Method for predicting message passing node based on meeting probability of target node

A technology of encounter probability and message passing, applied in the computer field, it can solve the problems of high routing table task overhead, troublesome calculation and decision-making, etc.

Active Publication Date: 2019-11-05
CENT SOUTH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method is too simple to calculate the probability value in the face of a sudden increase in the amount of information, and it is too troubles

Method used

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  • Method for predicting message passing node based on meeting probability of target node
  • Method for predicting message passing node based on meeting probability of target node
  • Method for predicting message passing node based on meeting probability of target node

Examples

Experimental program
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Embodiment 1

[0129] In this example, this study uses One Simulator to implement the proposed method, and evaluates the performance of the PEBN algorithm from the aspects of transmission success rate, routing overhead and transmission hops. In the experiment, the topological map of Stanford University was used as the simulation scene, such as figure 2 shown. And the data used in the simulated scenario is a real dataset from Stanford University. We designed different numbers of pedestrian, car and electronic tracks to simulate the influence of the number of nodes, simulation time and node cache on the simulation results.

[0130] The real map area of ​​Stanford University that we choose is 1070m × 810m. Simulation time ranges from 1 hour to 12 hours. The simulation nodes are set to 100–1300, and the speeds of pedestrians, cars and trams are 5km / h, 100km / h and 60km / h, respectively. After reaching the destination, the node will stay there for a period of time. The sensing radius of the n...

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Abstract

The invention provides a method for predicting a message passing node based on the meeting probability of a target node. A fuzzy similarity matrix is established based on various relationship characteristics of nodes, different attributes of mobile nodes are deeply researched by forming the fuzzy similarity matrix, the social attribute change rule of the mobile nodes is mined, and the weights of the different attributes are dynamically and adaptively distributed. The social relationship and cooperation relationship of the nodes are further quantified. Finally, experiments verify that a good effect is achieved when the encountering model provided by the invention is used for screening the trusted node as the next hop node of data transmission, so that the data is always transmitted along the trusted cooperative node in the network, and meanwhile, the influence of malicious node non-cooperation on the performance of the network is reduced.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a method for predicting message transfer nodes based on target node encounter probability. Background technique [0002] With the high popularity of smartphones in daily life and the increasing functions of these smart devices, Mobile Social Networking (MSN) has become a common data transfer platform, and the popularity of mobile devices has enabled various new services in social networks be achieved. Many social tools, such as Google Plus, Facebook and Twitter, have a large number of users and generate data every moment. Therefore, mobile social networks have brought a lot of new research and application opportunities, such as location services, identification and detection of abnormal taxi trajectories, etc. Due to the diversity of online data, traditional social network approaches to deal with the diverse transmission and reception of big data face great challe...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L12/58H04L12/725H04L12/733H04L12/761H04L45/122H04L45/16
CPCH04L51/04H04L41/0823H04L41/0893H04L41/147H04L45/3065H04L45/16H04L45/20H04L51/214H04L51/52
Inventor 吴嘉余庚花陈志刚
Owner CENT SOUTH UNIV
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