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Wireless Personal Communications

Erschienen in:

07.11.2017

Enhancing Coverage Using Weight Based Clustering in Wireless Sensor Networks

verfasst von: Amandeep Kaur Sohal, Ajay K. Sharma, Neetu Sood

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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Abstract

Energy conservation in wireless sensor networks (WSNs) is a fundamental issue. For certain surveillance applications in WSN, coverage lifetime is an important issue and this is related to energy consumption significantly. In order to handle these two interlinked aspects in WSN, a new scheme named Weight based Coverage Enhancing Protocol (WCEP) has been introduced. The WCEP aims to obtain longer full coverage and better network life time. The WCEP is based on assigning different weight values to certain governing parameters which are residual energy, overlapping degree, node density and degree of sensor node. These governing parameters affect the energy and coverage aspects predominantly. Further, these four different parameters are prime elements in cluster formation process and node scheduling mechanisms. The weight values help in selection of an optimal group of Cluster Heads and Cluster Members, which result in enhancement of complete coverage lifetime. The simulation results indicate that WCEP performs better in terms of energy consumption also. The enhancement of value 24% in full coverage lifetime has been obtained as compared to established existing techniques.

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Dargie, W., & Christian, P. (2010). Motivation for a network of wireless sensor nodes. In U. Xuemin & Y. Pan (Eds.), Fundamentals of wireless sensor networks theory and practice. Willey series on wireless communications and mobile computing (1st ed., pp. 17–32). New York: Willey.

Basagni, S., Naderi, M. Y., Petrioli, C., & Spenza, D. (2013). Wireless sensor networks with energy harvesting. In Mobile ad hoc networking: Cutting edge directions (2nd edn, pp. 1–36). Retrieved from http://senseslab.di.uniroma1.it/administrator/components/com_jresearch/files/publications/Wireless_Sensor_Networks_with_.pdf

Fafoutis, X., Vuckovic, D., Mauro, A. Di, Dragoni, N., & Madsen, J. (2012). Energy-harvesting wireless sensor networks. In Proceedings of the 9th European conference on wireless sensor network (EWSN) (pp. 84–85).

Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. Journal of Supercomputing, 68(1), 1–48. https://doi.org/10.1007/s11227-013-1021-9.CrossRef

Joshi, G., Nam, S., & Kim, S. (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Sensors. https://doi.org/10.3390/S130911196.

Vijay, G., Bdira, E. B. A., & Ibnkahla, M. (2011). Cognition in wireless sensor networks: A perspective. IEEE Sensors Journal, 11(3), 582–592. https://doi.org/10.1109/JSEN.2010.2052033.CrossRef

Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568. https://doi.org/10.1016/j.adhoc.2008.06.003.CrossRef

Mamalis, B., Gavalas, D., Konstantopoulos, C., & Pantziou, G. (2009). Clustering in wireless sensor networks. In Y. Zhang, L. T. Yang, & J. Chen (Eds.), RFID and sensor networks: Architectures, protocols, security, and integrations (pp. 323–354). New York: CRC Press, Taylor and Francis Group.

Wang, Y., Zhang, Y., Liu, J., & Bhandari, R. (2015). Coverage, connectivity and deployment in wireless sensor networks. In S. Patnaik, X. Li, Y. M. Yang (Eds.), Recent development in wireless sensor and adhoc networks. Signals and communication technology (pp. 25–44). New Delhi: Springer. https://doi.org/10.1007/978-81-322-2129-6_2.

10.

Soro, S., & Heinzelman, W. B. (2009). Cluster head election techniques for coverage preservation in wireless sensor networks. Ad Hoc Networks, 7(5), 955–972. https://doi.org/10.1016/j.adhoc.2008.08.006.CrossRef

11.

Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670. https://doi.org/10.1109/TWC.2002.804190.CrossRef

12.

Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad-hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 1–36. https://doi.org/10.1109/TMC.2004.41.CrossRef

13.

Saeidmanesh, M., Hajimohammadi, M., & Movaghar, A. (2009). Energy and Distance Based Clustering: An Energy Efficient Clustering Method for Wireless Sensor Networks. In World Academy of Science, Engineering and Technology, International Science Index 31, International Journal of Computer, Electrical, Automation, Control and Information Engineering (2009), 3(7), 1818-1822. http://waset.org/publications/4973.

14.

Lee, K., Lee, J., Lee, H., & Shin, Y. (2010). A density and distance based cluster head selection algorithm in sensor networks. In IEEE international conference on advanced communication technology (ICACT) (Vol. 1, pp. 162–165). IEEE.

15.

Gupta, S. K. (2012). Node degree based clustering for WSN. International Journal of Computer Applications, 40(16), 49–55.

16.

Ding, P., Holliday, J., & Celik, A. (2005). Distributed energy-efficient hierarchical clustering for wireless sensor networks. In V. K. Prasanna, S. S. Iyengar, & P. Spirakis (Eds.), IEEE international conference on distributed computing in sensor systems (DCOSS) (Vol. 356, pp. 322–339). Berlin: Springer. https://doi.org/10.1007/11502593_25.CrossRef

17.

Kim, K. T., & Youn, H. Y. (2005). PEACH: Proxy-enable adaptive clustering hierarchy for wireless sensor networks. In International conference on wireless networks, ICWN, 2005 (pp. 52–56).

18.

Soro, S., & Heinzelman, W. B. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In International parallel and distributed processing symposium, ACM (Vol. 13, pp. 236–243). 10.1109/IPDPS.2005.365

19.

Ye, M., Li, C., Chen, G., & Wu, J. (2006). An energy efficient clustering scheme in wireless sensor networks. Ad Hoc and Wireless Sensor Networks, 3, 99–119.

20.

Ming, L., Jian-Nong, C., Gui-Hai, C., Li-Jun, C., Xiao-Min, W., & Hai-Gang, G. (2007). EADEEG: An energy-aware data gathering protocol for wireless sensor networks. Distributed Sensor Networks, 18(5), 1092–1109. https://doi.org/10.1360/jos181092.

21.

Jin, Y., Wang, L., Kim, Y., & Yang, X. Z. (2008). Energy efficient non-uniform clustering division scheme in wireless sensor networks. Wireless Personal Communications, 45(1), 31–43. https://doi.org/10.1007/s11277-007-9370-4.CrossRef

22.

Jianbo, X., Yong, H., & Renfa, L. (2008). An energy-aware distributed clustering algorithm in wireless sensor networks. In IEEE international conference of computer science and software engineering (Vol. 3, pp. 528–531). IEEE. https://doi.org/10.1109/CSSE.2008.782.

23.

Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207. https://doi.org/10.1007/s11276-007-0035-8.CrossRef

24.

Zytoune, O., El Aroussi, M., & Aboutajdine, D. (2009). A uniform balancing energy routing protocol for wireless sensor networks. Wireless Personal Communications, 55(2), 147–161. https://doi.org/10.1007/s11277-009-9791-3.CrossRef

25.

Yu, J., Qi, Y., & Wang, G. (2011). An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks. Journal of Control Theory Application, 9(1), 133–139. https://doi.org/10.1007/s11768-011-0232-y.MathSciNetCrossRef

26.

Liao, Y., Qi, H., & Li, W. (2013). Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sensors, 13(5), 1498–1506. https://doi.org/10.1109/JSEN.2012.2227704.CrossRef

27.

Dohare, U., Lobiyal, D. K., & Kumar, S. (2014). Energy balanced model for lifetime maximization in randomly distributed wireless sensor networks. Wireless Personal Communications, 78(1), 407–428. https://doi.org/10.1007/s11277-014-1759-2.CrossRef

28.

Kim, H.-Y., & Kim, J. (2015). An energy-efficient balancing scheme in wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-015-3154-z.

29.

Tian, D., Avenue, K. E., & Georganas, N. D. (2002). A coverage-preserving node scheduling scheme for large wireless sensor networks. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications,WSNA '02 (pp. 32–41). Atlanta. https://doi.org/10.1145/570738.570744.

30.

Ye, F., Zhong, G., Cheng, J., Lu, S., & Zhang, L. (2003). PEAS: A robust energy conserving protocol for long-lived sensor networks. In IEEE international conference on distributed computing systems (pp. 28–37). IEEE. 10.1109/ICDCS.2003.1203449

31.

Cardei, M., & Du, D. (2005). Improving wireless sensor network lifetime through power. Wireless Networks, 11(3), 333–340.CrossRef

32.

Li, D., & Liu, H. (2009). Sensor Coverage in Wireless Sensor Networks. In Wireless Networks: Research, Technology and Applications, 3–31.

33.

Wang, B., Lim, H. B., & Ma, D. (2012). A coverage-aware clustering protocol for wireless sensor networks. Computer Networks, 56(5), 1599–1611. https://doi.org/10.1016/j.comnet.2012.01.016.CrossRef

34.

Liu, Z., Zheng, Q., Xue, L., & Guan, X. (2012). A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Generation Computer Systems, 28(5), 780–790. https://doi.org/10.1016/j.future.2011.04.019.CrossRef

35.

Tao, Y., Zhang, Y., & Ji, Y. (2013). Flow-balanced Routing for Multi-hop Clustered Wireless Sensor Networks. Ad Hoc Networks, 11(1), 541–554. https://doi.org/10.1016/j.adhoc.2012.08.001.CrossRef

36.

Torkestani, J. A. (2013). An Adaptive Energy-Efficient Area Coverage Algorithm for Wireless Sensor Networks. Ad Hoc Networks, 11(6), 1655–1666. https://doi.org/10.1016/j.adhoc.2013.03.002.CrossRef

37.

Mohamadi, H., Ismail, A. S., & Salleh, S. (2014). Solving target coverage problem using cover sets in wireless sensor networks based on learning automata. Wireless Personal Communications, 75(1), 447–463. https://doi.org/10.1007/s11277-013-1371-x.CrossRef

38.

Gu, X., Yu, J., Yu, D., Wang, G., & Lv, Y. (2014). ECDC: An energy and coverage-aware distributed clustering protocol for wireless sensor networks. Computers & Electrical Engineering, 40(2), 384–398. https://doi.org/10.1016/j.compeleceng.2013.08.003.CrossRef

39.

Mazumdar, N., & Om, H. (2015). Coverage-aware Unequal Clustering Algorithm for Wireless Sensor Networks. Procedia Computer Science, 57, 660–669. https://doi.org/10.1016/j.procs.2015.07.437.CrossRef

40.

Amgoth, T., & Jana, P. K. (2015). Energy and Coverage-Aware Routing Algorithm for Wireless Sensor Networks. Wireless Personal Communications, 81(2), 531–545. https://doi.org/10.1007/s11277-014-2143-y.CrossRef

41.

Enam, R. N., Ismat, N., & Farooq, F. (2016). Connectivity and Coverage Based Grid-Cluster Size Calculation in Wireless Sensor Networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-016-3901-9.

42.

Shokouhi, A., & Farahnaz, R. (2017). A Novel Energy-Aware Target Tracking Method by Reducing Active Nodes in Wireless Sensor Networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-017-4013-x.

43.

Handy, M. J., Haase, M., & Timmermann, D. (2002). Low Energy Adaptive Clustering Hierachy with Deterministic Cluster head Selection. In Fourth IEEE conference on mobile and wireless communications networks (pp. 368–372). Stockholm.

Titel: Enhancing Coverage Using Weight Based Clustering in Wireless Sensor Networks
verfasst von: Amandeep Kaur Sohal
Ajay K. Sharma
Neetu Sood
Publikationsdatum: 07.11.2017
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-017-5026-1

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