1.

Record Nr.

UNINA9910437871803321

Autore

Du Ding-Zhu

Titolo

Connected Dominating Set: Theory and Applications / / by Ding-Zhu Du, Peng-Jun Wan

Pubbl/distr/stampa

New York, NY : , : Springer New York : , : Imprint : Springer, , 2013

ISBN

1-4614-5242-2

Edizione

[1st ed. 2013.]

Descrizione fisica

1 online resource (205 p.)

Collana

Springer Optimization and Its Applications, , 1931-6828 ; ; 77

Disciplina

511.5

Soggetti

Operations research

Management science

Algorithms

Combinatorics

Computer communication systems

Mathematical optimization

Operations Research, Management Science

Algorithm Analysis and Problem Complexity

Computer Communication Networks

Optimization

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references (pages [193]-199) and index.

Nota di contenuto

Connected Dominating Set:Theory and Applications; Preface; Contents; Chapter1 Introduction; 1.1 Connected Domination Number; 1.2 Virtual Backbone in Wireless Networks; 1.3 Converter Placement in Optical Networks; 1.4 Connected Domatic Number; 1.5 Lifetime of Sensor Networks; 1.6 Theory and Applications; Chapter2 CDS in General Graph; 2.1 Motivation and Overview; 2.2 Complexity of Approximation; 2.3 Two-Stage Greedy Approximation; 2.4 Weakly CDS; 2.5 One-Stage Greedy Approximation; 2.6 Weighted CDS; 2.7 Directed CDS; Chapter3 CDS in Unit Disk Graph; 3.1 Motivation and Overview

3.2 NP-Hardness and PTAS3.3 Two-Stage Algorithm; 3.4 Independent Number (I); 3.5 Independent Number (II); 3.6 Zassenhaus-Groemer-Oler Inequality; Chapter 4 CDS in Unit Ball Graphs and Growth  Bounded Graphs; 4.1 Motivation and Overview; 4.2 Gregory-Newton Problem; 4.3 Independent Points in Two Balls; 4.4 Growth-Bounded Graphs; 4.5



PTAS in Growth-Bounded Graphs; Chapter5 Weighted CDS in Unit Disk Graph; 5.1 Motivation and Overview; 5.2 Node-Weighted Steiner Tree; 5.3 Double Partition; 5.4 Cell Decomposition; 5.5 6-Approximation; 5.6 4-Approximation; 5.7 3.63-Approximation; Chapter6 Coverage

9.4 A Two-Staged Algorithm for Min-CDSChapter10 Geometric Hitting Set and Disk Cover; 10.1 Motivation and Overview; 10.2 Minimum Geometric Hitting Set; 10.3 Minimum Disk Cover; Chapter11 Minimum-Latency Scheduling; 11.1 Motivation and Overview; 11.2 Geometric Preliminaries; 11.3 Dominating Tree; 11.4 Broadcast Scheduling; 11.5 Aggregation Scheduling; 11.6 Gathering Scheduling; 11.7 Gossiping Scheduling; Chapter12 CDS in Planar Graphs; 12.1 Motivation and Overview; 12.2 Preliminaries; 12.3 Algorithm Description; 12.4 Performance Analysis; References; Index

Sommario/riassunto

The connected dominating set (CDS) has been a classic subject studied in graph theory since 1975. It has been discovered in recent years that CDS has important applications in communication networks —especially in wireless networks —as a virtual backbone. Motivated from those applications, many papers have been published in the literature during last 15 years. Now, the connected dominating set has become a hot research topic in computer science. This work is a valuable reference for researchers in computer science and operations research, especially in areas of theoretical computer science, computer communication networks, combinatorial optimization, industrial engineering, and discrete mathematics. The book may also be used as a text in a graduate seminar for PhD students. Readers should have a basic knowledge of computational complexity and combinatorial optimization. In this book, the authors present the state-of-the-art in the study of connected dominating sets. Each chapter is devoted to one problem, and consists of three parts: motivation and overview, problem complexity analysis, and approximation algorithm designs. The text is designed to give the reader a clear understanding of the background, formulation, existing important research results, and open problems. Topics include minimum CDS, routing-cost constrained CDS, weighted CDS, directed CDS, SCDS (strongly connected dominating set), WCDS (weakly connected dominating set), CDS-partition, virtual backbone in wireless networks, convertor placement in optical networks, coverage in wireless sensor networks, and more.



2.

Record Nr.

UNINA9911049175103321

Autore

Auer Michael E

Titolo

Proceedings of the 2nd International Conference on Intelligent Optimization and Big Data Management (IOBDM2025) / / edited by Michael E. Auer, Xiaoguang Yue

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026

ISBN

3-032-10400-9

Edizione

[1st ed. 2026.]

Descrizione fisica

1 online resource (960 pages)

Collana

Lecture Notes in Networks and Systems, , 2367-3389 ; ; 1686

Altri autori (Persone)

Auer

Disciplina

006.3

Soggetti

Computational intelligence

Artificial intelligence

Engineering - Data processing

Computational Intelligence

Artificial Intelligence

Data Engineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Research of Stratified Teaching’s effects in Junior Schools based on DID model -- Research on the Integration of Ancient Chinese Word Segmentation and Labeling Based on CRF -- Machine Learning-Based Optimization of Neutral Equilibrium Mechanisms for Bridge Displacement Control -- An Adaptive Dual-Population PSO for Constrained Optimization Application to A Realistic Irregular Flight Recovery -- Research on the Standardized Application of the Entire Process of Digital Service Trade Supported by Internet of Things Technology -- ESG Optimization through Digital Finance Evidence from Chinese A-Share Companies -- Intelligent Optimization of Social Entrepreneurship Education A Big Data Driven Analysis of Student Satisfaction Patterns in China's Yangtze River Delta -- Research on Influencing Factors of AI in Hotel Room Service on Customer Experience Based on Multiple Linear Regression Model.

Sommario/riassunto

This book provides the proceedings of the 2nd International Conference on Intelligent Optimization and Big Data Management (IOBDM2025), which was held in Wuhan, China, from June 26 to 28,



2025, and was an ideal platform for presenting and discussing current trends in the field of intelligent optimization and big data management. Currently, the field of intelligent optimization and big data management is undergoing rapid development and transformation, with the integrated application of related new technologies becoming the focus of industry attention. To promote the progress of this field, academia and industry need to build an efficient communication platform to share cutting-edge achievements and practical experiences. The ways of research and application have changed, including the extensive use of advanced technical means such as intelligent algorithms and big data analysis. In addition, the continuous emergence of various emerging technologies is currently challenging traditional data management and optimization models. Effective practices in intelligent optimization and big data management need to be based on solid theories and rich industry application cases. As an annual conference of the International Engineering and Technology Institute (IETI) and CTI, the IOBDM conference is dedicated to exploring the basic theories, application achievements and practical experiences in the fields of intelligent optimization, computer science, information technology, big data management, and other related new technologies. Nowadays, the IOBDM conference has become an important forum for gathering global experts and scholars to exchange cutting-edge trends, research results and show practical experiences in related fields. In this way, we strive to promote the close integration between theoretical research and practical application. Interested readership includes policy makers in the field of data governance, academics specializing in intelligent optimization and big data, researchers in computer science and information technology, industry professionals engaged in data management, engineers focusing on intelligent algorithm development, practitioners in the big data industry, and lecturers in higher education institutions offering related disciplines, etc.