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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2015

01.12.2015

GCCT: A Graph-Based Coverage and Connectivity Technique for Enhanced Quality of Service in WSN

verfasst von: Deepak S. Sakkari, Basavaraju T. G.

Erschienen in: Wireless Personal Communications | Ausgabe 3/2015

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Abstract

The area of wireless sensor network has been a constant source of attention in the research community owing to its potential remote sensing application as well as significant issues associated with almost every applications. Out of various types of issues, coverage and connectivity is one of the unsolved issues till date in wireless sensor network that potentially degrade the efficient performance capability of the applications in wireless sensor network. This paper introduces a graph based coverage and connectivity technique (GCCT) that is aimed to attain better optimization of coverage and connectivity issues in large scale wireless sensor network considering the issues of energy depletion factor. A model is developed considering the radio and energy model of hierarchical routing protocol and a new module is introduced called as super-leader node, which aims to attain energy efficiency at a same time. Simulation results shows substantial energy conservation of GCCT compared to conventional protocols.
Vorheriger Artikel Multichannel CSMA Based MAC Scheme for Unsaturated Cognitive Radio Networks: Performance Study of the Opportunity and Contention Window
Nächster Artikel A Review of Group Mobility Models for Mobile Ad Hoc Networks

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Literatur
1.
Filippini, D. (2012). Autonomous sensor networks: Collective sensing strategies for analytical purposes. Berlin: Springer Science and Business Media.
2.
Govindan, K., Chander, D., & Jagyasi, B. G. (2010). Multihop mobile wireless networks. London: River Publisher.
3.
Ammari, H. M., & Das, S. K. (2010). A study of k-coverage and measures of connectivity in 3D wireless sensor networks. IEEE Transactions on Computers, 59(2), 243–257.
4.
Khanjary, M., Sabaei, M., & Meybodi, M. R. (2014). Critical density for coverage and connectivity in two-dimensional aligned-orientation directional sensor networks using continuum percolation. IEEE Sensor Journal, 14(8), 2856–2863.
5.
Chen, C-P., Mukhopadhyay, S. C., & Chuang, C-L. (2015). Efficient coverage and connectivity preservation with load balance for wireless sensor network. IEEE Sensors Journal, 15(1).
6.
Senel, F., Akkaya, K., & Yilmaz, T. (2013). Autonomous deployment of sensors for maximized coverage and guaranteed connectivity in underwater accoustic sensor networks. IEEE Conference on Local Computer Network, pp. 211–218.
7.
Ren, X., Liang, W., & Xu, W. (2015). Quality-aware target coverage in energy harvesting sensor networks. IEEE Transactions on Emerging Topics in Computing, 3(1), 8–21.
8.
Dezun, D., Xiangke, L., Kebin, L., Yunhao, L., & Weixia, X. (2012). Distributed coverage in wireless ad hoc and sensor networks by topological graph approaches. Computers, IEEE Transactions, 61(10), 1417–1428.CrossRef
9.
Khalil, A., & Beghdad, R. (2012). Coverage and connectivity protocol for Wireless Sensor Networks. In Microelectronics (ICM), 2012 24th International Conference, pp. 1–4.
10.
Nezhad, M. M., Kherandish, M., & Mosleh, M. (2012). A novel protocol for barrier k-coverage in wireless sensor networks. Global Journal of Computer Science and Technology Network, 12(11). Version 1.0.
11.
Khan, M. A., Hasbullah, H., & Nazir, B. (2012). Dynamic load-based multi-node repositioning for mobile sensor network. In Computer & Information Science (ICCIS), International Conference, pp. 808–813.
12.
Woehrle, M., Brockhoff, D., & Hohm, T. (2012). A new model for deployment coverage and connectivity of Wireless Sensor Networks. In International Workshop on Software Engineering.
13.
