ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

IoT-based filter management system using reinforced ANFIS for VOCs reduction in urban industrial facilities

This study proposes a low-cost, IoT-based multi-sensor system for monitoring volatile organic compound (VOC) emissions and predicting activated carbon filter replacement in small-scale industrial settings. Sensor modules composed of low-cost VOC sensors were installed at the exhaust of adsorption towers to enable real-time monitoring. To improve measurement accuracy, a Reinforced Adaptive Neuro-Fuzzy Inference System (RANFIS) was developed for VOC concentration prediction, incorporating dynamic outlier detection and correction. Based on RANFIS outputs, a Decision Tree (DT) model estimates the breakthrough point of activated carbon filters to support timely replacement. The system was deployed in an actual automotive painting facility using eight sensor modules with three sensor types. RANFIS outperformed Deep Neural Network (DNN) and conventional ANFIS models, improving RMSE by up to 82.4%. The DT model also achieved over 80% accuracy in predicting filter replacement under different efficiency thresholds. This integrated approach enables real-time, autonomous filter maintenance using economical sensor hardware, providing a scalable solution for VOC management. The proposed system supports more efficient and sustainable operation of emission control systems in small industrial sites.

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

The automatic weather system serves to inform farmers, tourists, planners, and others with precise information to help them take the appropriate action. Today, with the advancement of smart technologies, the system has evolved into many sensing methods to gather real-time climate data. This article investigates the modeling and implementation of a low-cost weather station device that also functions to measure air quality. The proposed system based on the Internet of Things (IoT) allows access to real-time climate data for a given area. This system monitors environmental conditions such as ambient temperature, humidity, atmospheric pressure, altitude, and levels of harmful atmospheric gases like CO2 and NO2. This real-time telemetry device uses MQ-135, DHT-11 and BMP280 sensors to gather data. The ESP32 board processes the obtained data from all sensors. Additionally, we present a model for a fuzzy inference system (FIS) that performs parameter categorization using a reasoning procedure and incorporates the results into an air quality index (AQI) that describes the levels of pollution for Al Hoceima city. The FIS takes CO2 and NO2 values as input and returns the AQI. The AQI for Al Hoceima city is categorized into six levels: Excellent, Good, Regular, Bad, Dangerous, and Very Dangerous. Furthermore, the suggested system's block hardware employs the Message Queuing Telemetry Transport (MQTT) protocol to broadcast collected data to a mobile and web application via the Internet. The suggested IoT-embedded device was tested in real life, and the results were promising.

Privacy-Preserving Data Mining on Blockchain-Based WSNs

Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution.