It is the cache of ${baseHref}. It is a snapshot of the page. The current page could have changed in the meantime.
Tip: To quickly find your search term on this page, press Ctrl+F or ⌘-F (Mac) and use the find bar.

A Self-adapted Anycast Routing Algorithm Based on Mobile Agent in Wireless Sensor Network | Zhang | Journal of Networks
Journal of Networks, Vol 6, No 2 (2011), 206-213, Feb 2011
doi:10.4304/jnw.6.2.206-213

A Self-adapted Anycast Routing Algorithm Based on Mobile Agent in Wireless Sensor Network

Nan Zhang, Jianhua Zhang

Abstract


To reduce the transmissions among sensor nodes and prolong the lifecycle of the wireless sensor network, the immune theory and anycast technique were brought into the mobile agent routing mechanism, and an immune-based MA anycast routing algorithm was put forward. In this algorithm, the diversity and self-adaptation characters of artificial immune system were used to find out the optimal data source searching order, and then the anycast strategy was used to select a node in multiple sensing nodes of a data source. MA migrated in the nodes which was satisfied the energy conditions. The MA anycast routing mechanism was used to balance the energy consumption of sensors. Simulation results show that the proposed algorithm can reduce the energy consumption and prolong the lifetime of WSN effectively.


Keywords


WSN; MA; anycast; immune; data source

References


[1] Akyildiz I F, Su W, Sankarasubramaniam Y, Cayirci E. A survey on sensor networks[J]. IEEE Communications Magazine, 2002, 40(8):102-114.
doi:10.1109/MCOM.2002.1024422

[2] Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. proc of the ACM MobiCom’00, Boston, MA, 2000:56-67

[3] Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, and Silva F. Directed diffusion for wireless sensor networking[J]. IEEE/ACM Trans. on networking, 2003, 11(1): 2-15
doi:10.1109/TNET.2002.808417

[4] Heinzelman W, Chandrakasan A, Balakrishnan H. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. IEEE proc of the 33rd Hawaii International conference on System Sciences, 2000:3005-3014

[5] Qi H R, Xu Y Y, and Wang X L. Mobil agent-based collaborative signal and information processing in sensor networks[C]. Proc. IEEE, 2003, 9l(8): 1172-1183.

[6] Iqbal M, Gondal I, Dooley L. Energy aware neighborhood protocol for anycast routing in Ad Hoc sensor networks[C] proceedings of 14th IEEE International Conference on Networks (ICON’06). Singapore, 2006,2:1-6

[7] Thepviojanapong N, Tobe Y, Sezaki K. HAR: hierarchy-based anycast routing protocol for wireless sensor wntworks[C] proceedings of Symposium on application and the internet workshops. 2005:204-212

[8] Kim J, Lin X J, shroff N B, et al. On maximizing the lifetime of delay-sensitive wireless sensor networks with anycast[C]. Proceedings of INFOCOM’08. 2008:807-815

[9] Hou Y T, Yi S, Sherali H D. Optimal base station selection for anycast routing in wireless sensor networks[J]. IEEE Transaction in vehicular technology, 2006,55(3): 813-821
doi:10.1109/TVT.2006.873822

[10] Jia W, Xuan D, Tu W Q, et al. Distributed admission control for anycast flows[J]. Transactions on parallel and distributed systems, 2004,15(8):673-686

[11] Qi Hairong, S Sitharama Iyengar, Krishnendu C. Multiresolution Data Integration Using Mobile Agents in Distributed Sensor Networks [J] IEEE Transactions on systems, man, and cybernetics—Part C: applications and reviews. 2001.31(8):383-391

[12] Rajagopalan R, Mohan C K, Varshney P, et al. Multi-objective mobile agent routing in wireless sensor networks[J]. IEEE Transition congress on evolutionary computation, 2005, 5(5):1730-1737

[13] Wang Ju, Cao Yongtao, Mi Zhengkun. The Simulant Annealing Solution for the Routing Problem of Mobile Agents in Wireless Sensor Networks[J]. Journal of Nanjing University of Pots and Telecommunications, 2007, 27(1):64-68(in Chinese)

[14] Wu Q, Rao N S V, et al. On computing mobile agent routes for data fusion in distributed sensor networks[J]. IEEE Transactions on knowledge and data engineering. 2004, 16(6):740-753
doi:10.1109/TKDE.2004.12

[15] Wu K, Gao Y, Li F, et al. Lightweight deployment-aware scheduling for wireless sensor networks[J]. Mobile networks and applications. 2005, 10(6):837-652
doi:10.1007/s11036-005-4442-8

[16] S. Hofmeyr, S. Forrest. Arichitecture for an Artificial Immune System[J]. Evolutionary Computation Journal, 1999,7(1):45-68.

[17] LI Tao. “A Risk Evaluation Based on Immune System for Network Security”[J]. Science in China, Ser. E, 2005,35(8): 798-816. (in Chinese)

[18] GONG Mao-Guo,HAO Lin,JIAO Li-Cheng,WANG Xiao-Hua,SUN Yi-Fei. Data reduction based on Artificial Immune System[J]. Journal of Software, 2009, 20(4):804-814 (in Chinese)

[19] ZHANG Nan,ZHANG Jian-hua,LI Zhi-shu,YANG Xian-ze. “Data Fusion Mechanism Based on Immune in Wireless Sensor Network”[J]. Journal of Chinese Computer Systems, 2009. 30(3) :454-459. (in Chinese)

[20] Endoh, S. Toma, N. Yamada, K. Immune algorithm for n-TSP[C]. Proc. of 1998 IEEE International Conference on Systems, Man, and Cybernetics. 1998,10: 3844-3849 vol.4.

[21] Michael Affenzeller, Rene Mayrhofer. Generic Heuristics for Combinatorial Optimization Problems[C]. Proc. of the 9th International Conference on Operational Research 2002.


Full Text: PDF


Journal of Networks (JNW, ISSN 1796-2056)

Copyright @ 2006-2014 by ACADEMY PUBLISHER – All rights reserved.