distance by 37.9%, reproduced at 38.7% on an actual OpenStreetMap street network. Scalability to 20,000 bins follows O ( n 1.72 ), and fault injection raises overflow-alert F 1 from 0.70 to 0.80 at 50% sensor faults. Discussion We situate the design in the public-governance and regulatory-governance literatures and identify the obstacles a deployment faces: statutory authority, due process, administrative discretion, attribution in communal bins, distributive incidence, legitimacy, data protection, and municipal capacity. Because a non-zero sensor error rate entails a non-zero wrongful-penalty rate, we conclude that the compliance layer should first be deployed as a non-penal, evidentiary, and advisory instrument rather than a penal one.