An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System

Abstract

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments.