Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends

AKMB Haque, B Bhushan, G Dhiman - Expert Systems, 2022 - Wiley Online Library
The emergence of smart cities and sustainable development has become a globally
accepted form of urbanization. The epitome of smart city development has become possible …

[HTML][HTML] Deep learning for geological hazards analysis: Data, models, applications, and opportunities

Z Ma, G Mei - Earth-Science Reviews, 2021 - Elsevier
As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

SB Atitallah, M Driss, W Boulila, HB Ghézala - Computer Science Review, 2020 - Elsevier
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …

Deep reinforcement learning for Internet of Things: A comprehensive survey

W Chen, X Qiu, T Cai, HN Dai… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

Tinyml-enabled frugal smart objects: Challenges and opportunities

R Sanchez-Iborra, AF Skarmeta - IEEE Circuits and Systems …, 2020 - ieeexplore.ieee.org
The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms
within small objects powered by Microcontroller Units (MCUs). This paves the way for the …

How to build a graph-based deep learning architecture in traffic domain: A survey

J Ye, J Zhao, K Ye, C Xu - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Smart City Data Science: Towards data-driven smart cities with open research issues

IH Sarker - Internet of Things, 2022 - Elsevier
Cities are undergoing huge shifts in technology and operations in recent days, and 'data
science'is driving the change in the current age of the Fourth Industrial Revolution (Industry …

Barriers to artificial intelligence adoption in smart cities: A systematic literature review and research agenda

AB Rjab, S Mellouli, J Corbett - Government Information Quarterly, 2023 - Elsevier
Artificial intelligence (AI) plays a prominent role in smart cities' development and offers
benefits to different services such as finance, healthcare, security, agriculture, transport …

A survey on deep learning for human mobility

M Luca, G Barlacchi, B Lepri… - ACM Computing Surveys …, 2021 - dl.acm.org
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

Deep reinforcement learning for autonomous internet of things: Model, applications and challenges

L Lei, Y Tan, K Zheng, S Liu, K Zhang… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …