Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends

MEE Alahi, A Sukkuea, FW Tina, A Nag… - Sensors, 2023 - mdpi.com
As the global population grows, and urbanization becomes more prevalent, cities often
struggle to provide convenient, secure, and sustainable lifestyles due to the lack of …

Five facets of 6G: Research challenges and opportunities

LH Shen, KT Feng, L Hanzo - ACM Computing Surveys, 2023 - dl.acm.org
While the fifth-generation systems are being rolled out across the globe, researchers have
turned their attention to the exploration of radical next-generation solutions. At this early …

Federated learning in smart city sensing: Challenges and opportunities

JC Jiang, B Kantarci, S Oktug, T Soyata - Sensors, 2020 - mdpi.com
Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

A comprehensive survey on UAV communication channel modeling

C Yan, L Fu, J Zhang, J Wang - IEEE Access, 2019 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic
community and the industrial community due to their diverse military applications and …

Reliable computation offloading for edge-computing-enabled software-defined IoV

X Hou, Z Ren, J Wang, W Cheng, Y Ren… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications
have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing …

RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey

J Liu, M Ahmed, MA Mirza, WU Khan… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …

Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning

J Shi, J Du, J Wang, J Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Vehicular fog computing (VFC) has been expected as a promising scheme that can increase
the computational capability of vehicles without relying on servers. Comparing with …

A survey on security attacks in VANETs: Communication, applications and challenges

M Arif, G Wang, MZA Bhuiyan, T Wang… - Vehicular Communications, 2019 - Elsevier
Over the past few decades, the intelligent transportation system (ITS) have emerged with
new technologies and becomes the data-driven ITS, because the substantial amount of data …

Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications

X Li, L Lu, W Ni, A Jamalipour… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Dynamic topology, fast-changing channels and the time sensitivity of safety-related services
present challenges to the status quo of resource allocation for cellular-underlaying vehicle …

Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning

H Zhu, Q Wu, XJ Wu, Q Fan, P Fan… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Vehicular edge computing (VEC) is envisioned as a promising approach to process the
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …