IOT-Based Smart Waste Management System Using AI for Route Optimization

Abstract

Rapid urbanization and population growth have placed increasing pressure on municipal solid waste management systems, many of which still rely on fixed collection schedules and static routes that ignore actual bin fill levels. This inefficiency leads to unnecessary fuel consumption, higher operational costs, traffic congestion, and, in some cases, overflowing bins that create public health and environmental hazards. This paper presents the design and evaluation of an IoT-based smart waste management system that integrates ultrasonic fill-level sensors, GPS-enabled collection vehicles, a cloud-based data platform, and an artificial intelligence (AI) route optimization engine. The system continuously collects real-time data on bin fill levels, location, and vehicle status, and applies a hybrid optimization approach combining a modified Genetic Algorithm with real-time traffic-aware heuristics to generate dynamic, near-optimal collection routes. A simulated deployment across a representative urban zone was used to evaluate the system against a conventional fixed-route baseline. Results indicate an approximate 30% reduction in travel distance and fuel consumption, a 31% reduction in average collection time, and a 73% reduction in bin overflow incidents. The findings suggest that combining IoT sensing with AI-driven optimization can substantially improve the efficiency, cost-effectiveness, and environmental sustainability of urban waste collection. The paper concludes with a discussion of implementation challenges, limitations, and directions for future research, including multi-city validation and integration with electric vehicle fleets.