无标度网络中基于能量的混合路由策略

杨先霞; 濮存来; 许忠奇; 陈荣斌; 吴洁鑫; 李伦波

doi:10.7498/aps.65.248901

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摘要

针对节点能量受限的静态无标度网络，提出了一种基于能量和最短路径相结合的路由策略.该策略综合考虑邻居节点的能量水平和其到目的节点的最短路径长度，利用控制参数调节二者的权重.仿真结果表明，存在最佳的值使得网络生存时间和数据包到达数达到最大值.最后，基于提出的路由策略研究了网络结构特征与网络生存时间之间的关系.

关键词:

作者及机构信息

1.
南京理工大学计算机科学与工程学院, 南京 210094

通信作者: 濮存来, pucunlai@njust.edu.cn

基金项目: 国家自然科学基金（批准号：61304154）、教育部博士点基金（批准号：20133219120032）、中国博士后基金（批准号：2013M541673）和中国博士后特别资助（批准号：2015T80556）资助的课题.

参考文献

[1]	van Schewick B 2012 Internet Architecture and Innovation (Massachusetts:MIT Press) pp37-57
[2]	Barabási A L 2013 Phil. Trans. R. Soc. A 371 20120375
[3]	Watts D J, Strogatz S H 1998 Nature 393 440
[4]	Barabási A L 1999 Science 286 509
[5]	Wang X F, Chen G 2012 Network Science:An Introduction (Beijing:Higher Education Press) pp3-26 (in Chinese)[汪小帆, 李翔, 陈关荣2012网络科学导论(北京:高等教育出版社)第3–26页]
[6]	Chen S, Huang W, Cattani C, Altieri G 2001 Phys. Rev. Lett. 86 3196
[7]	Guimerà R, Díaz-Guilera A, Vega-Redondo F, Cabrales A, Arenas A 2002 Phys. Rev. Lett. 89 248701
[8]	Zhao L, Lai Y C, Park K, Ye N 2005 Phys. Rev. E 71 026125
[9]	Liu Z, Hu M B, Jiang R, Wang W X, Wu Q S 2007 Phys. Rev. E 76 037101
[10]	Zhang G Q, Wang D, Li G J 2007 Phys. Rev. E 76 017101
[11]	Huang W, Chow T W S 2010 Chaos 20 033123
[12]	Yan G, Zhou T, Hu B, Fu Z Q, Wang B H 2006 Phys. Rev. E 73 046108
[13]	Wu Z X, Peng G, Wong W M, Yeung K H 2008 J. Stat. Mech. 2008 11002
[14]	Wang W X, Wang B H, Yin C Y, Xie Y B, Zhou T 2006 Phys. Rev. E 73 026111
[15]	Pu C L, Pei W J 2010 Acta Phys. Sin. 59 3841 (in Chinese)[濮存来, 裴文江2010物理学报 59 3841]
[16]	Yang H X, Tang M 2014 Physica A 402 1
[17]	Wang D, Jing Y, Zhang S 2008 Physica A 387 3001
[18]	Wang W X, Yin C Y, Yan G, Wang B H 2006 Phys. Rev. E 74 016101
[19]	Ling X, Hu M B, Jiang R, Wu Q S 2010 Phys. Rev. E 81 016113
[20]	Zhang H, Liu Z H, Tang M, Hui P M 2007 Phys. Lett. A 364 177
[21]	Danila B, Yu Y, Marsh J A, Bassler K E 2006 Phys. Rev. E 74 046106
[22]	Solé-Ribalta A, Gómez S, Arenas A 2016 Phys. Rev. Lett. 116 108701
[23]	Nian X, Fu H 2014 Physica A 410 421
[24]	Pu C, Li S, Yang X, Yang J 2016 Physica A 447 261
[25]	Zhou J, Yan G, Lai C H 2013 Europhys. Lett. 102 28002
[26]	Zhuo Y, Peng Y, Liu C, Liu Y, Long K 2011 Physica A 390 2401
[27]	Du W B, Zhou X L, Chen Z, Cai K Q, Cao X B 2014 Chaos Soliton. Fract. 68 72
[28]	Tan F, Wu J, Xia Y, Chi K T 2014 Phys. Rev. E 89 062813
[29]	Du W B, Zhou X L, Jusup M, Wang Z 2016 Sci. Rep. 6 19059
[30]	Li G Y, Xu Z K, Xiong C, Yang C, Zhang S, Chen Y, Xu S G 2011 IEEE Wireless Commun. 18 28
[31]	Heinzelman W R, Chandrakasan A, Balakrishnan H 2014 IEEE Trans. Ind. Inform. 10 766
[32]	Goh K I, Kahng B, Kim D 2001 Phys. Rev. Lett. 87 278701
[33]	Chen Y, Zhao Q 2005 IEEE Commun. Lett. 9 976

