Collection

Big Data Analytics based Artificial Intelligence for Environment and Sustainability

Submission status
Closed

The concept of environmental and sustainability aims to satisfy present-day needs for housing, working environments, and infrastructure without compromising the ability of future generations to meet their own needs in times to come. With the advent of the Internet of Things (IoT) and Big Data Analytics, there is a huge paradigm shift in how environmental data are used for sustainable cities and societies, especially by applying intelligent algorithms. Energy savings at BBVA’s headquarters are 5,766,731 kWh a year, equivalent to 1,900 households, which also reduces CO2 emission by a 1,430-metric ton per year. The building’s sustainable design and equipment are not solely responsible for this success: tools powered by artificial intelligence have also been used to optimize energy usage.

One of the most significant challenges facing humanity, and hence the Big Data scientist face, is not obtaining the data but properly use an existing intelligent algorithm. For example, when there is sufficient natural light coming into the work areas, the lights near the windows can adapt, reducing the light they emit, which can be achieved with a simple bio-inspired algorithm. On the other hand, with intelligent sensors measuring temperatures, air pollutants, and water poising in urban and rural areas, we can determine the precise time for adequately treating the air or water and raise an alert when certain thresholds are exceeded. These air/water treatment units are responsible for letting air/water from outside and feeding the climate control system. Artificial intelligence and green algorithms contribute to improved energy efficiency and the achievement of sustainable development goals.

This Topical Collection covers pure research and applications within the novel scopes related to sustainability computing, such as smart devices, smart homes, smart cities, smart transportation, smart environments, and smart grids will be deeply impacted by the research progress. The potential topics include, but are not limited

Artificial Intelligence-Based Environment Quality Analysis System

Environmental analytics using AI/Big Data technologies to increase data reliability and accuracy

Natural inspired computing techniques for environmental monitoring

Green computing techniques in waste management

Applications of deep learning and unsupervised feature learning for prediction of sustainable feature smart transportation system

AI-based Environmental sensor for air pollution monitoring and control Ambient intelligent techniques for sustainable transport

Intelligent techniques in sustainable digital environments

Green data analytic centers powered by renewable energy systems

Machine Learning Methods for smart environments and urban networking Artificial Neural Network (RNN/CNN/Deep learning) for monitoring and optimization of transportation systems to improve fuel efficiency and reduce pollution

IoT for real-time environmental awareness for firefighters

Green Computing for Sustainable planning and city configuration evaluation

Editors

  • Sadia Din Sadia Din , 

    Sadia Din

    Dr. Sadia Din, School of Computer Science, Kyungpook National University, South Korea Email: sadia.din@knu.ac.kr Leading guest editor

  • Ouri Wolfson Ouri Wolfson , 

    Ouri Wolfson

    Prof. Dr. Ouri Wolfson, Richard and Loan Hill Professor of Computer Science, University of Illinois at Chicago, USA

  • Carlos Alberto Kamienski Carlos Alberto Kamienski , 

    Carlos Alberto Kamienski

    Prof. Dr. Carlos Alberto Kamienski, Federal University of ABC (UFABC), Brazil Email: carlos.kamienski@ufabc.edu.br

  • Andrés Muñoz Andrés Muñoz , 

    Andrés Muñoz

    Dr. Andrés Muñoz, Profesor en Grado de Ingeniería Informática/Full Lecturer in Computer Science UCAM Universidad Católica de Murcia Email: amunoz@ucam.edu

  • Federico Cugurullo Federico Cugurullo , 

    Federico Cugurullo

    Dr. Federico Cugurullo, Department of Geography, Trinity College Dublin, Ireland Email : CUGURULF@tcd.ie

  • Georgios N. Kouziokas Georgios N. Kouziokas  &

    Georgios N. Kouziokas

    Dr. Georgios N. Kouziokas, School of Engineering, University of Thessaly, Greece Email : gekouzio@uth.gr

  • Sinan Q.Salih Sinan Q.Salih

    Sinan Q.Salih

    Dr. Sinan Q.Salih, Department of Computer Science and Engineering, Duy Tan University, Vietnam Email: sinanq.salih@duytan.edu.vn

Articles (10 in this collection)