A partial network coding based real-time multicast scheme in MANETs

Author(s):  
Guoping Tan ◽  
B.A.F. Lin ◽  
Xinhua Peng ◽  
Xiuquan Liu ◽  
Chuanyu Qu
2013 ◽  
Vol 8 (10) ◽  
Author(s):  
Guoping Tan ◽  
Xinhua Peng ◽  
Xinyang Ni ◽  
Betene A. Francis Lin ◽  
Xiuquan Liu

2014 ◽  
Vol 530-531 ◽  
pp. 768-772
Author(s):  
Guo Ping Tan ◽  
Lin Feng Tan ◽  
Lei Cao ◽  
Mei Yan Ju

For the study of the applications of partial network coding based real-time multicast protocol (PNCRM) in Mobile Ad hoc networks, the researches should be developed in the probability distribution of delay. In this paper, NS2 is used to obtain the delay of data packets through simulations. Because the delay does not obey the strict normal distribution, the maximum likelihood estimate method based on the lognormal distribution is used to process the data. Using MATLAB to obtain the actual distribution of the natural logarithm of delay, then drawing the delay distribution with the maximum likelihood estimation method based on the lognormal distribution, the conclusion that the distributions obtained by the above mentioned methods are basically consistent can be obtained. So the delay distribution of PNCRM meets the lognormal distribution and the characteristic of delay probability distribution can be estimated.


2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Guoping Tan ◽  
Yandan Zhu ◽  
Yueheng Li ◽  
Lili Zhang ◽  
Jing Xu

For real-time multicast services over wireless multihop networks, to minimize the energy of transmissions with satisfying the requirements of a fixed data rate and high reliabilities, we construct a conflict graph based framework by joint optimizing network coding and scheduling. Then, we propose a primal-dual subgradient optimization algorithm by random samplingKmaximal stable sets in a given conflict graph. This method transforms the NP-hard scheduling subproblem into a normal linear programming problem to obtain an approximate solution. The proposed algorithm only needs to adopt centralized technique for solving the linear programming problem while all of the other computations can be distributed. The simulation results show that, comparing with the existing algorithm, this algorithm can not only achieve about 20% performance gain, but also have better performance in terms of convergence and robustness.


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