Edge AI Agent Design for Policy-Aware Urban Waste Management

5 pages•Published: April 19, 2026

Binrong Zhu, Ruxue Jin, Yang Liu, Guiran Liu, Qun Wang and Phuong Mai Nguyen

Abstract

Urban waste management suffers from contamination, inefficiency, and poor adaptability to changing conditions. Existing AI-based waste classification systems act as static classifiers, unable to account for real-time factors such as bin capacity or evolving municipal policies. This paper presents Edge AI Agent, a framework that transforms smart bins into context-aware, decision-making systems. Built on a dual YOLOv5/YOLOv8 perception module, integrated with local policy databases and IoT-driven bin state monitoring, the agent uses an edge-native large language model to fuse perception, regulation, and infrastructure data for adaptive, user-oriented waste disposal guidance. Deployed on resource-constrained devices, the system can reroute waste when bins are full, update behavior with policy changes, and provide real-time educational feedback to users. In a federated network, these agents enable dynamic waste collection, reduce contamination, and enhance operational efficiency, offering a scalable pathway toward sustainable, circular urban economies.

Keyphrases: context aware ai, federated intelligent agents, iot enabled bin monitoring, large language models (llm) on edge, policy driven decision making, real time object detection, smart waste management, waste sorting automation, yolov5/yolov8

In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 98-102.

Links:	https://easychair.org/publications/paper/Xddq
	https://doi.org/10.29007/8gn4

BibTeX entry

@inproceedings{AIAS2025:Edge_AI_Agent_Design,
  author    = {Binrong Zhu and Ruxue Jin and Yang Liu and Guiran Liu and Qun Wang and Phuong Mai Nguyen},
  title     = {Edge AI Agent Design for Policy-Aware Urban Waste Management},
  booktitle = {Proceedings of AI for Accelerated Research Symposium},
  editor    = {Jernej Masnec and Hamid Reza Karimian and Parisa Kordjamshidi and Yan Li},
  series    = {EPiC Series in Technology},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2322},
  url       = {/publications/paper/Xddq},
  doi       = {10.29007/8gn4},
  pages     = {98-102},
  year      = {2026}}

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