Topology evolution model for ad hoc-cellular hybrid networks based on complex network theory

2016 ◽  
Vol 30 (6) ◽  
pp. e3119 ◽  
Author(s):  
Yongfu Hou ◽  
Yifei Wei ◽  
Mei Song ◽  
Richard F. Yu
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Ad Hoc ◽  
Evolution Model ◽  
Hybrid Networks ◽  
Complex Network Theory

scholarly journals Modeling and Topological Properties of a V2I Sub Network in VANET Based on a Complex Network

2015 ◽  
Vol 15 (4) ◽  
pp. 149-160 ◽  
Author(s):  
Hong Zhang ◽  
Li He
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Intelligent Transportation ◽  
System Structure ◽  
Topological Properties ◽  
Basic Part ◽  
Complex Network Theory ◽  
Wireless Communication Network

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.

scholarly journals A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3434 ◽  
Author(s):  
Lizhi Wang ◽  
Dawei Lu ◽  
Yuan Zhang ◽  
Xiaohong Wang
Keyword(s):  
Complex Network ◽  
Unmanned Aerial Vehicle ◽  
Network Theory ◽  
Ad Hoc ◽  
Point Of View ◽  
Statistical Characteristics ◽  
Modeling Framework ◽  
Complex Network Theory ◽  
Network Layers ◽  
Aerial Vehicle

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.

scholarly journals Ecological Network Optimization in Urban Central District Based on Complex Network Theory: A Case Study with the Urban Central District of Harbin

2021 ◽  
Vol 18 (4) ◽  
pp. 1427
Author(s):  
Shuang Song ◽  
Dawei Xu ◽  
Shanshan Hu ◽  
Mengxi Shi
Keyword(s):  
Complex Network ◽  
Network Optimization ◽  
Network Theory ◽  
Habitat Destruction ◽  
Landscape Patterns ◽  
Ecological Network ◽  
Complex Network Theory ◽  
Ecological Corridors ◽  
Low Degree ◽  
Central District

Habitat destruction and declining ecosystem service levels caused by urban expansion have led to increased ecological risks in cities, and ecological network optimization has become the main way to resolve this contradiction. Here, we used landscape patterns, meteorological and hydrological data as data sources, applied the complex network theory, landscape ecology, and spatial analysis technology, a quantitative analysis of the current state of landscape pattern characteristics in the central district of Harbin was conducted. The minimum cumulative resistance was used to extract the ecological network of the study area. Optimized the ecological network by edge-adding of the complex network theory, compared the optimizing effects of different edge-adding strategies by using robustness analysis, and put forward an effective way to optimize the ecological network of the study area. The results demonstrate that: The ecological patches of Daowai, Xiangfang, Nangang, and other old districts in the study area are small in size, fewer in number, strongly fragmented, with a single external morphology, and high internal porosity. While the ecological patches in the new districts of Songbei, Hulan, and Acheng have a relatively good foundation. And ecological network connectivity in the study area is generally poor, the ecological corridors are relatively sparse and scattered, the connections between various ecological sources of the corridors are not close. Comparing different edge-adding strategies of complex network theory, the low-degree-first strategy has the most outstanding performance in the robustness test. The low-degree-first strategy was used to optimize the ecological network of the study area, 43 ecological corridors are added. After the optimization, the large and the small ecological corridors are evenly distributed to form a complete network, the optimized ecological network will be significantly more connected, resilient, and resistant to interference, the ecological flow transmission will be more efficient.

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