施引文献

[1]	van Schewick B 2012 Internet Architecture and Innovation (Massachusetts:MIT Press) pp37-57
[2]	Barabási A L 2013 Phil. Trans. R. Soc. A 371 20120375
[3]	Watts D J, Strogatz S H 1998 Nature 393 440
[4]	Barabási A L 1999 Science 286 509
[5]	Wang X F, Chen G 2012 Network Science:An Introduction (Beijing:Higher Education Press) pp3-26 (in Chinese)[汪小帆, 李翔, 陈关荣2012网络科学导论(北京:高等教育出版社)第3–26页]
[6]	Chen S, Huang W, Cattani C, Altieri G 2001 Phys. Rev. Lett. 86 3196
[7]	Guimerà R, Díaz-Guilera A, Vega-Redondo F, Cabrales A, Arenas A 2002 Phys. Rev. Lett. 89 248701
[8]	Zhao L, Lai Y C, Park K, Ye N 2005 Phys. Rev. E 71 026125
[9]	Liu Z, Hu M B, Jiang R, Wang W X, Wu Q S 2007 Phys. Rev. E 76 037101
[10]	Zhang G Q, Wang D, Li G J 2007 Phys. Rev. E 76 017101
[11]	Huang W, Chow T W S 2010 Chaos 20 033123
[12]	Yan G, Zhou T, Hu B, Fu Z Q, Wang B H 2006 Phys. Rev. E 73 046108
[13]	Wu Z X, Peng G, Wong W M, Yeung K H 2008 J. Stat. Mech. 2008 11002
[14]	Wang W X, Wang B H, Yin C Y, Xie Y B, Zhou T 2006 Phys. Rev. E 73 026111
[15]	Pu C L, Pei W J 2010 Acta Phys. Sin. 59 3841 (in Chinese)[濮存来, 裴文江2010物理学报 59 3841]
[16]	Yang H X, Tang M 2014 Physica A 402 1
[17]	Wang D, Jing Y, Zhang S 2008 Physica A 387 3001
[18]	Wang W X, Yin C Y, Yan G, Wang B H 2006 Phys. Rev. E 74 016101
[19]	Ling X, Hu M B, Jiang R, Wu Q S 2010 Phys. Rev. E 81 016113
[20]	Zhang H, Liu Z H, Tang M, Hui P M 2007 Phys. Lett. A 364 177
[21]	Danila B, Yu Y, Marsh J A, Bassler K E 2006 Phys. Rev. E 74 046106
[22]	Solé-Ribalta A, Gómez S, Arenas A 2016 Phys. Rev. Lett. 116 108701
[23]	Nian X, Fu H 2014 Physica A 410 421
[24]	Pu C, Li S, Yang X, Yang J 2016 Physica A 447 261
[25]	Zhou J, Yan G, Lai C H 2013 Europhys. Lett. 102 28002
[26]	Zhuo Y, Peng Y, Liu C, Liu Y, Long K 2011 Physica A 390 2401
[27]	Du W B, Zhou X L, Chen Z, Cai K Q, Cao X B 2014 Chaos Soliton. Fract. 68 72
[28]	Tan F, Wu J, Xia Y, Chi K T 2014 Phys. Rev. E 89 062813
[29]	Du W B, Zhou X L, Jusup M, Wang Z 2016 Sci. Rep. 6 19059
[30]	Li G Y, Xu Z K, Xiong C, Yang C, Zhang S, Chen Y, Xu S G 2011 IEEE Wireless Commun. 18 28
[31]	Heinzelman W R, Chandrakasan A, Balakrishnan H 2014 IEEE Trans. Ind. Inform. 10 766
[32]	Goh K I, Kahng B, Kim D 2001 Phys. Rev. Lett. 87 278701
[33]	Chen Y, Zhao Q 2005 IEEE Commun. Lett. 9 976

