An energy-efficient fine-grained deep neural network partitioning scheme for wireless collaborative fog computing
Fog computing is a potential solution for heterogeneous resource-constrained mobile
devices to collaboratively operate deep learning-driven applications at the edge of the …
devices to collaboratively operate deep learning-driven applications at the edge of the …
Fog-assisted multiuser SWIPT networks: Local computing or offloading
This paper investigates a fog computing-assisted multiuser simultaneous wireless
information and power transfer network, where multiple sensors with power splitting (PS) …
information and power transfer network, where multiple sensors with power splitting (PS) …
Dynamic offloading for energy harvesting mobile edge computing: architecture, case studies, and future directions
Mobile edge computing (MEC) is envisioned as a new paradigm by integrating the mobile
computing functionality into 5G wireless networks, aiming at empowering communication …
computing functionality into 5G wireless networks, aiming at empowering communication …
Multi-domain resource scheduling for simultaneous wireless computing and power transfer in fog radio access network
Future 6G is deemed to provide Simultaneous Wireless cOmputing and Power Transfer
(SWOPT) services for addressing the shortage of local computation and that of energy at …
(SWOPT) services for addressing the shortage of local computation and that of energy at …
A Relay-Assisted Communication Scheme for Collaborative On-Device CNN Execution Considering Hybrid Parallelism
Deep learning (DL) has gained increasing prominence in latency-critical artificial
intelligence (AI) applications. Due to the intensive computational requirements of these …
intelligence (AI) applications. Due to the intensive computational requirements of these …
Monitoring multivariate coefficient of variation for high‐dimensional processes
Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process
relative variability. They are developed on the assumption that the process subgroup size …
relative variability. They are developed on the assumption that the process subgroup size …
Optimization of energy consumption in the MEC-assisted multi-user FD-SWIPT system
J Fu, J Hua, J Wen, H Chen, W Lu, J Li - IEEE Access, 2020 - ieeexplore.ieee.org
That alleviating the heavy computing task, improving spectral efficiency and prolonging
battery lifetime have been the key design challenges in Internet of Things (IoT) and …
battery lifetime have been the key design challenges in Internet of Things (IoT) and …
Energy-efficient computation offloading and resource allocation in SWIPT-based MEC Networks
E Xuefei, Z Ma, K Yu - IEEE Access, 2020 - ieeexplore.ieee.org
Recent years, 5G networks have become an important role in accelerating the development
of social intelligence. But it also increases energy consumption and data flow. In order to …
of social intelligence. But it also increases energy consumption and data flow. In order to …
A reinforcement learning algorithm for resource provisioning in mobile edge computing network
Mobile edge computing (MEC) is a model that allows integration of computing power into
telecommunications networks, to improve communication and data processing efficiency. In …
telecommunications networks, to improve communication and data processing efficiency. In …
[HTML][HTML] Joint offloading and energy harvesting design in multiple time blocks for FDMA based wireless powered MEC
Z Yu, G Xu, Y Li, P Liu, L Li - Future Internet, 2021 - mdpi.com
The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is
recognized as a promising technology to solve the problem of limited battery capacities and …
recognized as a promising technology to solve the problem of limited battery capacities and …