[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

Cross-layer interactions in multihop wireless sensor networks: A constrained queueing model

Published: 17 December 2010 Publication History

Abstract

In this article, we propose a constrained queueing model to investigate the performance of multihop wireless sensor networks. Specifically, the cross-layer interactions of rate admission control, traffic engineering, dynamic routing, and adaptive link scheduling are studied jointly with the proposed queueing model. In addition, the stochastic network utility maximization problem in wireless sensor networks is addressed within this framework. We propose an adaptive network resource allocation scheme, called the ANRA algorithm, which provides a joint solution to the multiple-layer components of the stochastic network utility maximization problem. We show that the proposed ANRA algorithm achieves a near-optimal solution, that is, (1-ϵ) of the global optimum network utility where ϵ can be arbitrarily small, with a trade-off with the average delay experienced in the network. The proposed ANRA algorithm enjoys the merit of self-adaptability through its online nature and thus is of particular interest for time-varying scenarios such as multihop wireless sensor networks.

References

[1]
Akyildiz, I. F. and Kasimoglu, I. H. 2004. Wireless sensor and actor networks: Research challenges. Elsevier Comput. Networks.
[2]
Akyol, U., Andrews, M., Gupta, P., Hobby, J., Saniee, I., and Stolyar, A. 2008. Joint scheduling and congestion control in mobile ad-hoc networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom).
[3]
Chiang, M. 2005. Balancing transport and physical layer in wireless multihop networks: Jointly optimal congestion control and power control. IEEE J. Select. Areas Comm. 1, 104--116.
[4]
Chiang, M., Low, S. H., Calderbank, A. R., and Doyle, J. C. 2007. Layering as optimization decomposition: A mathematical theory of network architectures. Proc. IEEE 95, 255--312.
[5]
Eryilmaz, A. and Srikant, R. 2005. Fair resource allocation in wireless networks using queue-length-based scheduling and congestion control. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom).
[6]
Georgiadis, L., Neely, M. J., and Tassiulas, L. 2006. Resource Allocation and Cross-Layer Control in Wireless Networks. Foundations and Trends in Networking.
[7]
Gupta, A., Lin, X., and Srikant, R. 2007. Low-complexity distributed scheduling algorithms for wireless networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom).
[8]
Gupta, G. R. and Shroff, N. 2009. Delay analysis for multi-hop wireless networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom).
[9]
Jiang, L. and Walrand, J. 2008. A distributed csma algorithm for throughput and utility maximization in wireless networks. In Proceedings of the Allerton Conference of Communication Control, and Computing.
[10]
Joo, C. 2008. A local greedy scheduling scheme with provable performance guarantee. In Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).
[11]
Kelly, F., Maulloo, A., and Tan, D. 1998. Rate control for communication networks: Shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49, 237--252.
[12]
Lin, X. 2006. On characterizing the delay performance of wireless scheduling algorithms. In Proceedings of the Allerton Conference of Communication Control, and Computing.
[13]
Low, S. H. and Lapsley, D. E. 1999. Optimization flow control, i: Basic algorithm and convergence. IEEE/ACM Trans. Netw. 7, 861--875.
[14]
Modiano, E., Shah, D., and Zussman, G. 2006. Maximizing throughput in wireless networks via gossiping. In Proceedings of the ACM SIGMETRICS Conference.
[15]
Neely, M. J. 2003. Dynamic power allocation and routing for satellite and wireless networks with time varying channels. Ph.D. thesis, Masssachusetts Institute of Technology.
[16]
Neely, M. J. 2006. Energy optimal control for time varying wireless networks. IEEE Trans. Inf. Theory 52, 2915--2934.
[17]
Neely, M. J., Modiano, E., and Li, C.-P. 2008. Fairness and optimal stochastic control for heterogeneous networks. IEEE/ACM Trans. Netw. 16, 396--409.
[18]
Neely, M. J., Modiano, E., and Rohrs, C. E. 2005. Dynamic power allocation and routing for time-varying wireless networks. IEEE J. Select. Areas Comm. 23, 89--103.
[19]
Radunovic, B., Gkantsidis, C., Gunawardena, D., and Key, P. 2008. Horizon: Balancing tcp over multiple paths in wireless mesh network. In Proceedings of the 14th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiCom).
[20]
Shakkottai, S. and Srikant, R. 2008. Network Optimization and Control. Foundations and Trends in Networking.
[21]
Sharma, A. B., Golubchik, L., Govindan, R., and Neely, M. J. 2009. Dynamic data compression in multi-hop wireless networks. In Proceedings of the ACM SIGMETRICS Conference.
[22]
Song, Y. and Fang, Y. 2007. Distributed rate control and power control in resource-constrained wireless sensor networks. In Proceedings of the IEEE MILCOM Conference.
[23]
Srikant, R. 2003. The Mathematics of Internet Congestion Control. Birkhauser Boston.
[24]
Stolyar, A. 2005. Maximizing queueing network utility subject to stability: Greedy primal-dual algorithm. Queu. Syst. 50, 401--457.
[25]
Stolyar, A. 2006. Large deviations of queues under qos scheduling algorithms. In Proceedings of the Allerton Conference of Communication Control, and Computing.
[26]
Stolyar, A. 2008. Dynamic distributed scheduling in random access networks. J. Appl. Probab. 45, 297--313.
[27]
Tassiulas, L. and Ephremides, A. 1992. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Trans. Autom. Control 37, 1936--1949.
[28]
Trench, W. F. 2003. Introduction to Real Analysis. Prentice Hall.
[29]
Wu, X., Srikant, R., and Perkins, J. 2007. Scheduling efficiency of distributed greedy scheduling algorithms in wireless networks. IEEE Trans. Mobile Comput.
[30]
Yick, J., Mukherjee, B., and Ghosal, D. 2007. Wireless sensor network survey. Elsevier Comput. Networks.
[31]
Ying, L., Shakkottai, S., and Reddy, A. 2009. On combining shortest-path and back-pressure routing over multihop wireless networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom).

Cited By

View all

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Modeling and Computer Simulation
ACM Transactions on Modeling and Computer Simulation  Volume 21, Issue 1
December 2010
183 pages
ISSN:1049-3301
EISSN:1558-1195
DOI:10.1145/1870085
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2010
Accepted: 01 October 2009
Revised: 01 September 2009
Received: 01 May 2009
Published in TOMACS Volume 21, Issue 1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Cross-layer design
  2. online algorithms
  3. stochastic network optimization
  4. stochastic utility maximization

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)2
Reflects downloads up to 11 Dec 2024

Other Metrics

Citations

Cited By

View all

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media