CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-11-06
Cited: 0
Clicked: 6754
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.
@article{title="Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network",
author="Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="10",
pages="1573-1590",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601585"
}
%0 Journal Article
%T Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network
%A Cheng-cheng Li
%A Ren-chao Xie
%A Tao Huang
%A Yun-jie Liu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 10
%P 1573-1590
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601585
TY - JOUR
T1 - Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network
A1 - Cheng-cheng Li
A1 - Ren-chao Xie
A1 - Tao Huang
A1 - Yun-jie Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 10
SP - 1573
EP - 1590
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601585
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.
[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.
Open peer comments: Debate/Discuss/Question/Opinion
<1>