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无标度网络中基于能量的混合路由策略

1. 南京理工大学计算机科学与工程学院, 南京 210094

通信作者: 濮存来, pucunlai@njust.edu.cn

基金项目:

国家自然科学基金（批准号：61304154）、教育部博士点基金（批准号：20133219120032）、中国博士后基金（批准号：2013M541673）和中国博士后特别资助（批准号：2015T80556）资助的课题.

收稿日期: 2016-07-16

修回日期: 2016-08-21

刊出日期: 2016-12-05

摘要: 针对节点能量受限的静态无标度网络，提出了一种基于能量和最短路径相结合的路由策略.该策略综合考虑邻居节点的能量水平和其到目的节点的最短路径长度，利用控制参数调节二者的权重.仿真结果表明，存在最佳的值使得网络生存时间和数据包到达数达到最大值.最后，基于提出的路由策略研究了网络结构特征与网络生存时间之间的关系.

关键词:

Energy-based hybrid routing strategy for scale-free networks

1.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant No. 61304154), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20133219120032), the Postdoctoral Science Foundation of China (Grant No. 2013M541673), and the China Postdoctoral Science Special Foundation (Grant No. 2015T80556).

Received Date: 16 July 2016

Accepted Date: 21 August 2016

Published Online: 05 December 2016

Abstract: The infrastructures such as the internet networks, and phone networks, and their traffic capacity are well discussed in the field of network science. However, there is another type of communication infrastructure, such as the wireless sensor networks, which are usually deployed in tough environments to perform specific tasks. This kind of network usually has limited power supply, and thus the main issue is how to make good use of the energy and prolong the network lifetime. In this paper, we investigate the transport process in power-limited communication networks. We use the complex network models to generate the scale-free networks. We assign each node E0 (a constant) unit of energy and an infinite queue with the first-in-first-out rule for buffering packets. In the traffic model, every node generates packets with a constant rate . The packets' destination nodes are randomly chosen from the network. At each time step, every node delivers at most C packets. If a packet's destination node is among the neighbors of the current node, the packet will be delivered to the destination node directly and then be discarded from the destination node. Otherwise, the packet will be forwarded to a neighbor of the current node with a given routing strategy. In the delivery of a packet, the node consumes a fixed amount of energy, and will die out when it uses up its energy. We propose a hybrid routing strategy for the power-limited scale-free networks based on both the node energy and the shortest path. Specifically, in the routing strategy, we consider the residual energy of neighbor nodes and the shortest path lengths between the neighbor nodes and the destination, and utilize a free parameter to adjust their relative importance. Simulation results demonstrate that there are optimal control parameters which correspond to the maximum network lifetime and the maximum number of delivered packets. According to the proposed routing strategy, we further study the relation between the network topological structure and network lifetime. We find that the more homogeneous the network, the larger the maximum network lifetime is. Moreover, we obtain that the maximum network lifetime gradually increases with the average node degree increasing, but almost decreases linearly with the network scale increasing. In this paper we discuss the network lifetime from the perspective of network science, and give more insights into the transport process on complex networks. In addition, our work provides some clues of how to design the efficient routing strategies for the power-limited communication networks.

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