Full Text:   <459>

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CLC number: TP393

On-line Access: 2017-12-04

Received: 2016-09-27

Revision Accepted: 2017-04-18

Crosschecked: 2017-11-06

Cited: 0

Clicked: 1920

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Cheng-cheng Li

http://orcid.org/0000-0003-3507-8935

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1573-1590

http://doi.org/10.1631/FITEE.1601585


Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network


Author(s):  Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu

Affiliation(s):  State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   lengcangche@bupt.edu.cn, renchao_xie@bupt.edu.cn, htao@bupt.edu.cn

Key Words:  Information-centric networking, Congestion control, Cross-layer design, Multihop wireless network


Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu. Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1573-1590.

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author="Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu",
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doi="10.1631/FITEE.1601585"
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Abstract: 
As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the multihop wireless network (MWN). In named-data MWNs, congestion control is a critical issue. Independent optimization for congestion control may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer congestion control is a potential method to enhance performance. There have been many cross-layer congestion control mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint congestion control, forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized congestion control, forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer congestion control mechanism for named-data MWNs. By comparison with the existing congestion control mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

命名数据多跳无线网络中的联合优化的拥塞控制、转发策略和链路调度

概要:作为一种非常有希望的未来网络架构,命名数据网络已经被公认为一种非常适合多跳无线网络的网络协议。在命名数据的多跳无线网络中,拥塞控制是一个关键问题。如果单独优化拥塞控制,而不考虑与其它协议层协同,那么有可能造成性能的严重降低。跨层优化的拥塞控制是一种提升性能的潜在方式。在利用互联网协议(internet protocol, IP)构造的多跳无线网络中,已经有很多跨层的拥塞控制机制。然而,这些机制无法应用在命名数据的多跳无线网络中,因为IP与命名数据网络的通信特点不同。本文研究了命名数据的多跳无线网络的联合拥塞控制、转发策略和链路调度问题。该问题被建模为一个网络效益最优化(network utility maximization, NUM)问题。基于近似次梯度算法,我们提出了名为JOCFS(Jointly optimized congestion control, forwarding strategy, and linkscheduling)的算法来求解NUM问题。就我们所知,我们的算法是命名数据的多跳无线网络中的第一个跨层的拥塞控制机制。通过与现有的拥塞控制机制对比,证明了JOCFS在网络吞吐量、公平性和待定兴趣表大小方面性能更优。

关键词:信息中心网络;拥塞控制;跨层优化;多跳无线网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Aggarwal, A., Imai, H., Katoh, N., et al., 1989. Finding k points with minimum spanning trees and related problems. Proc. 5th Annual Symp. on Computational Geometry, p.283-291.

[2]Agrawal, R., Gehrke, J., Gunopulos, D., et al., 1998. Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec., 27(2):94-105.

[3]Ankerst, M., Breunig, M.M., Kriegel, H.P., et al., 1999. Optics: ordering points to identify the clustering structure. SIGMOD Rec., 28(2):49-60.

[4]Aurenhammer, F., 1991. Voronoi diagrams—survey of a fundamental geometric data structure}. ACM Comput. Surv., 23(3):345-405.

[5]Chen, L.S., Cong, G., Jensen, C.S., et al., 2013. Spatial keyword query processing: an experimental evaluation. Proc. VLDB Endowm., 6(3):217-228.

[6]Chen, Y.Y., Suel, T., Markowetz, A., 2006. Efficient query processing in geographic web search engines. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.277-288.

[7]Cheng, C.H., Fu, A.W., Zhang, Y., 1999. Entropy-based subspace clustering for mining numerical data. Proc. 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.84-93.

[8]Christoforaki, M., He, J., Dimopoulos, C., et al., 2011. Text vs. space: efficient geo-search query processing. Proc. 20th ACM Int. Conf. on Information and Knowledge Management, p.423-432.

[9]Cong, G., Jensen, C.S., Wu, D.M., 2009. Efficient retrieval of the top-k most relevant spatial web objects. Proc. VLDB Endowm., 2(1):337-348.

[10]Ester, M., Kriegel, H.P., Sander, J., et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.226-231.

[11]Fan, J., Li, G.L., Zhou, L.Z., et al., 2012. SEAL: spatio-textual similarity search. Proc. VLDB Endowm., 5(9):824-835.

[12]Feige, U., Seltser, M., 1997. On the densest k -subgraph problem. Technical Report, the Weizmann Institute, Rehovot.

[13]Feige, U., Kortsarz, G., Peleg, D., 2001. The dense k-subgraph problem. Algorithmica, 29:410-421.

[14]Guo, D.S., Peuquet, D.J., Gahegan, M., 2003. ICEAGE: interactive clustering and exploration of large and high-dimensional geodata. GeoInformatica, 7(3):229-253.

[15]Hinneburg, A., Keim, D.A., 1999. Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering. Proc. 25th Int. Conf. on Very Large Data Bases, p.506-517.

[16]Jones, C.B., Purves, R., Ruas, A., et al., 2002. Spatial information retrieval and geographical ontologies an overview of the SPIRIT project. Proc. 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.387-388.

[17]Joshi, T., Joy, J., Kellner, T., et al., 2008. Crosslingual location search. Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.211-218.

[18]Khodaei, A., Shahabi, C., Li, C., 2010. Hybrid indexing and seamless ranking of spatial and textual features of web documents. LNCS, 6261:450-466.

[19]Komusiewicz, C., Sorge, M., 2012. Finding dense subgraphs of sparse graphs. Proc. 7th Int. Conf. on Parameterized and Exact Computation, p.242-251.

[20]Lee, D.T., 1982. On k-nearest neighbor Voronoi diagrams in the plane. IEEE Trans. Comput., 100(6):478-487.

[21]Leung, K.W.T., Lee, D.L., Lee, W.C., 2011. CLR: a collaborative location recommendation framework based on co-clustering. Proc. 34th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.305-314.

[22]Li, Z.S., Lee, K.C., Zheng, B.H., et al., 2011. IR-tree: an efficient index for geographic document search. IEEE Trans. Knowl. Data Eng., 23(4):585-599.

[23]Mai, H.T., Kim, J., Roh, Y.J., et al., 2013. STHist-C: a highly accurate cluster-based histogram for two and three dimensional geographic data points. GeoInformatica, 17(2):325-352.

[24]Ortega, E., Otera, I., Mancebo, S., 2014. TITIM GIS-tool: a GIS-based decision support system for measuring the territorial impact of transport infrastructures. Exp. Syst. Appl., 41(16):7641-7652.

[25]Saoussen, K., Sami, F., Takwa, T., et al., 2014. Tabu-based GIS for solving the vehicle routing problem. Exp. Syst. Appl., 41(14):6483-6493.

[26]Schikuta, E., 1996. Grid-clustering: an efficient hierarchical clustering method for very large data sets. Proc. 13th Int. Conf. on Pattern Recognition, p.101-105.

[27]Shamos, M.I., Hoey, D., 1975. Closest-point problems. 16th Annual Symp. on Foundations of Computer Science, p.151-162.

[28]Son, L.H., 2014. Optimizing municipal solid waste collection using chaotic particle swarm optimization in GIS based environments: a case study at Danang city, Vietnam. Exp. Syst. Appl., 41(18):8062-8074.

[29]Thomee, B., Rae, A., 2013. Uncovering locally characterizing regions within geotagged data. Proc. 22nd Int. Conf. on World Wide Web, p.1285-1296.

[30]Vaid, S., Jones, C.B., Joho, H., et al., 2005. Spatio-textual indexing for geographical search on the web. Advances in Spatial and Temporal Databases, p.218-235.

[31]Wei, L.Y., Zheng, Y., Peng, W.C., 2012. Constructing popular routes from uncertain trajectories. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.195-203.

[32]Wu, D.M., Yiu, M.L., Cong, G., et al., 2012. Joint top-k spatial keyword query processing. IEEE Trans. Knowl. Data Eng., 24(10):1889-1903.

[33]Yuan, J., Zheng, Y., Xie, X., 2012. Discovering regions of different functions in a city using human mobility and POIs. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.186-194.

[34]Zhang, F.Z., Wilkie, D., Zheng, Y., et al., 2013a. protectSensing the pulse of urban refueling behavior. Proc. ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing, p.13-22.

[35]Zhang, Q., Kang, J.H., Gong, Y.Y., et al., 2013b. Map search via a factor graph model. Proc. 22nd ACM Int. Conf. on Information and Knowledge Management, p.69-78.

[36]Zhou, Y.H., Xie, X., Wang, C., et al., 2005. Hybrid index structures for location-based web search. Proc. 14th ACM Int. Conf. on Information and Knowledge Management, p.155-162.

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