High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks
Academic paper|更新时间:2024-06-05
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High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks
Journal on CommunicationsVol. 36, Issue 6, Pages: 41-48(2015)
作者机构:
重庆邮电大学 移动通信技术重庆市重点实验室,重庆 400065
作者简介:
基金信息:
The National Natural Science Foundation of China(61379159);The Program for Changjiang Scholars and Innovative Research Team in University(IRT1299);The Natural Science Foundation of Chongqing(cstc2012jjA40051);The Project of Chongqing Municipal Education Commission(Kjzh11206)
Zhi REN, Jian-wei SUO, Wen-peng LIU, et al. High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks[J]. Journal on Communications, 2015, 36(6): 41-48.
DOI:
Zhi REN, Jian-wei SUO, Wen-peng LIU, et al. High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks[J]. Journal on Communications, 2015, 36(6): 41-48. DOI: 10.11959/j.issn.1000-436x.2015129.
High-throughput and low-overhead probabilistic routing based on multi-player bargaining game for opportunistic networks
To address the problems existing in the present probabilistic routing based on bargaining games
including unidirectional transmission of messages degrades the success ratio
depending on the virtual money decreases the purchasing power of nodes
and redundancy exists in the interaction process of messages
an routing algorithm based on multi-player bargaining game for opportunistic networks
HLPR-MG
was proposed.Through extending the two-player game to a multi-player bargaining game
introducing the barter trade to enhance purchasing power of nodes
and improving the existing interaction mechanism to reduce the times of game
proposed algorithm achieves the effect of increasing network throughput and decreasing control overhead.Theoretical analysis verifies the effectiveness of HLPR-MG
and simulation results show that HLPR-MG improves the network throughput and success ratio at least 3.63%
and reduces the control overhead and average end-to-end delay by more than 17.76% and 4.03%
respectively
as compared to the classical GSCP and BG algorithms.
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