Search Results for author:
Found 9 papers, 1 papers with code
Intelligent Data-Driven Architectural Features Orchestration for Network Slicing
In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture.
Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration
Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others.
On Modeling Network Slicing Communication Resources with SARSA Optimization
The communication slices and their allocated communication resources are essential in slicing architectures for resource orchestration and allocation, virtual network function (VNF) deployment, and slice operation functionalities.
Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model
The paper investigates how a BAM model can contribute to the RL agent design and efficiency.
On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid
This paper presents the power network reconfiguration algorithm HATSGA with a "R" modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context.
Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning
This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm.
A Methodological Approach to Model CBR-based Systems
The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.
A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid
This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks.
Towards Smart City Innovation Under the Perspective of Software-Defined Networking, Artificial Intelligence and Big Data
Smart city projects address many of the current problems afflicting high populated areas and cities and, as such, are a target for government, institutions and private organizations that plan to explore its foreseen advantages.
