scholarly journals A Model for Traffic Forwarding through Service Function Chaining using Deep Reinforcement Learning Techniques

2021 ◽  
Author(s):  
Silvio Romero de Araújo Júnior ◽  
Reinaldo A. C. Bianchi
Keyword(s):  
Reinforcement Learning ◽  
Communication Networks ◽  
Computational Simulations ◽  
Research Opportunities ◽  
Service Function ◽  
Network Algorithm ◽  
Learning Techniques ◽  
New Challenges ◽  
New Research

The development of new communication networks to offer innovative services has increased the volume of data. With the introduction of Deep Reinforcement Learning and Service Function Chaining architecture, new research opportunities have emerged to propose solutions to the new challenges. This work proposes a model through computational simulations how these techniques can be applied. The model was evaluated using two variations of the Deep Q-Network algorithm over the CIC-Darknet dataset. Results showed that both variations are a promising mechanism to make the networks more autonomous and intelligent. to demonstrate

scholarly journals A Survey on Machine-Learning Techniques for UAV-Based Communications

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5170 ◽  
Author(s):  
Petros S. Bithas ◽  
Emmanouel T. Michailidis ◽  
Nikolaos Nomikos ◽  
Demosthenes Vouyioukas ◽  
Athanasios G. Kanatas
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
Spectral Efficiency ◽  
Channel Modeling ◽  
Machine Learning Techniques ◽  
Wireless Communication Networks ◽  
Relevant Research ◽  
Functional Aspects ◽  
Learning Techniques ◽  
New Challenges

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

2021 ◽  
Vol 12 (3) ◽  
pp. 11-14
Author(s):  
Joon-Seok Kim ◽  
Taylor Anderson ◽  
Ashwin Shashidharan ◽  
Andreas Züfle
Keyword(s):  
Spatial Data ◽  
International Workshop ◽  
Data Sources ◽  
Relative Location ◽  
Technological Changes ◽  
Research Opportunities ◽  
New Methodologies ◽  
New Research

Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

scholarly journals On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

2020 ◽  
Author(s):  
Bryan P Bednarski ◽  
Akash Deep Singh ◽  
William M Jones
Keyword(s):  
Public Health ◽  
Reinforcement Learning ◽  
Medical Equipment ◽  
Census Bureau ◽  
Learning Models ◽  
Public Health Emergencies ◽  
Medical Supplies ◽  
Learning Techniques ◽  
Disease Impact ◽  
Random States

Abstract objective This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. materials and methods The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. results The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states. conclusion These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

Sign in / Sign up

Export Citation Format

Share Document