Results 61 to 70 of about 3,846,226 (201)
Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing [PDF]
, 2020 © 2019 Binbin Huang et al. With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications.
Huang, B +6 more
core +1 more source
Survey on rail transit mobile edge computing network security
Tongxin xuebao, 2023 The introduction of mobile edge computing (MEC) technology in rail transit which has the characteristics of complex environment, high densities of passengers, and high-speed mobility can meet the low latency, mobility, and massive connection requirements
Renchao XIE +5 more
doaj +2 more sources
Digital Communications and Networks, 2019 The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the
Liang Huang +4 more
doaj +1 more source
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.A π‐conjugated carbazole (p‐CBZ) layer is electro‐polymerized on BiVO4 to form a p–n heterojunction that enhances charge separation and suppresses surface recombination, while a NiOOH co‐catalyst accelerates oxygen evolution kinetics. The synergistic BVO/p‐CBZ/NiOOH photoanode delivers a high photocurrent density and long‐term stability for efficient ...
Lakshman Sundar Arumugam +11 more
wiley +1 more source
IEEE Access, 2022 Mobile edge computing has been widely used in various IoT devices due to its excellent computing power and good interaction speed. Task offloading is the core of mobile edge computing.
Peiying Zhang +5 more
doaj +1 more source
Fire and Materials, EarlyView.ABSTRACT This study addresses the influence of fibre architecture on the mechanical properties of vacuum‐infused glass fibre reinforced polymer (GFRP) composites at elevated temperatures (ET). Laminates with three different fibre configurations—unidirectional (UD), bidirectional 0°/90° cross‐ply (BD), and ±45° off‐axis angle‐ply (OA) – were tested in ...
Eloísa Castilho +4 more
wiley +1 more source
Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
, 2021 Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources.
Dugki Min +7 more
core +1 more source
International Journal for Numerical Methods in Fluids, EarlyView.This study utilizes the Method of Manufactured Solutions (MMS) to validate high‐order compact finite‐difference schemes for 2D incompressible viscoelastic flows. By applying Oldroyd‐B and Giesekus models to a lid‐driven cavity benchmark, the researchers rigorously assessed numerical accuracy across varying Reynolds and Weissenberg numbers.
Juniormar Organista +5 more
wiley +1 more source
Modelling task offloading mobile edge computing
, 2022 With the rapid growth of mobile devices (such as smart phones and IoT devices) and the upcoming 5G era, it has been considered that edge computing will play a significant role, which together with the Cloud server forms the Mobile Edge Computing (MEC ...
He, Ligang, Chen, Zhiyan
core +1 more source
Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting
IEEE Access, 2020 Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper.
Jiafa Chen +3 more
doaj +1 more source