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Wireless Networks
Erschienen in: Wireless Networks 1/2016

01.01.2016

LEACH-MAC: a new cluster head selection algorithm for Wireless Sensor Networks

verfasst von: Payal Khurana Batra, Krishna Kant

Erschienen in: Wireless Networks | Ausgabe 1/2016

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Abstract

Battery power is a critical resource of Wireless Sensor Networks (WSNs). Therefore, an effective operation of WSNs depend upon the efficient use of its battery resource. Cluster based routing protocols are proven to be more energy efficient as compared to other routing protocols. Most of the cluster based routing protocols, especially Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, follows Dynamic, Distributed and Randomized (DDR) algorithm for clustering. Due to the randomness present in clustering algorithms, number of cluster heads generated varies highly from the optimal count. In this paper, we present an approach which attempts to control the randomness present in LEACH’s clustering algorithm. This approach makes the cluster head count stable. NS-2 simulation results show that proposed approach improved the First Node Death (FND) time and Last Node Death (LND) time by 21 and 24 % over LEACH, 10 and 20 % as compared to Advance LEACH (ALEACH) and 5 and 35 % over LEACH with Deterministic Cluster Head Selection (LEACH-DCHS) respectively.
Vorheriger Artikel Performance study of a CSMA based multiuser MAC protocol for cognitive radio networks: analysis of channel utilization and opportunity perspective
Nächster Artikel Channel selection for simultaneous move game in cognitive radio ad hoc networks

