Smart Waste Segregation and Recycling System: A Systematic Literature Review and System Design

Abstract

Municipal solid waste management is the point at which decades of production and consumption converge into a single operational problem, and how well that convergence is handled has a direct bearing on public health, landfill life, and the share of recoverable material that is actually recycled rather than discarded. For decades this convergence point has been managed through manual segregation—first by residents at the point of disposal, and again by sanitation workers at collection and material-recovery facilities—an approach that is labor-intensive, physically hazardous, inconsistent across households and shifts, and structurally reactive, since contamination in a waste stream is usually discovered only after different waste types have already been commingled. This review surveys eight recent works spanning computer-vision and deep-learning waste classification, sensor-driven smart-bin segregation hardware, fill-level monitoring with collection-route optimization, and gamified citizen-engagement applications for recycling behavior. Read together, these works show a consistent shift away from purely manual, end-of-pipe sorting and toward continuous, automated, and increasingly behavior-aware waste management. At the same time, nearly every reviewed approach treats bin-level segregation, collection logistics, and citizen behavior as separate problems, and only one line of work attempts to close the loop between automated hardware and the resident whose sorting habits determine how much contamination ever reaches a bin in the first place. Building on these findings, this paper presents the design of an Intelligent Smart Waste Segregation and Recycling System: a three-tier, sensor-plus-software platform that combines an edge-deployed computer-vision classification module for real-time, bin-level sorting into biodegradable, recyclable, and hazardous streams; a cloud analytics layer that performs fill-level prediction, data-quality validation, and dynamic collection-route optimization; and a citizen-facing mobile application that closes the loop through gamified recycling rewards and a live, color-coded municipal dashboard. The resulting architecture is intended to move municipal waste operations away from reactive, labor-bound sorting and toward continuous, evidence-based, and behaviorally reinforced recycling.