A machine learning for environmental noise classification in smart cities

Many people may not be aware of the adverse effects of noise pollution on their health which include hearing impairment, negative social behaviour, anxiety, sleep disturbances and intelligibility to understand speech. Machine learning (ML) is the concept of making the machine determines, classifies, and does operations without being explicitly programmed. It is used in many fields such as intelligent transportation system and autonomous driving. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively, little research was done towards recognizing non-speech environmental sounds. For this reason, this project aims to develop an ML based classifier of sounds originated from the environment and compares the sound levels with the recommended levels by international standards via a created Graphical User Interface (GUI). Noise Capture mobile application will be used to record four sources of environmental noise, that are from highway, railway, lawn mowers and birds. Then, Python programming will be used to simulate the classification model using Scikit-learn. The trained data entered Scikit-learn gathered from Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Bootstrap Aggregation (Bagging) and Random Forest (RF) classifiers, as well as Artificial Neural Network (ANN) algorithm from Keras and TensorFlow libraries for comparative performances in the accuracy test. In addition to ML, a noise pollution survey is conducted to provide qualitative analysis of community perceptions. The findings of ML are presented in terms of confusion matrix, accuracy, precision, recall and F1 score. The results show that the noise classification accuracy for all models exceeded 95%. The best ML models are RF and ANN due to its high accuracy and the least computational time. The findings of survey are also presented, which indicates that there is no correlation between gender, age, location with knowledge of noise pollution and the effect of noise on people. People are bothered by noise regardless of their age and gender

A custom sensor network for autonomous water quality assessment in fish farms

Producción CientíficaThe control of water quality is crucial to ensure the survival of fish in aquaculture production facilities. Today, the combination of sensors with communication technologies permits to monitor these crucial parameters in real-time, allowing to take fast management decisions. However, out-of-the-box solutions are expensive, due to the small market and the industrial nature of sensors, besides being little customizable. To solve this, the present work describes a low-cost hardware and software architecture developed to achieve the autonomous water quality assessment and management on a remote facility for fish conservation aquaculture within the framework of the Smart Comunidad Rural Digital (smartCRD) project. The developed sensor network has been working uninterruptedly since its installation (20 April 2021). It is based on open source technology and includes a central gateway for on-site data monitoring of water quality nodes as well as an online management platform for data visualization and sensor network configuration. Likewise, the system can detect autonomously water quality parameters outside configurable thresholds and deliver management alarms. The described architecture, besides low-cost, is highly customizable, compatible with other sensor network projects, machine-learning applications, and is capable of edge computing. Thus, it contributes to making open sensorization more accessible to real-world applications.Torres Quevedo (grant PTQ2018-010162

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications