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The Research of Clustering and Connectivity in Tactical Ad Hoc Network

Author: JinChao
Tutor: LiDeMin
School: Donghua University
Course: Signal and Information Processing
Keywords: tactical Ad Hoc networks mobility model clustering algorithm network connectivity
CLC: TN929.5
Type: Master's thesis
Year: 2011
Downloads: 48
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Abstract


With the development of Ad Hoc networks, the application of tactical Ad Hoc is gaining more attention. But considering the harsh battlefield environment and the complex consists in large-scale practical application of tactical Ad Hoc networks, it requires prior choice of virtual platforms to run the entire network performance. This paper developed a new Gauss-Markov mobility model, and studied the clustering algorithm and connectivity between clusters based on such model.This paper studied the mobility model used in Ad Hoc network, discussed several typical physical mobility model and group mobility model, analyze its ability to apply tactical Ad Hoc network, and the special environment of the battlefield for a new Gauss-Markov group mobility model, the model simulates a large extent the reality of the battlefield environment, the simulation results obtained more realistic and objective.Based on the Gauss-Markov group mobility model, this paper in-depth studied the clustering problem in tactical Ad Hoc network. Then described the advantages and disadvantages of adaptive weighted clustering algorithm (AOW), on the basis of the clustering algorithm for the battlefield environment proposed to improve the new tactical Ad Hoc network clustering algorithm- TACA algorithm. The TACA clustering algorithm considers the problem of tactical command of troops and combat mobile node usually has the characteristics of the group, making the algorithm is better than previous clustering algorithms have better performance.Finally this paper studied the tactical Ad Hoc networks connectivity based on the Gauss-Markov group mobility model, in the NS-2 simulation platform of the proposed clustering algorithm TACA and AOW, MAOW and TAOW clustering algorithms group the first number, the rule set update frequency, and the number of nodes rejoin the cluster were compared, the results show that the proposed algorithm is better than other clustering algorithms TACA has better stability and connectivity.

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