Improved routing strategy based on gravitational field theory

doi:10.1088/1674-1056/24/10/108901

Abstract Routing and path selection are crucial for many communication and logistic applications. We study the interaction between nodes and packets and establish a simple model for describing the attraction of the node to the packet in transmission process by using the gravitational field theory, considering the real and potential congestion of the nodes. On the basis of this model, we propose a gravitational field routing strategy that considers the attractions of all of the nodes on the travel path to the packet. In order to illustrate the efficiency of proposed routing algorithm, we introduce the order parameter to measure the throughput of the network by the critical value of phase transition from a free flow phase to a congested phase, and study the distribution of betweenness centrality and traffic jam. Simulations show that, compared with the shortest path routing strategy, the gravitational field routing strategy considerably enhances the throughput of the network and balances the traffic load, and nearly all of the nodes are used efficiently.

Keywords: complex network network transport routing strategy gravitational field theory congestion

Received: 13 February 2015
Revised: 14 April 2015
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	89.20.Hh	(World Wide Web, Internet)

Fund: Project supported by the Technology and Development Research Project of China Railway Corporation (Grant No. 2012X007-D) and the Key Program of Technology and Development Research Foundation of China Railway Corporation (Grant No. 2012X003-A).

Corresponding Authors: Song Hai-Quan
E-mail: songmapnet@163.com

Cite this article:

Song Hai-Quan (宋海权), Guo Jin (郭进) Improved routing strategy based on gravitational field theory 2015 Chin. Phys. B 24 108901

[1]	Watts D J and Strogatz S H 1998 Nature 393 440
[2]	Barabási A L and Albert R 1999 Science 286 509
[3]	Newman M E J 2003 SIAM Rev. 45 167
[4]	Boccaletti S, Latora V, Moreno Y, Chavez M and Hwang D U 2006 Phys. Rep. 424 175
[5]	Strogatz S H 2001 Nature 410 268
[6]	Melanie M 2006 Artificial Intelligence 170 1194
[7]	Ali A A, Fortuna L, Frasca M, Rashid M T and Xibilia M G 2011 Chaos, Solitons & Fractals 44 306
[8]	Carmi S, Wu Z, Lpez E, Havlin S and Stanley H E 2007 Eur. Phys. J. B 57 165
[9]	Strogatz S H 2005 Nature 433 365
[10]	Naimzada A K and Ricchiuti G 2009 Chaos, Solitons & Fractals 41 764
[11]	Holme P 2003 Advances in Complex Systems 6 163
[12]	Daniele D M, Luca D A, Ginestra B and Matteo M 2009 Phys. Rev. E 79 015101R
[13]	Goh K I, Kahng B and Kim D 2001 Phys. Rev. Lett. 87 278701
[14]	Chen H L, Liu Z X, Chen Z Q and Yuan Z Z 2009 Acta Phys. Sin. 58 6068(in Chinese)
[15]	Yan G, Zhou T, Hu B, Fu Z Q and Wang B H 2006 Phys. Rev. E 73 046108
[16]	Bogdan D, Sun Y D and Kevin E B 2009 Phys. Rev. E 80 066116
[17]	Shen Y, Pei Y J, Wang K and Wang S P 2009 Chin. Phys. B 18 3783
[18]	Noh J D and Rieger H 2004 Phys. Rev. Lett. 92 118701
[19]	Wang W X, Wang B H, Yin C Y, Xie Y B and Zhou T 2006 Phys. Rev. E 73 026111
[20]	Echenique P 2004 Phys. Rev. E 70 056105
[21]	Zhao L, Ahmed Y, Dubai A and Geyong M 2011 Simul. Mod. Pract. Th. 19 1415
[22]	Wang D, Yu H, Jing Y W, Jiang N and Zhang S Y 2009 Acta Phys. Sin. 58 6802(in Chinese)
[23]	Dong C F, Ma X, Wang G W, Sun X Y and Wang B H 2009 Physica A 388 4651
[24]	Dong C F, Ma X and Wang B H 2010 Phys. Lett. A 374 1326
[25]	Dong C F, Ma X, Wang B H and Sun X Y 2010 Physica A 389 3274
[26]	Chen B K, Xie Y B, Tang W, Dong C F, Shi D M and Wang B H 2012 Physica A 391 2730
[27]	Chen B K, Dong C F, Liu Y K, Tang W, Zhang W Y, Liu J and Wang B H 2012 Comput. Phys. Commun. 183 2081
[28]	Guimerá R, D'ı az-Guilera A, Vega-Redondo F, Cabrales A and Arenas A 2002 Phys. Rev. Lett. 89 248701
[29]	Danila B, Yu Y, Marsh J A and Bassler K E 2007 Chaos 17 026102
[30]	Danila B, Sun Y D and Bassler K E 2009 Phys. Rev. E 80 066116
[31]	Kawamoto H and Igarashi A 2012 Physica A 391 895
[32]	Qian J H and Han D D 2009 Acta Phys. Sin. 58 3028(in Chinese)
[33]	Qian J H and Han D D 2009 Physica A 388 4248
[34]	Jung W S, Wang F Z and Stanley H E 2008 Europhys. Lett. 81 48005
[35]	Maniadakis D and Varoutas D 2014 Physica A 405 114
[36]	Liu G and Li Y S 2012 Acta Phys. Sin. 61 248901(in Chinese)
[37]	Liu G, Li Y S, Yang J, Cai G L and Zhang X P 2014 Int. J. Geograph. Inform. Sci.28 39
[38]	Liu G, Li Y S and Zhang X P 2013 Chin. Phys. B 22 068901
[39]	Arenas A, Diaz G A and Guimera R 2001 Phys. Rev. Lett. 86 3196

[1]	Analysis of cut vertex in the control of complex networks Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Chin. Phys. B, 2023, 32(2): 028902.
[2]	Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Chin. Phys. B, 2023, 32(1): 018903.
[3]	Effect of observation time on source identification of diffusion in complex networks Chaoyi Shi(史朝义), Qi Zhang(张琦), and Tianguang Chu(楚天广). Chin. Phys. B, 2022, 31(7): 070203.
[4]	An extended improved global structure model for influential node identification in complex networks Jing-Cheng Zhu(朱敬成) and Lun-Wen Wang(王伦文). Chin. Phys. B, 2022, 31(6): 068904.
[5]	Characteristics of vapor based on complex networks in China Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
[6]	Explosive synchronization: From synthetic to real-world networks Atiyeh Bayani, Sajad Jafari, and Hamed Azarnoush. Chin. Phys. B, 2022, 31(2): 020504.
[7]	Robust H_∞ state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks Yahan Deng(邓雅瀚), Zhongkai Mo(莫中凯), and Hongqian Lu(陆宏谦). Chin. Phys. B, 2022, 31(2): 020503.
[8]	Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为). Chin. Phys. B, 2022, 31(10): 100504.
[9]	Explosive synchronization in a mobile network in the presence of a positive feedback mechanism Dong-Jie Qian(钱冬杰). Chin. Phys. B, 2022, 31(1): 010503.
[10]	LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks Gui-Qiong Xu(徐桂琼), Lei Meng(孟蕾), Deng-Qin Tu(涂登琴), and Ping-Le Yang(杨平乐). Chin. Phys. B, 2021, 30(8): 088901.
[11]	Complex network perspective on modelling chaotic systems via machine learning Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Chin. Phys. B, 2021, 30(6): 060506.
[12]	Dynamical robustness of networks based on betweenness against multi-node attack Zi-Wei Yuan(袁紫薇), Chang-Chun Lv(吕长春), Shu-Bin Si(司书宾), and Dong-Li Duan(段东立). Chin. Phys. B, 2021, 30(5): 050501.
[13]	Exploring individuals' effective preventive measures against epidemics through reinforcement learning Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞). Chin. Phys. B, 2021, 30(4): 048901.
[14]	Improving robustness of complex networks by a new capacity allocation strategy Jun Liu(刘军). Chin. Phys. B, 2021, 30(1): 016401.
[15]	An extended cellular automata model with modified floor field for evacuation Da-Hui Qin(秦大辉), Yun-Fei Duan(段云飞), Dong Cheng(程栋), Ming-Zhu Su(苏铭著), Yong-Bo Shao(邵永波). Chin. Phys. B, 2020, 29(9): 098901.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Improved routing strategy based on gravitational field theory

Cite this article:

Online attention