Special Issue "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)

Special Issue Editor

Guest Editor
Dr. Khmaies Ouahada

Department of Electrical and Electronics Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Website | E-Mail
Interests: information theory; coding techniques; power-line communications; visible light communications; smart grid; energy demand management; renewable energy; wireless sensor networks; wireless communications; reverse engineering and engineering education

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

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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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Research

Open AccessArticle The g-Good-Neighbor Diagnosability of Bubble-Sort Graphs under Preparata, Metze, and Chien’s (PMC) Model and Maeng and Malek’s (MM)* Model
Information 2019, 10(1), 21; https://doi.org/10.3390/info10010021
Received: 2 December 2018 / Revised: 22 December 2018 / Accepted: 4 January 2019 / Published: 10 January 2019
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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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Open AccessArticle g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model
Information 2018, 9(11), 275; https://doi.org/10.3390/info9110275
Received: 17 September 2018 / Revised: 27 October 2018 / Accepted: 5 November 2018 / Published: 7 November 2018
Cited by 1 | PDF Full-text (377 KB) | HTML Full-text | XML Full-text
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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