IoT-based Route Recommendation for an Intelligent Waste Management System

TL;DR

This paper presents an AI-driven route recommendation system for IoT-enabled waste management in smart cities, optimizing collection routes based on sensor data and spatial constraints to improve efficiency.

Contribution

It introduces a novel multi-level decision-making approach that integrates IoT sensor data with AI models for optimized waste collection routing.

Findings

AI models outperform traditional routing methods

Sensor data improves route efficiency

Enhanced decision-making in waste management systems

Abstract

The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial Intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compares their corresponding results. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system.