Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Edge learning: The enabling technology for distributed big data analytics in the edge
Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …
smart city, which are fundamentally altering the way individuals and organizations live, work …
A learning-based incentive mechanism for federated learning
Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …
learning models are often built on these data, to enable the detection, classification, and …
Reliable distributed computing for metaverse: A hierarchical game-theoretic approach
The metaverse is regarded as a new wave of technological transformation that provides a
virtual space for people to interact through digital avatars. To achieve immersive user …
virtual space for people to interact through digital avatars. To achieve immersive user …
Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning
J Wang, C Guo, C Yu, Y Liang - Electric power systems research, 2022 - Elsevier
Virtual power plants (VPPs), which aggregate customer-side flexibility resources, provide an
effective way for customers to participate in the electricity market, and provide a variety of …
effective way for customers to participate in the electricity market, and provide a variety of …
Online altitude control and scheduling policy for minimizing AoI in UAV-assisted IoT wireless networks
This article considers unmanned aerial vehicle (UAV) assisted Internet of Things (IoT)
networks, where low resource IoT devices periodically sample a stochastic process and …
networks, where low resource IoT devices periodically sample a stochastic process and …
An incentive mechanism for privacy-preserving crowdsensing via deep reinforcement learning
With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and
computing capabilities increases dramatically, paving the way toward an emerging …
computing capabilities increases dramatically, paving the way toward an emerging …
Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: A three-stage stackelberg game approach
In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where
mobile social-aware workers have unknown qualities and can share their answers to tasks …
mobile social-aware workers have unknown qualities and can share their answers to tasks …
Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning
Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart
mobile devices to collect and share data. Traditional MCS systems mainly leverages the …
mobile devices to collect and share data. Traditional MCS systems mainly leverages the …
TVD-RA: A truthful data value discovery based reverse auction incentive system for mobile crowd sensing
Emerging crowdsensing paradigm enables a large number of sensing applications, where
much attention is drawn to the fundamental problems for maximizing the system utility and …
much attention is drawn to the fundamental problems for maximizing the system utility and …