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Graphs for Smart Communications Systems
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Graphs for Smart Communications Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (1 February 2019) | Viewed by 5787

Special Issue Editor

Prof. Dr. Khmaies Ouahada

E-Mail Website
Guest Editor
School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, PO Box 524, Auckland Park 2006, South Africa
Interests: information theory; coding techniques; powerline communications; visible light communications; smart grid; energy demand management; renewable energy; wireless sensor networks; reverse engineering and engineering education.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Graph theory has been extensively implemented and used to model and analyse the relationships in scientific, information and modern technical systems. In many applications in digital communications and information theory, graph theory has become a very interesting modelling and analytical tool due to the simplicity in the geometric structures of a graph which is made up of vertices and lines and regarded as an abstract notion of a set of nodes and connection relations between them. This simplify in the graphs structures helped researchers in solving scheduling problems or finding approximate solutions in both theory and applications.

This Special Issue on “Graphs for Smart Communications Systems”, presents the application of graph theory in many modern communication systems. Two major themes are presented: Graph theoretic approach data communications and Graph theoretic approach for communications networks. In the first theme we will look at the contribution of graphs in the information theory, big data, network coding and error correcting codes. Whereas in the second theme, graphs will play a role in the wireless sensor networks, mobile networks, internet of things (IoT) and smart grid. Both comprehensive surveys and original technical contributions are welcome.

Dr. Khmaies Ouahada
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Graph theory
  • Information theory
  • Coding techniques
  • Mobile networks
  • Wireless sensor networks
  • Big data
  • Smart grid
  • Network coding

Published Papers (2 papers)

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14 pages, 396 KiB  
Article
The g-Good-Neighbor Diagnosability of Bubble-Sort Graphs under Preparata, Metze, and Chien’s (PMC) Model and Maeng and Malek’s (MM)* Model
by Shiying Wang and Zhenhua Wang
Information 2019, 10(1), 21; https://doi.org/10.3390/info10010021 - 10 Jan 2019
Cited by 2 | Viewed by 3145
Abstract
Diagnosability of a multiprocessor system is an important topic of study. A measure for fault diagnosis of the system restrains that every fault-free node has at least g fault-free neighbor vertices, which is called the g-good-neighbor diagnosability of the system. As a [...] Read more.
Diagnosability of a multiprocessor system is an important topic of study. A measure for fault diagnosis of the system restrains that every fault-free node has at least g fault-free neighbor vertices, which is called the g-good-neighbor diagnosability of the system. As a famous topology structure of interconnection networks, the n-dimensional bubble-sort graph B n has many good properties. In this paper, we prove that (1) the 1-good-neighbor diagnosability of B n is 2 n − 3 under Preparata, Metze, and Chien’s (PMC) model for n ≥ 4 and Maeng and Malek’s (MM) ∗ model for n ≥ 5 ; (2) the 2-good-neighbor diagnosability of B n is 4 n − 9 under the PMC model and the MM ∗ model for n ≥ 4 ; (3) the 3-good-neighbor diagnosability of B n is 8 n − 25 under the PMC model and the MM ∗ model for n ≥ 7 . Full article
(This article belongs to the Special Issue Graphs for Smart Communications Systems)
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17 pages, 377 KiB  
Article
g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model
by Shiying Wang and Yunxia Ren
Information 2018, 9(11), 275; https://doi.org/10.3390/info9110275 - 07 Nov 2018
Cited by 6 | Viewed by 2252
Abstract
Diagnosability of a multiprocessor system is an important research topic. The system and interconnection network has a underlying topology, which usually presented by a graph G = ( V , E ) . In 2012, a measurement for fault tolerance of the graph [...] Read more.
Diagnosability of a multiprocessor system is an important research topic. The system and interconnection network has a underlying topology, which usually presented by a graph G = ( V , E ) . In 2012, a measurement for fault tolerance of the graph was proposed by Peng et al. This measurement is called the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. Under the PMC model, to diagnose the system, two adjacent nodes in G are can perform tests on each other. Under the MM model, to diagnose the system, a node sends the same task to two of its neighbors, and then compares their responses. The MM* is a special case of the MM model and each node must test its any pair of adjacent nodes of the system. As a famous topology structure, the ( n , k ) -arrangement graph A n , k , has many good properties. In this paper, we give the g-good-neighbor diagnosability of A n , k under the PMC model and MM* model. Full article
(This article belongs to the Special Issue Graphs for Smart Communications Systems)
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