ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

A Genetic-Algorithm-Based Optimized Clustering for Energy-Efficient Routing in MWSN

  • Sara, Getsy S. (Department of Electronics and Communication Engineering, College of Engineering, Anna University) ;
  • Devi, S. Prasanna (Department of Computer Science, Apollo College of Engineering) ;
  • Sridharan, D. (Department of Electronics and Communication Engineering, College of Engineering, Anna University)
  • Received : 2012.04.30
  • Accepted : 2012.08.27
  • Published : 2012.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the increasing demands for mobile wireless sensor networks in recent years, designing an energy-efficient clustering and routing protocol has become very important. This paper provides an analytical model to evaluate the power consumption of a mobile sensor node. Based on this, a clustering algorithm is designed to optimize the energy efficiency during cluster head formation. A genetic algorithm technique is employed to find the near-optimal threshold for residual energy below which a node has to give up its role of being the cluster head. This clustering algorithm along with a hybrid routing concept is applied as the near-optimal energy-efficient routing technique to increase the overall efficiency of the network. Compared to the mobile low energy adaptive clustering hierarchy protocol, the simulation studies reveal that the energy-efficient routing technique produces a longer network lifetime and achieves better energy efficiency.

Keywords

References

  1. G. Anastasi et al., "Energy Conservation in Wireless Sensor Networks: A Survey," Ad Hoc Netw. vol. 7, 2009, pp. 537-568. https://doi.org/10.1016/j.adhoc.2008.06.003
  2. S.A. Munir et al., "Mobile Wireless Sensor Network: Architecture and Enabling Technologies for Ubiquitous Computing," Proc. 21st Int. Conf. Adv. Inf. Netw. Appl. Workshop (AINAW), 2007.
  3. X. Min et al., "Energy Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Networks," Int. J. Electron. Commun. (AEU), vol. 64, no. 4, 2010, pp. 289-298. https://doi.org/10.1016/j.aeue.2009.01.004
  4. S. Deng, J. Li, and L. Shen, "Mobility-Based Clustering Protocol for Wireless Sensor Networks with Mobile Nodes," IET Wireless Sensor Syst., vol. 1, no. 1, 2011, pp. 39-47. https://doi.org/10.1049/iet-wss.2010.0084
  5. A. Nayebi and H. Sarbazi-Azad, "Performance Modeling of the LEACH Protocol for Mobile Wireless Sensor Networks," J. Parallel Distr. Comput., vol. 71, no. 6, 2011, pp. 812-821. https://doi.org/10.1016/j.jpdc.2011.02.004
  6. A. Roy and S.K. Das, "QM2RP: A QOS-Based Mobile Multicast Routing Protocol Using Multi-objective Genetic Algorithm," Wireless Netw. vol. 10, 2004, pp. 271-286. https://doi.org/10.1023/B:WINE.0000023861.10684.f1
  7. C.E. Perkins., Ad Hoc Networking, Addison-Wesley Professional, 2008, pp. 225-226.
  8. M. Buttner et al., "X-MAC: A Short Preamble MAC Protocol for Duty-Cycled Wireless Sensor Networks," Technical Report CU-CS-1008-06, May 2006, pp: 1-11.
  9. J. Polastre, J. Hill, and D. Culler, "Versatile Low Power Media Access for Wireless Sensor Networks," SenSys, ACM, Nov. 2004, pp. 95-100.
  10. F. Liu, C.-Y. Tsui, and Y.J. (Angela) Zhang, "Joint Routing and Sleep Scheduling for Lifetime Maximization of Wireless Sensor Networks," IEEE Trans. Wireless Commun., vol. 9, no. 7, July 2010, pp. 2256-2267.
  11. C. Cano et al., "Analytical Model of the LPL with Wake Up After Transmissions MAC Protocol for MWSN," Proc. ISWCS, 2009, pp. 146-150.
  12. C. Bettstetter, H. Hartenstein, and X. Perez-Costa, "Stochastic Properties of the Random Waypoint Mobility Model," Wireless Netw.: Special Issue Modeling Anal. Mobile Netw., vol. 10, Kluwer Academic Publishers, 2004, pp. 555-567.
  13. E. Hyytia and J. Virtamo, "Random Waypoint Mobility Model in Cellular Networks," Springer Wireless Netw., vol. 13, 2007, pp. 177-188. https://doi.org/10.1007/s11276-006-4600-3
  14. B.A. Attea and E.A. Khalil, "A New Evolutionary Based Routing Protocol for Clustered Heterogeneous Wireless Sensor Networks," Appl. Soft Comput., doi:10,1016/j.soc.2011.04.007.
  15. K. Deb, "Optimization for Engineering Design: Algorithms and Examples," New Delhi: Prentice-Hall of India Private Limited, 2005, pp. 290-319.
  16. MATLAB: www.mathworks.com/products/matlab
  17. M.K. Marina and S. Das, "On-Demand Multipath Distance Vector Routing in Ad Hoc Networks," Proc. Int. Conf. Netw. Protocols, 2001.
  18. G.S. Sara et al., "Energy Efficient Clustering and Routing in Mobile Wireless Sensor Network Routing Protocol," Int. J. Wireless Mobile Netw. (IJWMN), vol. 2, no.4, Nov. 2010, pp: 106-114. https://doi.org/10.5121/ijwmn.2010.2409
  19. C.E. Perkins and E.M. Royer, "Ad Hoc On Demand Distance Vector Routing," Mobile Comput. Syst. Appl. (WMCSA), 1999, pp. 90-100.
  20. OMNET++ Simulator: http://www.omnetpp.org
  21. IRIS motes: http://www.memsic.com/products/wireless-sensor-networks/wireless-modules.html

Cited by

  1. Performance of vehicle speed estimation using wireless sensor networks: a region-based approach vol.71, pp.6, 2012, https://doi.org/10.1007/s11227-014-1306-7
  2. An Energy-Efficient Unequal Clustering Algorithm Using ‘Sierpinski Triangle’ for WSNs vol.88, pp.3, 2016, https://doi.org/10.1007/s11277-015-3137-0
  3. RTCO: Reliable Tracking for Continuous Objects Using Redundant Boundary Information in Wireless Sensor Networks vol.eb99, pp.7, 2016, https://doi.org/10.1587/transcom.2015ebp3431
  4. Novel Packet Switching for Green IP Networks vol.39, pp.2, 2017, https://doi.org/10.4218/etrij.17.0116.0003
  5. Q-Learning-Based Optimized Routing in Biomedical Wireless Sensor Networks vol.63, pp.1, 2017, https://doi.org/10.1080/03772063.2016.1229580
  6. Universal logic assessment for complex product design with respect to smart energy efficient manufacturing characteristics vol.232, pp.15, 2018, https://doi.org/10.1177/0954406217722379
  7. A generic algorithmic protocol approaches to improve network life time and energy efficient using combined genetic algorithm with simulated annealing in MANET vol.8, pp.1, 2012, https://doi.org/10.1108/ijius-02-2019-0011
  8. The spring load adjustment method for six-axle high-power locomotives vol.39, pp.3, 2012, https://doi.org/10.1177/1461348418817948