Within a netcentric environment, military communication networks (MCNs) are mostly characterized by heterogeneous nodes and multilinked relationships between nodes. Traditional graph theory based network structure models are difficult to describe such characteristics of MCN accurately. In order to analyze, design, and build MCNs for a netcentric environment, we propose a novel structure model based on supernetwork by extending and integrating Jeff Cares' combat network model and Hans E.keus' Netforce Reference Model. The model's mathematical structure is given in detail. The proposed model is able to represent two key characteristics of MCNs, i.e. node's functional types and link's transmission types. Based on the proposed model, the military communication supernetwork can be decomposed into several functional or special sub-networks which are more meaningful in the military background. As an example, a joint operational forces communication supernetwork structure is given to validate the proposed supernetwork model.
Network complexity is one of the primary researches in the network science. There are a large number of research efforts concerned with the measuring of network (or graph) complexity, such as medium articulation (MAg), Offdiagonal Complexity (OdC), and so on. But they are difficult to describe the differences between military communication network (MCN) and other networks, since MCNs are man-made functional networks. According to the structure and functional characteristics of MCNs, we introduce the concept of military communication supernetwork (MCSN), adjacent matrix of supernetwork, type mixing matrix, type assortatively coefficient, and the definitions of supernetwork motif, core and the entropy of the motif. Then we take motifs which are the basic construct modules of MCSN, as the possible microstructure, and propose supernetwork motif entropy (SNME) as a complexity measure of MCNs. Finally, by taking the complexity measuring steps of a Naval Vessels Fleet communication supernetwork structure which was discussed in detail as an example, the computing results of SNME were compared with the present complexity measures, and it is demonstrated that SNME can measure the functional complexity of network when its structural complexity is determined.
In the evaluation process of Military communication network effectiveness (MCNE), the evaluation data mainly come from expert judgments, simulation results and test bed data, and these data cannot be directly used to evaluate MCNE because these three kinds of data are very different in form and in attribute. This research proposed a novel method which synthesizes expert judgments, simulation results, and test bed data to evaluate MCNE with respect to the characteristics of them. Using Belief map as data expression form; taking test bed data as training example; acquiring simulation data's confidence using test bed data and Support vector regression (SVR) which build regression relational model between network parameters and measures of performance; using an extensive Bayesian algorithm (EBA) to fuse data from multiple sources; we proposed an evaluation framework of MCNE based on multiple data sources. Take MCNE evaluation of a Naval Vessels Fleet as an example, we demonstrate that the fusion process proposed is credible and effective. Keywords-military communication network effectiveness; data fusion; belief map; support vector regression; extensive Bayesian algorithmI.
In the choice process of optimal military communication (MC) alternative, evaluation data mainly come from expert judgments, simulation results and test bed data, and they cannot be directly used in evaluation because of differences in form and attribute; and the MC environment changes rapidly as the operation tempo increasing. It is an important effort to judge the effectiveness robustness of MC alternative, since both the evaluation data and the MC environment are full of uncertainty. A robustness evaluation method based on multiple data sources and Monte Carlo simluation is proposed with respect to the characteristics of them. Mainly include Belief map as data expression form; Regression relational model built with Support Vector Regression (SVR) to acquire simulation data's confidence with test bed data as training example; Extensive Bayesian Algorithm (EBA) to fuse data from multiple sources; Beta distribution fitting method for each criterion of each alternative by using the fused results; and calculation of the Probability of Best (PoB) of each alternative through Monte Carlo simulation. Take MCE evaluation of a Naval Vessels Fleet as an example, the proposed method is compared with some general methods. The results indicate that the proposed method helps to obtain relatively conservative alternative and is effective in guaranteeing the robustness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.