Artificial Intelligence (AI)

Recent Trends and Applications

S. Kanimozhi Suguna, M. Dhivya, Sara Paiva, S. Kanimozhi Suguna, M. Dhivya, Sara Paiva

• Publisher: CRC Press
• Year: 2021
• Print ISBN: 9780367431365
• eBook ISBN: 9781000375541
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Technology & Engineering

This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Artificial Intelligence. The book provides a premier interdisciplinary platform to present practical challenges and adopted solutions.

The book addresses the complete functional framework workflow in Artificial Intelligence technology. It explores the basic and high-level concepts and can serve as a manual for the industry for beginners and the more advanced. It covers intelligent and automated systems and its implications to the real-world, and offers data acquisition and case studies related to data-intensive technologies in AI-based applications.

The book will be of interest to researchers, professionals, scientists, professors, students of computer science engineering, electronics and communications, as well as information technology.

1 Advances in Large-Scale Systems Simulation Modelling Using Multi-Agent Architectures Optimized with Artificial Intelligence Techniques for Improved Concurrency-Supported Scheduling Mechanisms with Application to Wireless Systems Simulation

P.M. Papazoglou and Dimitrios Alexios Karras

Contents

1.1 Literature Review

1.1.1 Simulation Methodologies Applied in Wireless Communication Systems (WCS)

1.1.1.1 Simulation of WCS

1.1.1.2 Discrete Event Simulation

1.1.1.3 Event Scheduling

1.1.2 Channel Assignment in WCS

1.1.3 Multi-Agent Systems in WCS

1.1.3.1 Agent and Multi-Agent Systems

1.1.3.2 Multi-Agent Systems in WCS

1.1.4 The Concept of Cellular Network

1.1.5 Simulation Languages (SLs)

1.2 The Proposed Simulation Model

1.2.1 Network Structure

1.2.1.1 Operational Parameters

1.2.2 Modelled Network Services and Channel Allocation

1.2.2.1 Network Services

1.2.2.2 Channel Allocation

1.2.2.3 Traffic Generation

1.2.3 The Multi-Agent/Multilayered Model

1.2.4 Theoretical Analysis of Agents Adapted to Modelled Network Services

1.2.4.1 Network Agent Definition

1.2.4.2 Architecture of the Intelligent Network Agents

1.2.4.3 Network Agent Interface

1.2.4.4 Network Agents Which Maintain State

1.2.4.5 Network Agent Utility Functions

1.2.4.6 Multi-Agent Encounters

1.2.5 Event Interleaving as Scheduling Technique Based on Real-Time Scheduling Theory

1.2.5.1 Real-Time Scheduling Algorithms for Implementing Synchronized Processes or Events

1.2.5.2 Process Life Span in a Real-Time Scheduling Set-Up

1.2.5.3 Scheduling Concurrent Events in WCS

1.2.5.4 Response Time Analysis

1.2.5.5 Pre-emptive Stationary Priority Scheduling (PSPS)

1.2.6 Supported DCA Variations

1.2.6.1 The Conventional Unbalanced Variation (Classical DCA)

1.2.6.2 The Conventional Balanced Variation (Min Cell Congestion)

1.2.6.3 The Conventional Best CNR Variation

1.2.6.4 The Conventional Round Blocking Variation

1.2.6.5 The Proposed Novel Artificial Intelligence Based Balanced and Best CNR DCA Variation for Concurrent Channel Assignment

1.2.7 Implementation Architectures

1.2.7.1 Conventional Model

1.2.7.2 Concurrent Models

1.3 Simulation Model Evaluation

1.3.1 Network Behaviour

1.3.2 Monte Carlo Simulation Method

1.3.3 Simulation Model Behaviour

1.3.4 Results Accuracy

1.3.5 Reference Analysis Model Employing One Cell Only

1.4 Experimental Results

1.4.1 Indicative Results Based on Five Days of Network Operation

1.4.2 Model Behaviour Based on Architectural Variations

1.4.3 Scheduling Mechanism Comparison

1.4.4 Response Time Analysis Results

1.5 Conclusions and Future Work

References

1.1 Literature Review

1.1.1 Simulation Methodologies Applied in Wireless Communication Systems (WCS)

1.1.1.1 Simulation of WCS

A real wireless network’s efficiency and behaviour can be tested using simulation systems without the need for field experiments and prototype creation. The simulation solutions give the opportunity to grow to a desired wireless network channel allocation schemes, network architectures, etc. The simulation software development approach becomes a very critical issue influencing the resulting network model and efficiency, due to the complexity of real wireless networks. A big challenge for wireless network simulation is the discovery of a way to tackle the actual actions of the network and not just speed up execution time using parallel machines. The simulation model and environment structure affect the performance of simulated wireless networks, and for this reason the design and development of such systems is studied thoroughly. Modern simulation tools provide network engineers with the opportunity to develop and test wireless communication systems at low cost very quickly. There are three major simulation techniques (Chaturvedi, A., et al. 2001): discrete event simulation (DES), system dynamics, and multi-agents. The most widely known simulation tools are based on the DES concept and use various model architectures to implement. A more accurate and reliable simulation environment can be developed with the help of efficient model architectures (Chaturvedi, A., et al. 2001; Liu, W., et al. 1996; Zeng, X., et al. 1998; Bajaj, L., et al. 1999; Kelly, O.E., e...

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Contents
• Preface
• Acknowledgements
• Editor biographies
• Contributors
• Chapter 1 Advances in Large-Scale Systems Simulation Modelling Using Multi-Agent Architectures Optimized with Artificial Intelligence Techniques for Improved Concurrency-Supported Scheduling Mechanisms with Application to Wireless Systems Simulation
• Chapter 2 Let’s Find Out: Why Do Users React Differently to Applications Infused with AI Algorithms?
• Chapter 3 AI vs. Machine Learning vs. Deep Learning
• Chapter 4 AI and Big Data: Ethical Reasoning and Responsibility
• Chapter 5 Online Liquid Level Estimation in Dynamic Environments Using Artificial Neural Network
• Chapter 6 Computer Vision Concepts and Applications
• Chapter 7 Generative Adversarial Network: Concepts, Variants, and Applications
• Chapter 8 Detection and Classification of Power Quality Disturbances in Smart Grids Using Artificial Intelligence Methods
• Chapter 9 Robust Design of Artificial Neural Network Methodology to Solve the Inverse Kinematics of a Manipulator of 6 DOF
• Chapter 10 Generative Adversarial Network and Its Applications
• Chapter 11 Applications of Artificial Intelligence in Environmental Science
• Chapter 12 A Genetic Algorithm-based Artificial Intelligence Solution for Optimizing E-Commerce Logistics Vehicle Routing
• Chapter 13 Application of Machine Learning for Fault Detection and Energy Efficiency Improvement in HVAC Application
• Chapter 14 Smart City Using Artificial Intelligence Enabled by IoT
• Chapter 15 AI Emerging Communication and Computing
• Index