谭国真

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:计算机科学与技术学院

办公地点:大连理工大学创新园大厦8-A0824

联系方式:18641168567

电子邮箱:gztan@dlut.edu.cn

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Optimizing Retransmission Threshold in Wireless Sensor Networks

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论文类型:期刊论文

发表时间:2016-05-01

发表刊物:SENSORS

收录刊物:SCIE、EI、PubMed

卷号:16

期号:5

ISSN号:1424-8220

关键字:packet delivery; optimal retransmission threshold; in time

摘要:The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is O (n Delta . max(1<i< n) {u(i)}), where u(i) is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Delta is the given upper bound of the transmission delay of the delivery path. If Delta is greater than the polynomial, to reduce the time complexity, a linear programming-based (1 + p(min))-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance.