Tools for the selection of the transmission probability in the cluster formation phase for Event-Driven Wireless Sensor Networks

Mario Eduardo Rivero-Angeles; Izlian Yolanda Orea-Flores

doi:10.17533/udea.redin.15731

Authors

Mario Eduardo Rivero-Angeles National Polytechnic Institute https://orcid.org/0000-0003-1020-6806
Izlian Yolanda Orea-Flores National Polytechnic Institute

DOI:

https://doi.org/10.17533/udea.redin.15731

Keywords:

clustering, event-driven WSNs, Markovian analysis, transmission probability

Abstract

In the literature, it is common to find studies on Wireless Sensor Networks (WSNs) that consider the Carrier Sense Multiple Access (CSMA) protocol with a fixed transmission probability for means of the random access strategy. This is especially true for event-driven applications for clusteredbased architectures. However, due to the highly variable environment in these networks in terms of the number of nodes attempting a transmission (at the beginning of the cluster formation all nodes in the network contend for the channel, while at the end only a few nodes attempt a transmission), a fixed transmission probability may not entail an adequate performance. Specifically, the energy consumption may be too high by considering a fixed transmission strategy, because the use of a low transmission probability at the beginning of the cluster formation reduces the collision probability, but at the end entails long idle periods. In view of this, the effects of three different transmission probability strategies for event-driven WSNs are studied. Based on the obtained results, it is shown that a careful selection of the transmission probability is required in order to prolong the network lifetime.

|Abstract

= 370 veces | PDF (ESPAÑOL (ESPAÑA))

= 72 veces|

Downloads

Download data is not yet available.

Author Biographies

Mario Eduardo Rivero-Angeles, National Polytechnic Institute

Computer Networks and Communications Laboratory, Computer Research Center (CIC).

Izlian Yolanda Orea-Flores, National Polytechnic Institute

Professor, Academy of Telematics, Interdisciplinary Professional Unit in Engineering and Advanced Technologies (UPIITA).

References

Akiyldiz, W. Su, Y. Sankarasubramaniam, E. Cayirci. “A survey on sensor networks”. IEEE, Communications Magazine. Vol. 40. 2002. pp. 102-114. DOI: https://doi.org/10.1109/MCOM.2002.1024422

N. Bouabdallah, M. Rivero, B. Sericola. “Continuos monitoring using event-driven reporting for clusterbased wireless sensor networks”. IEEE Transactions on Vehicular Technology. Vol. 58. 2009. pp. 3460-3479. DOI: https://doi.org/10.1109/TVT.2009.2015330

W. Heinzelman, A. Chandrakasan, H. Balakrishnan. “An application-specific protocol architecture for wireless microsensor networks”. IEEE Transactions on Wireless Communication. Vol. 1. 2002. pp. 660- 670. DOI: https://doi.org/10.1109/TWC.2002.804190

O. Younis, S. Fahamy. Distributed clustering in AdHoc sensor networks: A hybrid, energy-efficient approach. Proceedings of the twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM). Hong Kong, China. 2004. pp. 629–640.

S. Bandyopadhyay, E. Coyle, An energy efficient hierarchical clustering algorithm for wireless sensor networks. Proceedings of the twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM). San Francisco, USA. 2003. pp. 1713–1723.

J. Kil-Woong. “Meta-heuristic algorithms for channel scheduling problem in wireless sensor networks”. International Journal of Communication Systems. Vol. 25. 2012. pp. 427-446. DOI: https://doi.org/10.1002/dac.1267

J. Fuu-Cheng, H. Der-Chen, Y. Chao-Tung, L. ChuHsing, W. Kuo-Hsiung. “Design strategy for optimizing power consumption of sensor node with Min(N,T) policy M/G/1 queuing models”. International Journal of Communication Systems. Vol. 25. 2012. pp. 652-671. DOI: https://doi.org/10.1002/dac.1288

A. Khan, S. Madani, K. Hayat, S. Khan. “Clusteringbased power-controlled routing for mobile wireless sensor networks”. International Journal of Communication Systems. Vol. 25. 2012. pp. 529-542. DOI: https://doi.org/10.1002/dac.1280

Z. Ping, S. Jianghong, Z. Zhihong, Z. Dezhong, Z. Liping, C. Huihuang. Performance evaluation of the IEEE 802.11 MAC for mobile wireless sensor networks. Proceedings of the 6th International Conference on Computer Science and Education (ICCSE). Singapore, Singapore. 2011. pp. 1094-1098. DOI: https://doi.org/10.1109/ICCSE.2011.6028825

Y. Sagduyu, A. Ephremides. “The problem of medium access control in wireless sensor networks”. IEEE Wireless Communications. Vol. 11. 2004. pp. 44-53. DOI: https://doi.org/10.1109/MWC.2004.1368896

K. Kredo, P. Mohapatra. “Medium access control in wireless sensor networks”. Computer Networks. Vol. 51. 2007. pp. 961-994. DOI: https://doi.org/10.1016/j.comnet.2006.06.012

M. Vuran, I. Akyildiz. “Spatial correlation-based collaborative medium access control in wireless sensor networks”. IEEE ACM Transactions on Networking. Vol. 14. 2006. pp. 316-329. DOI: https://doi.org/10.1109/TNET.2006.872544

I. Chatzigiannakis, A. Kinalis, S. Nikoletseas. Wireless sensor networks protocols for effi- cient collision avoidance in multi-path data propagation. Proceedings of the 1st ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks. Venice, Italy. 2004. pp. 8-16. DOI: https://doi.org/10.1145/1023756.1023759

I. Tinnirello, G. Bianchi. “Interference estimation in IEEE 802.11 networks”. IEEE Control Systems Magazine. Vol. 30. 2010. pp. 30-43. DOI: https://doi.org/10.1109/MCS.2009.935570

P. Patras, A. Banchs, P. Serrano, A. Azcorra. “A control-theoretic approach to distributed optimal configuration of 802.11 WLANs”. IEEE Transactions on Mobile Computing. Vol. 10. 2011. pp. 897-910. DOI: https://doi.org/10.1109/TMC.2010.231

C. Guzmán, M. Rivero, G. Rubino. Residual energybased transmission schemes for event reporting wireless sensor networks. Proceedings of the IEEE 11th International Symposium on Wireless Communication Systems (ISWCS). Barcelona, Spain. 2014, pp. 1-5.