Automatic Learning-based MANET Cross-Layer Parameter Configuration

Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.

[1]  Amir F. Atiya,et al.  An algorithmic approach to adaptive state filtering using recurrent neural networks , 2001, IEEE Trans. Neural Networks.

[2]  Vipul Gupta,et al.  Freeze-TCP: a true end-to-end TCP enhancement mechanism for mobile environments , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[3]  Klara Nahrstedt,et al.  On setting TCP's congestion window limit in mobile ad hoc networks , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[4]  Jim Dowling,et al.  Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Mani B. Srivastava,et al.  Adaptive frame length control for improving wireless link throughput, range, and energy efficiency , 1998, Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Gateway to the 21st Century (Cat. No.98.

[6]  Masayuki Murata,et al.  Performance improvement of TCP on a wireless ad hoc network , 2003, The 57th IEEE Semiannual Vehicular Technology Conference, 2003. VTC 2003-Spring..

[7]  Kwan-Woong Kim,et al.  A new tuning maximum congestion window for improving TCP performance in MANET , 2005, 2005 Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05).

[8]  Paramvir Bahl,et al.  A rate-adaptive MAC protocol for multi-Hop wireless networks , 2001, MobiCom '01.

[9]  Erol Gelenbe,et al.  Self-aware networks and QoS , 2004, Proceedings of the IEEE.

[10]  Talib S. Hussain,et al.  Adaptive reconfiguration of data networks using genetic algorithms , 2004, Appl. Soft Comput..

[11]  Lex Weaver,et al.  A Multi-Agent Policy-Gradient Approach to Network Routing , 2001, ICML.

[12]  Wilpen L. Gorr,et al.  Comparative study of artificial neural network and statistical models for predicting student grade point averages , 1994 .

[13]  D. Caprioni,et al.  Small unit operations situation awareness system (SUO-SAS) radio architecture and system field testing results , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[14]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.

[15]  Murat Yuksel,et al.  Large-Scale Network Parameter Configuration Using an On-Line Simulation Framework , 2008, IEEE/ACM Transactions on Networking.

[16]  Ιωαννησ Τσιτσικλησ,et al.  PROBLEMS IN DECENTRALIZED DECISION MAKING AND COMPUTATION , 1984 .

[17]  Zhisheng Niu,et al.  A delayed adaptive retransmission scheme for false route failure in MANET , 2004, APCC/MDMC '04. The 2004 Joint Conference of the 10th Asia-Pacific Conference on Communications and the 5th International Symposium on Multi-Dimensional Mobile Communications Proceeding.

[18]  Guohong Cao,et al.  Adaptive power-aware prefetch in wireless networks , 2004, IEEE Transactions on Wireless Communications.

[19]  U. Ligges.  Review of An R and S-PLUS companion to applied regression by J. Fox, Sage Publications, Thousand Oaks, California 2002 , 2003 .

[20]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[21]  Peter Steenkiste,et al.  Improving wireless LAN performance via adaptive local error control , 1998, Proceedings Sixth International Conference on Network Protocols (Cat. No.98TB100256).

[22]  Mani B. Srivastava,et al.  Adaptive radio for multimedia wireless links , 1999, IEEE J. Sel. Areas Commun..

[23]  Benjamin Van Roy,et al.  Consensus Propagation , 2005, IEEE Transactions on Information Theory.

[24]  J. Fox.  Nonparametric Regression Appendix to An R and S-PLUS Companion to Applied Regression , 2002 .

[25]  H. Gharavi,et al.  Cross-layer feedback control for video communications via mobile ad-hoc networks , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[26]  Carl W. Steinbach.  A Reinforcement-Learning Approach to Power Management , 2002 .

[27]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.