Backscatter-assisted computation offloading for energy harvesting IoT devices via policy-based deep reinforcement learning
Wireless Internet of Things (IoT) devices can be deployed for data acquisition and decision
making, eg, the wearable sensors used for healthcare monitoring. Due to limited …
making, eg, the wearable sensors used for healthcare monitoring. Due to limited …
Joint optimization of data offloading and resource allocation with renewable energy aware for IoT devices: A deep reinforcement learning approach
H Ke, J Wang, H Wang, Y Ge - IEEE Access, 2019 - ieeexplore.ieee.org
A large number of connected sensors and devices in Internet of Things (IoT) can generate
large amounts of computing data and increase massive energy consumption. Real-time …
large amounts of computing data and increase massive energy consumption. Real-time …
Learning-based computation offloading for IoT devices with energy harvesting
Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy
harvesting (EH) to provide high-level experiences for computational intensive applications …
harvesting (EH) to provide high-level experiences for computational intensive applications …
Computation offloading in energy harvesting systems via continuous deep reinforcement learning
As a promising technology to improve the computation experience for mobile devices,
mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous …
mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous …
Optimal computation resource allocation in energy-efficient edge IoT systems with deep reinforcement learning
This paper investigates a computation resource optimization problem of mobile edge
computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) …
computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) …
Backscatter-assisted data offloading in OFDMA-based wireless-powered mobile edge computing for IoT networks
Mobile-edge computing (MEC) has emerged as a prominent technology to overcome
sudden demands on computation-intensive applications of the Internet of Things (IoT) with …
sudden demands on computation-intensive applications of the Internet of Things (IoT) with …
Energy efficient edge computing: When lyapunov meets distributed reinforcement learning
In this work, we study the problem of energy-efficient computation offloading enabled by
edge computing. In the considered scenario, multiple users simultaneously compete for …
edge computing. In the considered scenario, multiple users simultaneously compete for …
Computation offloading in energy harvesting powered MEC network
Mobile edge computing (MEC) is a promising technique which migrates computational
intensive tasks from smart devices to edge servers, so as to increase the computational …
intensive tasks from smart devices to edge servers, so as to increase the computational …
Power control in energy harvesting multiple access system with reinforcement learning
The Internet of Things (IoT) application has a crucial need for long-term and self-sustainable
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
Dynamic computation offloading with energy harvesting devices: A hybrid-decision-based deep reinforcement learning approach
Mobile-edge computing (MEC) with energy harvesting (EH) is becoming an emerging
paradigm to improve the computation experience for the Internet-of-Things (IoT) devices. For …
paradigm to improve the computation experience for the Internet-of-Things (IoT) devices. For …