washake Goto Github PK

digital-twin-opcua

Files used in the development of a digital twin for a robot cell at NTNU with the use of Visual Components 4.0 and OPC UA

double-deep-q-learning-for-resource-allocation

Reproduce results of the research article "Deep Reinforcement Learning Based Resource Allocation for V2V Communications"

dqn_rc_dsa_iot2019

Deep Reinforcement Learning (DRL) based Dynamic Spectrum Access (DSA) using Reservoir Computing (RC) (In IoT-J-2019)

dra

A Distribute and Reactive Approach for Real-time Task Offloading in the MEC Environment

droo

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

dual-based-ultra-dense-networks-optimation

A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks中的对偶方法，在与作者（周力，[email protected]）邮件交流后，作者分享的。

dynamic-lot-sizing

UConn Manuf Engr Project 2015

dynamicprogrammingproject2

Dynamic Programming course project - Optimizing IoT to Fog Latency in Resource Allocation

edge-boost-vanilla-framework

This is a prototype simulation model of Mobile Device Cloud Caching in 5G using device to device interaction.

edge-caching-algorithms

Edge caching algorithms for dense small cell networks. Research Project under NYU

edge-computing-codes

Simulation of algorithms for Edge Computing-related papers.

edge-intelligence

随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)

edgesim

Simulate the real environment, perform edge computing, edge caching experiments

end2end_gan

Conditional GAN based End-to-End Communication System

energy-efficiency-in-reinforcement-learning

Code for the paper 'Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks'

fl_ci

Source code for paper 'An Improved Federated Learning Algorithm for Privacy-Preserving in Cybertwin-Driven 6G System'

gadmm

GADMM: fast and communication efficient framework for distributed machine learning

generalized-benders-decomposition-

This project concentrates on implementation of Generalized Bender's Decomposition (GBD) algorithm for sparse linear regression problem.

gg1sim

A G/G/1 queue simulation estimating long-run mean queue length under different conditions.

github_repository_collection

将自己点星的一些仓库进行整理

graph_comb_opt

Implementation of "Learning Combinatorial Optimization Algorithms over Graphs"

hadetection

Human activity detection project for Wireless Networking

icarus

A scalable simulator for evaluating the performance of in-network caches in Information Centric Networking (ICN)

idt-sdvn-platform

a powerful tool developed in Python environment to simulate data transmission in SDVN scenario, IDT-SDVN architecture is utilized here

ieee802154inet-standalone

A new IEEE 802.15.4 Simulation Model for OMNeT++ 4.x / INET 2.x

ieee802_15_4_matlab

Matlab Code of the implementation of IEEE 802.15.4 Mac/Phy Layer

implementation-of-mec-papers

Implementation in Python & MATLAB of submitted & popular papers on Mobile Edge Computing (MEC).

implementation-of-prediction-collaborative-and-replacement-

implementation of Prediction, Collaborative and Replacement (PCR) caching algorithm for edge computing

jadce-channel-estimation

latency-minimization-for-mmwave-d2d-mobile-edge-computing-systems