Tools for evaluation of social relations in mobility models

Abstract

In MANET-DTN, the main idea is to detect the social relations between nodes in mobility models because the wireless mobile devices are carried by humans and the network uses the social relations to transport messages between isolated islands of the mobile terminals in order to increase the network performance. Today there are a lot of social and non-social mobility models. The problem is how to use these models in MANET-DTN. Therefore, proper evaluation method is needed, that is able to reveal the social aspect of investigated mobility model. Since there are a lot of methods that do not directly exhibit the social aspect of mobility models, in this paper the new evaluation method was proposed based on Louvain method for community detection and the other network graph parameter (average weighted degree). Simulations of evaluation method were made as a comparison between two random mobility models and one social based mobility model in order to point out differences between social and non-social mobility models. All models were evaluated by proposed method and other existing protocol dependent and independent methods. The main idea of the simulations was to analyse how the mobility modThis work was performed in the framework of research project VEGA 1/0075/15, funded by the ministry of education of Slovak Republic. els with social and non-social mobility models can affect the network performance and provide new and reliable tool, which enables analysis of the mobility models from social behaviour point of view.

The Educational Models in Air Force Personnel Training Process

“…Anyway, the basic educational method still remains practical pilot training on simulators. The simulator training is included in two subjects "Flight Simulator Practical Training part I, II, III" and "Flight Planning and Monitoring" [7][8][13][14][15].…”

Protocol-Independent Evaluation Of UAV Network

“…When evaluating the FANET network, it is also important to keep in mind the type of deployed movement that can detect the communities in the network. With this finding, it can be said whether the deployed movement shows signs of grouping UAV devices with a common goal, or each UAV device carries out its mission individually [8].…”

“…In the cases, when some communities are detected, the value of modularity quality is in positive meaning, larger number represents a stronger community. Based on the research in [8], is the value above about 0,3 indicator of good community structure in the network. The modularity quality is calculated based on (5), where Ai,j represents the weight of the edge between couple of nodes (i,j), ki is the sum of the weights of the edges attached to vertex i, ci is community to witch vertex i is assigned, δ(ci,cj) is 1 if ci=cj and 0 otherwise and [10].…”