Component based ant routing protocols analysis over mobile ad hoc networks

Da-peng Qu , Xing-wei Wang , Min Huang

Journal of Central South University ›› 2013, Vol. 20 ›› Issue (9) : 2378 -2387.

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Journal of Central South University ›› 2013, Vol. 20 ›› Issue (9) : 2378 -2387. DOI: 10.1007/s11771-013-1747-9
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Component based ant routing protocols analysis over mobile ad hoc networks

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Abstract

To deeply exploit the mechanisms of ant colony optimization (ACO) applied to develop routing in mobile ad hoc networks (MANETS), some existing representative ant colony routing protocols were analyzed and compared. The analysis results show that every routing protocol has its own characteristics and competitive environment. No routing protocol is better than others in all aspects. Therefore, based on no free lunch theory, ant routing protocols were decomposed into three key components: route discovery, route maintenance (including route refreshing and route failure handling) and data forwarding. Moreover, component based ant routing protocol (CBAR) was proposed. For purpose of analysis, it only maintained basic ant routing process, and it was simple and efficient with a low overhead. Subsequently, different mechanisms used in every component and their effect on performance were analyzed and tested by simulations. Finally, future research strategies and trends were also summarized.

Keywords

routing protocol / mobile ad hoc networks / ant colony optimization / route discovery / route maintenance / data forwarding

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Da-peng Qu, Xing-wei Wang, Min Huang. Component based ant routing protocols analysis over mobile ad hoc networks. Journal of Central South University, 2013, 20(9): 2378-2387 DOI:10.1007/s11771-013-1747-9

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References

[1]

DorigoM, StutzleTAnt colony optimization [M], 2004MassachusettsMIT Press

[2]

DorigoM, GambardellaL M. Ant colony system: A cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66

[3]

ManiezzoV, ColorniA. The ant system applied to the quadratic assignment problem [J]. IEEE Transactions on Knowledge and Data Engineering, 1999, 11(5): 769-778

[4]

KwangM S, WengH S. Ant colony optimization for routing and load-balancing: Survey and new directions [J]. IEEE Transactions on Systems, Man and Cybernetics, 2003, 33(5): 560-572

[5]

DucatelleF, CaroG D, GambardellaL M. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks [J]. Swarm Intelligence, 2010, 4(3): 1-33

[6]

HamidehS, SamJ. A survey of ant-based routing algorithms for mobile ad hoc networks [C]. International Conference on Signal Processing Systems, 2009SingaporeIEEE323-329

[7]

KalaavathiB, MadhaviS, VijayaragavanS, DuraiswamyK. Review of ant based routing protocols for manet [C]. ICCCN (International Conference on Computing, Communication and Networking), 2008Virgin IslandsIEEE1-9

[8]

JohnS B, HarshM. A probabilistic emergent routing algorithm for mobile ad hoc networks [C]. Modeling and Optimization in Mobile, AdHoc and Wireless Networks, 2003Sophia-AntipolisIEEE1-10

[9]

MesutG, MartinK, ImedB. Ant routing algorithm (ARA) for mobile multi-hop ad-hoc networks-new features and results [C]. Med-Hoc-Net’2003 Mediterranean Workshop on Ad-Hoc Networks, 2003MahdiaACM120-138

[10]

HusseinO H, SaadawiT N, MyungJ L. Probability routing algorithm for mobile ad hoc networks’ resources management [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(12): 2248-2259

[11]

DucatelleFAdaptive routing in ad hoc wireless multi-hop networks [D], 2007Lugano University, LuganoSwitzerland

[12]

LauraR, MatteoB, GianlucaR. On ant routing algorithms in ad hoc networks with critical connectivity [J]. Ad Hoc Networks, 2008, 6(6): 827-859

[13]

MartinR, StephenW. Termite: Ad-hoc networking with stigmergy [C]. Global Telecommunications Conference, 2003San FranciscoIEEE2937-2941

[14]

FernandoC, TeresaV. Simple ant routing algorithm strategies for a multipurpose manet model [J]. Ad Hoc Networks, 2010, 8(8): 810-823

[15]

DhillonS S, ArbonaX, MieghemP V. Ant routing in mobile ad hoc networks [C]. International Conference on Networking and Services, 2007AthensIEEE67-75

[16]

DorigoM, CaroG D. AntNet: Distributed stigmergetic control for communications networks [J]. Journal of Artificial Intelligence Research, 1998, 9(1): 317-365

[17]

ZhengX-q, GuoW, GeL-j, LiuR-ting. A cross-layer design and ant colony optimization based load-balancing routing protocol for ad hoc networks [J]. Chinese Journal of Electronics, 2006, 34(7): 1199-1208

[18]

LiuZ-y, MartaZ K, CostasC. A biologically inspired qos routing algorithm for mobile ad hoc networks [J]. International Journal of Wireless and Mobile Computing, 2010, 4(2): 64-75

[19]

LiuZ-y, MartaZ K, CostasC. A self-organized emergent routing mechanism for mobile ad hoc networks [J]. European Transactions on Telecommunications, Special Issue on Self-organisation in Mobile Network, 2005, 16(5): 457-470

[20]

WangJ-p, EseosaO, ParimalaT, RuppaK T. HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network [J]. Ad Hoc Networks, 2009, 7(4): 690-705

[21]

DavidH W, WilliamG M. No free theorems for optimization [J]. IEEE Transactions on Evolutionary Computing, 1997, 1(1): 67-82

[22]

ElizabethM R, Chai-keongT. A review of current routing protocols for ad hoc mobile wireless networks [J]. IEEE Personal Communications, 1999, 6(2): 46-55

[23]

CharlesE P, ElizabethM R, SamirR D, MaheshK M. Performance comparison of two on-demand routing protocols for ad hoc networks [J]. IEEE Personal Communications Magazine, Special Issue on Ad hoc Networking, 2001, 8(1): 16-28

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