Resource management at the network edge: A deep reinforcement learning approach

D Zeng, L Gu, S Pan, J Cai, S Guo - IEEE Network, 2019 - ieeexplore.ieee.org
With the advent of edge computing, it is highly recommended to extend some cloud services
to the network edge such that the services can be provisioned in the proximity of end users …

Reinforcement learning-based routing protocols for vehicular ad hoc networks: A comparative survey

RA Nazib, S Moh - IEEE Access, 2021 - ieeexplore.ieee.org
Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in
road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high …

Reinforcement-learning-based resource allocation for energy-harvesting-aided D2D communications in IoT networks

A Omidkar, A Khalili, HH Nguyen… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
This article proposes a novel approach to improve the energy efficiency (EE) of an energy-
harvesting (EH)-enabled IoT network supported by simultaneous wireless information and …

An energy-efficient routing protocol with reinforcement learning in software-defined wireless sensor networks

D Godfrey, BK Suh, BH Lim, KC Lee, KI Kim - Sensors, 2023 - mdpi.com
The enormous increase in heterogeneous wireless devices operating in real-time
applications for Internet of Things (IoT) applications presents new challenges, including …

An energy-efficient routing algorithm based on greedy strategy for energy harvesting wireless sensor networks

S Hao, Y Hong, Y He - Sensors, 2022 - mdpi.com
Energy harvesting wireless sensor network (EH-WSN) is considered to be one of the key
enabling technologies for the internet of things (IoT) construction. Although the introduced …

[HTML][HTML] The development of green wireless mesh network: A survey

Y Chai, XJ Zeng - Journal of Smart Environments and Green …, 2021 - oaepublish.com
Wireless mesh network (WMN) is a type of self-healing, self-configuration, and peer-to-peer
wireless network. Without expensive and fixed base stations, WMN can be established fast …

Routing selection with reinforcement learning for energy harvesting multi-hop CRN

X He, H Jiang, Y Song, C He, H **ao - IEEE Access, 2019 - ieeexplore.ieee.org
This paper considers the routing problem in the communication process of an energy
harvesting (EH) multi-hop cognitive radio network (CRN). The transmitter and the relay …

A new energy‐aware method for load balance managing in the fog‐based vehicular ad hoc networks (VANET) using a hybrid optimization algorithm

R Qun, SM Arefzadeh - IET Communications, 2021 - Wiley Online Library
Abstract Fog‐based VANETs (Vehicular Ad hoc NETworks) is a new model with vehicular
cloud and fog computing benefits. Fog‐based VANETs consist of a series of mobile nodes …

The future of wireless mesh network in next-generation communication: a perspective overview

Y Chai, XJ Zeng, Z Liu - Evolving Systems, 2024 - Springer
Wireless mesh network (WMN) which evolves from ad-hoc network is a type of self-healing,
self-configuration, and multi-hop wireless network. Without expensive and fixed base …

A multi-featured actor-critic relay selection scheme for large-scale energy harvesting WSNs

T Wang, S Wu, Z Wang, Y Jiang, T Ma… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
In this letter, we focus on the design of a relay selection scheme in large-scale energy-
harvesting wireless sensor networks. Considering the dynamic nature required for practical …