Article
Computer Science, Hardware & Architecture
Maryam Songhorabadi, Morteza Rahimi, AmirMehdi MoghadamFarid, Mostafa Haghi Kashani
Summary: The development of smart cities heavily relies on advanced computing paradigms, such as fog computing, to address the requirements of location-aware, latency-sensitive, and security-crucial applications. However, the frequently used cloud-based approaches in smart cities restrict the security, time-sensitive services, flexibility, and reliability. This paper proposes a study to explore the state-of-the-art fog-based approaches in smart cities and presents a classification of these approaches into service-based, resource-based, and application-based classes.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Rang Zhou, Xiaosong Zhang, Xiaofen Wang, Guowu Yang, Nadra Guizani, Xiaojiang Du
Summary: The smart city concept aims to improve the management level of modern cities, with health management being a crucial aspect. Hospitals play a significant role in providing high-quality medical services through accurate patient health data analysis. Protecting data privacy is important when sharing patient health data for precise diagnosis and efficient treatment plans.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Armin Razmjoo, Amirhossein Gandomi, Maral Mahlooji, Davide Astiaso Garcia, Seyedali Mirjalili, Alireza Rezvani, Sahar Ahmadzadeh, Saim Memon
Summary: As smart cities emerge, the Internet of Things plays a crucial role in sectors such as environment monitoring, public transport, utilities, street lighting, waste management, public safety, and smart parking, enhancing city efficiency and improving quality of life.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Fariza Sabrina, Julian Jang-Jaccard
Summary: Smart cities utilize IoT devices to collect and analyze data for infrastructure improvement, but security concerns remain. This study introduces a blockchain-based entitlement-based access control architecture for secure data sharing in IoT environments.
Article
Computer Science, Information Systems
Zhentao Huang, Yuyang Peng, Jun Li, Fei Tong, Konglin Zhu, Limei Peng
Summary: This study proposes a scheme using artificial noise and antenna selection technology for space shift keying to enhance the security performance of IoT applications. By employing appropriate cancellation technology and signal selection methods, the security performance of the communication system can be significantly improved.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Qi Luo, Dongxiao Yu, Yanwei Zheng, Hao Sheng, Xiuzhen Cheng
Summary: This study investigates the use of deep graph generative models in simulating large-scale Internet of Things (IoT) networks and presents a variable graph autoencoder called Core-GAE that considers the coreness of nodes during network generation. Core-GAE preserves both local proximity similarity and global structural features when learning the structural features of graphs.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Mathematics, Interdisciplinary Applications
R. Carreno Aguilera, M. Patino Ortiz, J. Patino Ortiz, A. Acosta Banda
Summary: Blockchain technology, despite its seemingly trivial nature, has attracted significant investment and shows potential for various applications. This paper discusses an expert system utilizing smart contracts and neural networks to optimize sensor distribution and take actions based on user preferences.
FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY
(2021)
Article
Construction & Building Technology
Rami D. Orejon-Sanchez, David Crespo-Garcia, Jose R. Andres-Diaz, Alfonso Gago-Calderon
Summary: This study examines the evolution of Spanish cities towards digital transformation and the need for evaluation in order to better utilize funding and resources for future development.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Computer Science, Information Systems
Mohammad Aminul Hoque, Mahmud Hossain, Shahid Noor, S. M. Riazul Islam, Ragib Hasan
Summary: The Internet of Things (IoT) enables new smart city services through digital devices, but deployment and management tasks can be difficult and expensive. A drone-based service framework is proposed to dynamically configure IoT devices, reducing costs and increasing device utilization.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Review
Automation & Control Systems
Othmane Friha, Mohamed Amine Ferrag, Lei Shu, Leandros Maglaras, Xiaochan Wang
Summary: This paper comprehensively reviews emerging technologies for IoT-based smart agriculture, including UAVs, wireless technologies, and cloud computing. It provides a classification of IoT applications for smart agriculture and discusses the latest methods for supply chain management based on blockchain technology. Real projects using these technologies are showcased, along with open research challenges and future research directions for agricultural IoTs.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Mathematics
Abdulaziz Aldribi, Aman Singh
Summary: This study proposes a blockchain-empowered decentralized and scalable solution for a sustainable smart-city network using technologies such as IoT, fog nodes, permissioned trust chain, smart contract, blockchain, and IPFS. It improves performance, scalability, and distribution for a sustainable smart-city network.
Review
Chemistry, Analytical
Md Whaiduzzaman, Alistair Barros, Moumita Chanda, Supti Barman, Tania Sultana, Md. Sazzadur Rahman, Shanto Roy, Colin Fidge
Summary: Smart cities utilize various components and emerging technologies to enhance city administration and resident services. IoT communications play a crucial role in smart city operations. This paper provides an overview of smart cities' concepts, characteristics, and applications, and discusses the challenges and possibilities, as well as solutions, in recent technological trends such as machine learning and blockchain. It also highlights the importance of security and privacy aspects, including blockchain applications, in building trustworthy and resilient smart cities. The impact of recent emerging technologies on challenges, applications, and solutions for futuristic smart cities is outlined.
Review
Computer Science, Information Systems
Himanshu Sharma, Ahteshamul Haque, Frede Blaabjerg
Summary: Artificial intelligence and machine learning technologies have great potential in efficiently managing IoT nodes in smart cities, which are often based on low power Bluetooth and wireless sensor network standards. The study suggests using machine learning methods to optimize WSN-IoT nodes in smart cities, with supervised learning algorithms being the most widely used in smart city applications.
Article
Computer Science, Information Systems
Naercio Magaia, Ramon Fonseca, Khan Muhammad, Afonso H. Fontes N. Segundo, Aloisio Vieira Lira Neto, Victor Hugo C. de Albuquerque
Summary: The significant evolution of the Internet of Things has led to the development of smart city devices that have replaced manual labor, increasing efficiency and intelligence in cities. However, the increased sensitivity of data, especially in the industrial sector, has attracted hackers targeting Industrial IoT devices or networks, leading to a rise in the number of malware infections. This article discusses the concept and applications of IIoT in smart cities, as well as the security challenges faced in this emerging area, along with available deep learning techniques to enhance IIoT security.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Green & Sustainable Science & Technology
Seunghwan Myeong, Khurram Shahzad
Summary: The study highlights the importance of innovative leadership in monitoring air pollution using data and citizen volunteers, proposing energy-efficient and cleaner air quality solutions for smart cities, and exploring the effectiveness of data-driven approaches and citizen participation in pollution management in smart cities.
Article
Environmental Sciences
Xuening Qin, Tien Huu Do, Jelle Hofman, Esther Rodrigo Bonet, Valerio Panzica La Manna, Nikos Deligiannis, Wilfried Philips
Summary: Urban air quality mapping plays a crucial role in urban planning, air pollution control, and personal air pollution exposure assessment. Traditional fixed monitoring stations are limited in providing fine-grained air quality maps due to their sparse deployment and inability to capture short-distance variations influenced by factors such as meteorology, road network, and traffic flow. In this study, a context-aware locally adapted deep forest (CLADF) model is proposed to infer the distribution of NO2 with high resolution using measurements from low-cost mobile sensors and contextual factors, particularly traffic flow. The CLADF model outperforms various benchmark models in terms of accuracy and correlation according to extensive validation experiments using mobile NO2 measurements collected in Antwerp, Belgium.
Article
Environmental Sciences
Jelle Hofman, Jan Peters, Christophe Stroobants, Evelyne Elst, Bart Baeyens, Jo Van Laer, Maarten Spruyt, Wim Van Essche, Elke Delbare, Bart Roels, Ann Cochez, Evy Gillijns, Martine Van Poppel
Summary: The study evaluated the usability and accuracy of commercial low-cost air quality sensor systems in real-world settings. Through local re-calibration, the accuracy was improved to meet data quality standards, and a methodological setup was proposed for quantifying air quality impacts from local policy interventions and traffic measures.
