Topology Evolution Model for Cognitive Ad Hoc Networks Based on Complex Network Theory

Author(s):  
Yongfu Hou ◽  
Yifei Wei ◽  
Mei Song ◽  
F. Richard Yu
2015 ◽  
Vol 15 (4) ◽  
pp. 149-160 ◽  
Author(s):  
Hong Zhang ◽  
Li He

Abstract Vehicles to Infrastructure (V2I) communicate with each other in a Vehicular Ad hoc NETwork (VANET) that can be represented as a complex network. In them much interest has been attracted towards the topological properties and structure recently, and many studies focus the attention on it, in particular V2I sub network. V2I is an important basic part of the future intelligent transportation, which transfers information through a wireless communication network. Analyzing the topological properties would help understand the VANET system structure and reveal the essence of the network. In this paper we propose a V2I model in VANET based on the complex network theory, analyzing the degree distribution. VANET topology characteristics are designed and discussed. The simulation results further illustrate the efficiency and applicability of the proposed approach.


Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3434 ◽  
Author(s):  
Lizhi Wang ◽  
Dawei Lu ◽  
Yuan Zhang ◽  
Xiaohong Wang

Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.


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