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Literatur
1.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.CrossRef
2.
Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98.CrossRef
3.
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12), 2470–2494.CrossRef
4.
Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.CrossRef
5.
Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. (2011). Body area networks: A survey. Mobile Networks and Applications, 16(2), 171–193.CrossRef
6.
Vasilakos, A. V., Zhang, Y., & Spyropoulos, T. (2011). Delay tolerant networks: Protocols and applications. Florida: CRC Press.
7.
Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.CrossRef
8.
Li, M., Li, Z., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.CrossRef
9.
He, D., Chen, C., Chan, S., Bu, J., & Vasilakos, A. V. (2012). Retrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16(4), 623–632.CrossRef
10.
Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.CrossRef
11.
Liu, L., Song, Y., Zhang, H., Ma, H., & Vasilakos, A. (2013). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.MathSciNet
12.
Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1093–1102.CrossRef
13.
Xiong, N., Cao, M., Vasilakos, A. V., Yang, L. T., & Yang, F. (2010). An energy-efficient scheme in next-generation sensor networks. International Journal of Communication Systems, 23(9–10), 1189–1200.CrossRef
14.
Yao, Y., Cao, Q., & Vasilakos, A. V. (2013). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In IEEE 10th international conference on Mobile ad-hoc and sensor systems (MASS), Hangzhou, 14–16 Oct 2013 (pp. 182–190).
15.
Yao, Y., Cao, Q., & Vasilakos, A. (2014). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking. doi:10.​1109/​TNET.​2014.​2306592.
16.
Xiang, L., Luo, J., & Vasilakos, A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON), Salt Lake City, UT, 27–30 June 2011 (pp. 46–54).
17.
Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.CrossRef
18.
Xiao, Y., Peng, M., Gibson, J., Xie, G. G., Du, D.-Z., & Vasilakos, A. V. (2012). Tight performance bounds of multihop fair access for mac protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.CrossRef
19.
Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors. doi:10.​1155/​2009/​134165.
20.
Liu, X.-Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., Vasilakos, A. V., et al. (2014). Cdc: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. doi:10.​1109/​TPDS.​2014.​2345257.
21.
Wang, X., Vasilakos, A. V., Chen, M., Liu, Y., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.CrossRef
22.
Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2012). Codepipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In Proceedings of the IEEE, INFOCOM, Orlando, FL, 25–30 Mar 2012 (pp. 100–108).
23.
Cheng, H., Xiong, N., Vasilakos, A. V., Yang, L. T., Chen, G., & Zhuang, X. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.CrossRef
24.
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.CrossRef
25.
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.CrossRef
26.
Wang, Q., Hassanein, H., & Takahara, G. (2004). Stochastic modeling of distributed, dynamic, randomized clustering protocols for wireless sensor networks. In IEEE international conference on parallel processing Workshops, ICPP 2004 Workshops, Montreal, QC, Canada, 18 Aug 2004 (pp. 456–463).
27.
Wang, Y., & Xiong, M. (2005). Monte carlo simulation of leach protocol for wireless sensor networks. In Sixth IEEE international conference on parallel and distributed computing, applications and technologies, PDCAT 2005, Dalian, China, 5–8 Dec 2005 (pp. 85–88).
28.
Loscri, V., Morabito, G., & Marano, S. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (tl-leach). IEEE Vehicular Technology Conference, 62, 1809.
29.
Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, San Francisco, CA, 30 Mar–3 Apr 2003 (Vol. 3, pp. 1713–1723).
30.
Handy, M., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th IEEE International Workshop on Mobile and Wireless Communications Network, Stockholm, Sweden, Sept 2002 (pp. 368–372).
31.
Ali, M. S., Dey, T., & Biswas, R. (2008). Aleach: Advanced leach routing protocol for wireless microsensor networks. In IEEE International Conference on Electrical and Computer Engineering, ICECE 2008, Dhaka, 20–22 Dec 2008 (pp. 909–914).
32.
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), 366–379.CrossRef
33.
Ye, M., Li, C., Chen, G., & Wu, J. (2005). Eecs: an energy efficient clustering scheme in wireless sensor networks. In 24th IEEE International Performance. Computing, and Communications Conference, IPCCC 2005, Phoenix, Arizona, 7–9 Apr 2005 (pp. 535–540).
34.
Bsoul, M., Al-Khasawneh, A., Abdallah, A. E., Abdallah, E. E., & Obeidat, I. (2013). An energy-efficient threshold-based clustering protocol for wireless sensor networks. Wireless Personal Communications, 70(1), 99–112.CrossRef
35.
Xie, D., Zhou, Q., You, X., Li, B., & Yuan, X. (2013). A novel energy-efficient cluster formation strategy: From the perspective of cluster members. IEEE Communications Letters, 17(11), 2044–2047.CrossRef
36.
Ahmad, A., Javaid, N., Khan, Z. A., Qasim, U., & Alghamdi, T. A. (2014). (ach) 2: Routing scheme to maximize lifetime and throughput of wireless sensor networks. IEEE Sensors Journal, 14(10), 3516–3532.CrossRef
37.
Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.CrossRef
38.
Thakkar, A., & Kotecha, K. (2014). Cluster head election for energy and delay constraint applications of wireless sensor network. IEEE Sensor Journal, 14(8), 2658–2664.CrossRef
39.
Wang, Q., Xu, K., Hassanein, H., & Takahara, G. (2005). Swatch: A stepwise adaptive clustering hierarchy in wireless sensor networks. In NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems, Canada (pp. 1422–1425). Waterloo, Canada: Springer.
40.
Liu, H., Li, L., & Jin, S. (2006). Cluster number variability problem in leach, In Ubiquitous Intelligence and Computing, Third International Conference, UIC 2006, China, 3–6 Sept 2006 (pp. 429–437). Wuhan, China: Springer.
41.
Wang, A., Yang, D., & Sun, D. (2012). A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks. Computers & Electrical Engineering, 38(3), 662–671.CrossRef
42.
Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. PhD thesis, Massachusetts Institute of Technology, USA, June 2000.
43.
44.
Metadaten
Titel
LEACH-MAC: a new cluster head selection algorithm for Wireless Sensor Networks
verfasst von
Payal Khurana Batra
Krishna Kant
Publikationsdatum
01.01.2016
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 1/2016
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-015-0951-y

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