Kim, Y-H., Kim, C-M., & Yang, D-S. (2012). Regular sensor deployment patterns for p-coverage and q-connectivity in wireless sensor networks. In Information Networking (ICOIN), International Conference, pp. 290–295.
14.
He, S., Chen, J., Li, X., Shen, X., & Sun, Y. (2012). Leveraging prediction to improve the coverage of wireless sensor networks. IEEE Transactions on Parallal and Distributed Systems, 23(4), 701–712.
15.
Wawryszczuk, M., & Amanowicz, M. (2012). An energy effective method for topology control in wireless sensor networks. In Microwave Radar and Wireless Communications (MIKON), 19th International Conference, pp. 647–651.
16.
Gupta, S., & Roy, K. C. (2011). Energy efficient target coverage issues in wireless sensor network. International Journal of Computer & Organization Trends, 1(3).
17.
Jedda, A., Khair, M., & Mouftah, H. T. (2012). Connected coverage for RFID and wireless sensor network. Elsevier-Procedia Computer Science, 10, 1046–1051.CrossRef
18.
Yong-hwan, K., Chan-Myung, K., Dong-Sun, Y., Young-jun, O., & Youn-Hee, H. (2012). Regular sensor deployment patterns for p-coverage and q-connectivity in wireless sensor networks. In Information Networking (ICOIN), 2012 International Conference, pp. 290–295.
19.
Erdelj, M., Loscri, V., & Natalizio, E. (2013). Multiple point of interest discovery and coverage with mobile wireless sensors. Elsevier, 11(8), 2288–2300.
20.
Balamurugan, A., & Purusothaman, T. (2012). “IPSD”new coverage preserving and connectivity maintenance scheme for improving lifetime of wireless sensor networks. WSEAS Transactions on Communications, 11, 744–761.
21.
Sakkari, D. S., & Basavaraju, T. G. (2012). Extensive study on coverage and network lifetime issues in wireless sensor networks. International Journal of Computer Applications, 52(8), 0975–8887.CrossRef
22.
Sakkari, D. S., & Basavaraju, T. G. (2013). Framework for enhanced joint connectivity and coverage using random based distribution in WSN. In Proceedings of the Fourth IEEE International Conference on Computing, Communication and Networking Technologies, July 4–6, 2013.
23.
Sakkari, D. S., & Basavaraju, T. G. (2013). Optimized coverage and connectivity for randomly deployed wireless sensor network for lifetime conservatory. International Journal of Computer Engineering & Technology, 4(6), 247–255.
24.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor network. IEEE.
25.
Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. IEEE Aerospace Conference Proceedings, Vol. 3.
26.
Manjeshwar, A., & Agrawal, D. P. (2000). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th International Parallel and Distributed Processing Symposium. pp. 2009–2015.
27.
Manjeshwar, A., & Agrawal, D. P. (2001). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless. In Proceedings International Parallel and Distributed Processing Symposium.
28.
Bansal, P., Kundu, P., & Kaur, P. (2014). Comparison of LEACH and PEGASIS hierarchical routing protocols in wireless sensor networks. International Journal of Recent Trends in Engineering & Technology, 11.
29.
Lee, Y. H., Lee, K. O., Lee, H. J., & Kusdaryono, A. CBERP: Cluster based energy efficient routing protocol for wireless sensor network. In Recent Advances In Networking, Vlsi And Signal Processing.
30.
Sangolgi, N. B., & Ahmeduddin Zakir, S. K. (2013). Energy aware data aggregation technique in WSN. International Journal of Scientific and Research Publications, 3(10).
31.
Madheswaran, M., & Shanmugasundaram, R. N. (2013). Enhancements of leach algorithm for wireless networks. The proceedings of Journal On Communication Technology, 4, 821–827.
32.
Lazakidou, A. (2010). Wireless technologies for ambient assisted living and healthcare: systems and applications: Systems and applications. IGI Global, Computers.
Metadaten
Titel
GCCT: A Graph-Based Coverage and Connectivity Technique for Enhanced Quality of Service in WSN
verfasst von
Deepak S. Sakkari
Basavaraju T. G.
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-015-2841-0

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