Article
Engineering, Electrical & Electronic
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
Summary: This paper proposes a method for multimodal image restoration and fusion, using a coupled convolutional sparse coding problem and the principle of deep unfolding to design the models. Compared to traditional approaches, the proposed models achieve better performance in accurate image reconstruction.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Environmental Sciences
Robin Lasters, Thimo Groffen, Marcel Eens, Dries Coertjens, Wouter A. Gebbink, Jelle Hofman, Lieven Bervoets
Summary: Humans are exposed to PFAS through their diet, and this study revealed that home-produced eggs may be a significant source of PFAS intake. The concentration of PFAS in eggs decreased with distance from a fluorochemical plant, and the type of feed and age of the chickens also influenced the PFAS concentrations. Based on a moderate consumption scenario, the European health guideline for PFAS was exceeded in a majority of the locations examined.
Article
Computer Science, Artificial Intelligence
Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis, Nikos Deligiannis
Summary: The study proposes a gradient compression method based on distributed learning, which improves compression efficiency by leveraging inter-node gradient correlations. Experimental results show significant compression effects across different datasets and deep learning models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yuqing Yang, Peng Xiao, Nikos Deligiannis
Summary: This paper presents a new method for underwater source localization by combining the matched field processing method (MFP) with 1-bit compressive sensing (1-bit CS). The Fixed Point Continuation (FPC) method and a deep neural network (DNN) are used to solve the 1-bit recovery problem and evaluate their performance in source localization. Additionally, a simple average technique is proposed to improve the robustness of signal recovery to noise added in the binary measurements.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Xiangyu Yang, Giannis Bekoulis, Nikos Deligiannis
Summary: This study addresses the problem of detecting traffic events using Twitter and proposes two subtasks: a text classification subtask to identify whether a tweet is traffic-related or not, and a slot filling subtask to extract fine-grained information about the traffic event. Experimental results show that the proposed deep learning methods achieve high performance scores (95%+ F1 score) on the constructed datasets for both subtasks, even in a transfer learning scenario. The Dutch Traffic Twitter datasets from Belgium and the Brussels capital region, as well as the code, are available on GitHub.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Environmental Sciences
Joanna A. Kaminska, Tomasz Turek, Martine Van Poppel, Jan Peters, Jelle Hofman, Jan K. Kazak
Summary: Studying the air quality and exposure to pollution in urban areas is crucial for creating more sustainable cities. In Poland, monitoring of black carbon (BC) concentration is not part of the air quality monitoring network. Results show that urban greenery and surrounding infrastructure influence BC concentrations, with higher levels near main roads in the city center. However, these findings are based on preliminary short-term field campaigns.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Lusine Abrahamyan, Nikos Deligiannis
Summary: This paper presents an efficient Entropy-based Patch Encoder (EPE) module for resource-constrained semantic segmentation, which utilizes different numbers of parameters in encoders based on entropy levels in image patches to boost performance while reducing computational cost.
2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP
(2022)
Meeting Abstract
Regional & Urban Planning
Elizabeth Cooper, Yan Wang, Samuel Stamp, Tamara Nijsen, Pascal de Graaf, Jelle Hofman
JOURNAL OF PLANNING LITERATURE
(2022)
Proceedings Paper
Acoustics
Esther Rodrigo Bonet, Tien Huu Do, Xuening Qin, Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips, Nikos Deligiannis
Summary: This paper presents a deep-learning-based air quality forecasting model that addresses the issue of data scarcity by optimizing spatio-temporal correlations and leveraging contextual data, resulting in improved prediction performance.
2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022)
(2022)
Proceedings Paper
Acoustics
Tanmoy Mukherjee, Nikos Deligiannis
Summary: This paper investigates the problem of discovering novel classes never encountered in the labeled set, and proposes a dependency measure based on Squared Mutual Information (SMI) to simultaneously learn to classify and cluster the data. Experimental results show competitive performance on CIFAR and Imagenet datasets.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Article
Engineering, Electrical & Electronic
Esther Rodrigo Bonet, Tien Huu Do, Xuening Qin, Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips, Nikos Deligiannis
Summary: This paper presents a novel post-hoc explainability framework for GNN-based models, which is especially important in health-related applications such as air pollution estimation. The proposed topology-aware kernelised node selection method and topological node embedding method effectively capture the graph topology and the relevance between nodes.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2022)