Accurate RSS-Based Positioning Using a Terrain-Independent Dynamic Propagation Model

2017 ◽  
Author(s):  
Marzieh Mahrokh ◽  
Esfadiar Mehrshahi ◽  
Kumbesan Sandy Sandrasegaran
Keyword(s):  
Propagation Model ◽  
Dynamic Propagation

Dynamic Propagation Model of Blue-Tooth Virus on Smart Phones

2010 ◽  
Vol 121-122 ◽  
pp. 620-626 ◽  
Author(s):  
Xiao Yan Huang ◽  
Cong Jin ◽  
Song Lin Jin
Keyword(s):  
Epidemic Model ◽  
Spreading Rate ◽  
Malicious Code ◽  
Propagation Model ◽  
Smart Phones ◽  
Dynamic Propagation ◽  
Simulation Results ◽  
Development Tendency ◽  
Virus Spreading ◽  
Blue Tooth

According to blue-tooth viruses spread actuality, an epidemic model of blue-tooth phone virus is proposed in this paper. In this epidemic model, we proposed four basic statues to represent smart phones in different states. We also introduced some factors which can affect the basic trend of virus spreading, such as density of smart phones, length of malicious code and so on. But we mainly focused on parameter of spreading rate, and defined it as a variable which could change with time. At the end of this model, the simulation results showed the development tendency of this propagation model.

Curvature effects in the dynamic propagation of wildfires

2016 ◽  
Vol 25 (12) ◽  
pp. 1238 ◽  
Author(s):  
J. E. Hilton ◽  
C. Miller ◽  
J. J. Sharples ◽  
A. L. Sullivan
Keyword(s):  
Propagation Model ◽  
Computationally Efficient ◽  
Local Curvature ◽  
Fire Propagation ◽  
Curvature Effects ◽  
Dynamic Propagation ◽  
Rate Of Spread ◽  
Complex Interactions ◽  
Curvature Parameter ◽  
Curvature Dependence

The behaviour and spread of a wildfire are driven by a range of processes including convection, radiation and the transport of burning material. The combination of these processes and their interactions with environmental conditions govern the evolution of a fire’s perimeter, which can include dynamic variation in the shape and the rate of spread of the fire. It is difficult to fully parametrise the complex interactions between these processes in order to predict a fire’s behaviour. We investigate whether the local curvature of a fire perimeter, defined as the interface between burnt and unburnt regions, can be used to model the dynamic evolution of a wildfire’s progression. We find that incorporation of curvature dependence in an empirical fire propagation model provides closer agreement with the observed evolution of field-based experimental fires than without curvature dependence. The local curvature parameter may represent compounded radiation and convective effects near the flame zone of a fire. Our findings provide a means to incorporate these effects in a computationally efficient way and may lead to improved prediction capability for empirical models of rate of spread and other fire behaviour characteristics.

scholarly journals C-SIW Rumor Propagation Model with Variable Propagation Rate and Perception Mechanism in Social Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Liqing Qiu ◽  
Shuqi Liu
Keyword(s):  
Social Networks ◽  
Propagation Rate ◽  
Propagation Model ◽  
Experimental Simulation ◽  
Field Equations ◽  
Dynamic Propagation ◽  
Rumor Propagation ◽  
Key Points ◽  
State Changes ◽  
The Individual

The propagation of rumor has become a common phenomenon in social networks. Studying the dynamic propagation of rumor can help locate the key points to control rumor propagation. To further research the internal motivation of state transition, a corrector-ignorant-spreader-weakener (C-SIW) model is proposed in this paper. When the individual changes state to transmit rumor, the neighbor may have a significant impact on rumor propagation. Considering the point, this paper constructs a function to describe the propagation rate, which relates to the state of neighbors and the reputation of the spreader. In addition, perception from life also can cause individual state changes. Based on the above fact, the links from the spreader and the weakener to the corrector are added to describe the perception mechanism. Then, combining the derived average field equations, the steady state of the model is analyzed and verified in experimental simulation. Moreover, the experimental results on different networks show that the perception mechanism reduces the rumor influence. Besides, the variable propagation rate can position the fast-growing stage of rumor propagation more accurately and facilitate the control of rumor propagation.

scholarly journals ITDPM: An Internet Topology Dynamic Propagation Model Based on Generative Adversarial Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hangyu Hu ◽  
Xuemeng Zhai ◽  
Gaolei Fei ◽  
Guangmin Hu
Keyword(s):  
Network Control ◽  
Propagation Model ◽  
Generative Adversarial Networks ◽  
Network Data ◽  
Information Propagation ◽  
Adversarial Learning ◽  
Network Information ◽  
Internet Topology ◽  
Dynamic Propagation ◽  
Influential Nodes

Network information propagation analysis is gaining a more important role in network vulnerability analysis domain for preventing potential risks and threats. Identifying the influential source nodes is one of the most important problems to analyze information propagation. Traditional methods mainly focus on extracting nodes that have high degrees or local clustering coefficients. However, these nodes are not necessarily the high influential nodes in many real-world complex networks. Therefore, we propose a novel method for detecting high influential nodes based on Internet Topology Dynamic Propagation Model (ITDPM). The model consists of two processing stages: the generator and the discriminator like the generative adversarial networks (GANs). The generator stage generates the optimal source-driven nodes based on the improved network control theory and node importance characteristics, while the discriminator stage trains the information propagation process and feeds back the outputs to the generator for performing iterative optimization. Based on the generative adversarial learning, the optimal source-driven nodes are then updated in each step via network information dynamic propagation. We apply our method to random-generated complex network data and real network data; the experimental results show that our model has notable performance on identifying the most influential nodes during network operation.

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