A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning
Offloading assists in overcoming the resource constraints of specific elements, making it one
of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery …
of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery …
A QoS-aware technique for computation offloading in IoT-edge platforms using a convolutional neural network and Markov decision process
Offloading is one of the critical enablers of the Internet of Things (IoT) as it helps overcome
the resource limitations of individual objects. Offering enough computational power for IoT …
the resource limitations of individual objects. Offering enough computational power for IoT …
Dynamic edge computation offloading for Internet of Things with energy harvesting: A learning method
Z Wei, B Zhao, J Su, X Lu - IEEE Internet of Things Journal, 2018 - ieeexplore.ieee.org
Mobile edge computing (MEC) has recently emerged as a promising paradigm to meet the
increasing computation demands in Internet of Things (IoT). However, due to the limited …
increasing computation demands in Internet of Things (IoT). However, due to the limited …
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 …
Smart computational offloading for mobile edge computing in next-generation Internet of Things networks
Limited battery and computing resources of mobile devices (MDs) induce performance
limitations in mobile edge computing (MEC) networks. Computational offloading has the …
limitations in mobile edge computing (MEC) networks. Computational offloading has the …
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 …
Autonomic computation offloading in mobile edge for IoT applications
Computation offloading is a protuberant elucidation for the resource-constrained mobile
devices to accomplish the process demands high computation capability. The mobile cloud …
devices to accomplish the process demands high computation capability. The mobile cloud …
A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios
To fulfill people's expectations for smart and user-friendly Internet of Things (IoT)
applications, the quantity of processing is fast expanding, and task latency constraints are …
applications, the quantity of processing is fast expanding, and task latency constraints are …
A deep learning approach for energy efficient computational offloading in mobile edge computing
Mobile edge computing (MEC) has shown tremendous potential as a means for
computationally intensive mobile applications by partially or entirely offloading computations …
computationally intensive mobile applications by partially or entirely offloading computations …
Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning
Internet of Things (IoT) is considered as the enabling platform for a variety of promising
applications, such as smart transportation and smart city, where massive devices are …
applications, such as smart transportation and smart city, where massive devices are …