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Abstract
By using hyper-graph theory, this paper proposes a QoS adaptive topology configuration (QATC) algorithm to effectively control large-scale topology and achieve robust data transmitting in synchronous wireless sensor networks. Firstly, a concise hyper-graph model is abstracted to analyze the large-scale and high-connectivity network. Secondly, based on the control theory of biologic “Cell Mergence”, a novel self-adaptive topology configuration algorithm is used to build homologous perceptive data logic sub-network for data aggregation. Compared with Flooding, Directed Diffusion, and Energy Aware Directed Diffusion protocols, the simulation proved that QATC algorithm can save more energy, e.g., about 23.7% in a large size network, and has less delay than the other algorithms. In dynamic experiments, QATC keeps a robust transmitting quality with 10%, 20% and 30% random failure nodes.
Keywords
wireless sensor network
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QoS
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topology
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synchronous network
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Ting Yang, Jiaowen Wu, Ang Li, Zhidong Zhang.
QoS adaptive topology configuration in synchronous wireless sensor networks.
Transactions of Tianjin University, 2010, 16(5): 354-358 DOI:10.1007/s12209-010-1407-1
| [1] |
Ammari H. M., Das S. K. Fault tolerance measures for largescale wireless sensor networks[J]. ACM Transactions on Autonomous and Adaptive Systems, 2009, 4(1): 1-23.
|
| [2] |
Klingbeil L, Wark T. A wireless sensor network for real-time indoor localisation and motion monitoring[C]. In: International Conference on Information Processing in Sensor Networks (IPSN 2008). St. Louis, Missouri, USA, 2008. 39–50.
|
| [3] |
Rickenbach P., Wattenhofer R. Algorithmic models of interference in wireless Ad Hoc and sensor networks[J]. IEEE/ACM Transactions on Networking, 2009, 17(1): 172-185.
|
| [4] |
Chatterjea S., Tim Nieberg T., Meratnia N., et al. A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks[J]. ACM Transactions on Sensor Networks, 2008, 4(4): 1-41.
|
| [5] |
Zhang Z., Sun Y., Liu Y., et al. Energy model in wireless sensor networks[J]. Journal of Tianjin University, 2007, 40(9): 1029-1034.
|
| [6] |
Krishnamachari B, Estrin D. Modelling data-centric routing in wireless sensor networks[C]. In: Proc in IEEE INFOCOM. New York, USA, 2002. 1269–1273.
|
| [7] |
Lee M, Wong V W S. An energy-aware spanning tree algorithm for data aggregation in wireless sensor net works[C]. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Canada, 2005. 300–303.
|
| [8] |
Younis O., Fahmy S. HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks[J]. IEEE Trans on Mobile Computing, 2004, 3(4): 366-379.
|
| [9] |
Zahra E, Hossien M. Automata based energy efficient spanning tree for data aggregation in wireless sensor networks[C]. In: 11th IEEE Singapore International Conference on Communication Systems (ICCS08). Singapore, 2008. 943–947.
|
| [10] |
Elson J, Girod L, Estrin D. Fine-grained network time synchronization using reference broadcasts[C]. In: Proceedings of the Fifth Symposium on Operating Systems Design and Implementation. Boston, MA, USA, 2002. 147–163.
|
| [11] |
Sichitiu M L, Veerarittiphan C. Simple, accurate time synchronization for wireless sensor networks[C]. In: IEEE Wireless Communications and Networking Conference, WCNC. New Orleans, LA, USA, 2003. 1266–1273.
|
| [12] |
Ganeriwal S, Kumar R, Adlakha S et al. Network-Wide Time Synchronization in Sensor Networks[R]. Technical Report UCLA, 2002.
|
| [13] |
Rerge C. Graph and Hypergraph[M]. 1973, Amsterdam: North-Holland.
|
| [14] |
Intanagonwiwat C., Govindan R., Estrin R., et al. Directed diffusion for wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2003, 11(1): 2-16.
|
| [15] |
Jisul C, Keecheon K. EADD: Energy aware directed diffusion for wireless sensor networks[C]. In: International Symposium on Parallel and Distributed Processing with Applications 2008(ISPA’08). Sydney, Australia, 2008. 779–783.
|