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An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network

by Hemawathi.p, T G Basavaraju
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 7
Year of Publication: 2014
Authors: Hemawathi.p, T G Basavaraju
10.5120/17698-8673

Hemawathi.p, T G Basavaraju . An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network. International Journal of Computer Applications. 101, 7 ( September 2014), 14-19. DOI=10.5120/17698-8673

@article{ 10.5120/17698-8673,
author = { Hemawathi.p, T G Basavaraju },
title = { An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 7 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number7/17698-8673/ },
doi = { 10.5120/17698-8673 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:02.535924+05:30
%A Hemawathi.p
%A T G Basavaraju
%T An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 7
%P 14-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the advent of wireless networking, wireless sensor network (WSN) has been a constant target of research due to its potential data aggregation techniques in hostile environment. Even after crossing more than a decade, wireless sensor network is still more under research and development and less on commercial deployment when it comes to large scale wireless environment. Although, there are various issues exists in WSN that ranges from quality of service to security policies, it was frequently seen that root cause of majority of the issues originates from the energy that backs up the sensor motes to transmit the data to base station. The past research work has witness massive volumes of algorithms using various sophisticated technologies in order to mitigate the issues of energy problems in sensor motes, however, till date none of the prior studies has yet been standardized and hence the issues of unwanted power depletion still persist because of numerous unsolved factors. This paper is an attempt to study only the prominent techniques that has been introduced in the past for energy efficiency exclusively for hierarchical routing protocols. A brief review of some prior Swarm Intelligence (SI) techniques is also given a special focus for the similar purpose in this paper.

References
  1. Dargie, W, Poellabauer, C. 2010. Fundamentals of Wireless Sensor Networks: Theory and Practice, John Wiley & Sons, Technology & Engineering - 336 pages
  2. Ma, C. 2007. Battery-aware and Energy-efficient Algorithms for Wireless Networks, Doctorial Thesis of Stony Brooks University
  3. Raghavendra, C, Krishna, K, Znati, T. 2004. Wireless Sensor Networks, Springer-Verlag.
  4. Akyildiz, IF, Su, W. , Sankarasubramaniam, Y. , Cayirci, E. 2002. A survey on sensor networks, IEEE Communications Magazine, vol. 40 (8), pp. 102–114
  5. Kephart, J. , Chess, D. 2003. The vision of autonomic computing, IEEE Computer Magazine, vol. 36 (1), pp. 41–50.
  6. Zheng, J. , Jamalipour, A . 2009. Wireless Sensor Networks: A Networking Perspective. , a book published by A John & Sons, Inc, and IEEE
  7. Heinzelman, W. R. , Chandrakasan, A. , Balakrishnan, H. 2000. Energy- efficient Communication Protocol for Wireless Microsensor Networks, in IEEE Computer Society Proceedings of the Thirty Third Hawaii International Conference on System Sciences (HICSS '00), Washington, DC, USA, 2000, vol. 8, pp. 8020
  8. Younis, O. , Fahmy, S. 2004. Heed: A Hybrid, energy, Distributred Clustering Approach for Ad-hoc Networks:, IEEE Transactions on Mobile Computing Issues in Wireless Networks and Mobile Computing,vol 3,no. 4, pp. 366-369
  9. Younis, O. , Fahmy, S. 2002. Distributed Clustering in Ad-hoc sensor Networks:A Hybrid Energy - efficient Approach, International Journal of Computer Science.
  10. Lindsey, S. , Raghavendra, C. S. 2002. PEGASIS: Power-efficient Gathering in Sensor Information System", Proceedings IEEE Aerospace Conference, Big Sky, MT, vol. 3, pp. 1125-1130.
  11. Manjeshwar, A. , Agarwal, D. P. 2001. TEEN : A Protocol for Enhanced Efficiency in Wireless Sensor Networks, in the Proceedings of the 1 st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA
  12. Manjeshwar, A. , Agarwal, D. P. 2001. APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless in Wireless Sensor Networks,in the Proceedings of the 2nd International Workshop of Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco CA
  13. Yoon S. , Shahabi C. 2005. Exploiting Spatial Correlation Towards an Energy Efficient Clustered Aggregation Techique (CAG), IEEE Conference on Communications
  14. Xu, Z. , Yin, Y. , Wang, J. , Kim, J-Uk. 2014. A Density-based Energy-efficient Clustering Heterogeneous Algorithm for Wireless Sensor Networks, International Journal of Control and Automation, vol. 7, no. 2, pp. 175-188
  15. Lee, S-K. , Koh, J-G. , Jung, C-R. 2014. An Energy-Efficient QoS-aware Routing Algorithm for Wireless Multimedia Sensor Networks, International Journal of Multimedia and Ubiquitous Engineering, vol. 9, No. 2 (2014), pp. 245-252
  16. Zytoune, O. , Aboutajdine, D. 2014. A Low Energy Time Based Clustering Technique for Routing in Wireless Sensor Networks, American Journal of Sensor Technology, vol. 2, no. 1, pp. 1-6
  17. Poostfroushan, S. , Sarram, M. A. , Sheikhpour, Razieh. 2014. Energy Efficient Backbone Formation Using Particle Swarm Optimization Algorithm in Wireless Sensor Networks, International Journal of Grid and Distributed Computing, vol. 7, no. 1, pp. 123-134
  18. Haider, A. , Sandhu, M. M. , Amjad, N. 2014. REECH-ME: Regional Energy Efficient Cluster Heads based on Maximum Energy Routing Protocol with Sink Mobility in WSNs, Journal of Basic and Applied Scientific Research, vol. 4(1),pp. 200-216
  19. Zhang, C. , Liu, Fangai. , WU, Nan. 2014. A Distributed Energy-e_cient Unequal Clustering Routing Protocol for Wireless Sensor Networks, Journal of Computational Information Systems, vol. 10: 6, pp. 2369-2376
  20. Shu, W. , Wang,J. 2013. An Optimized Multi-hop Routing Algorithm Based on Clonal Selection Strategy for Energy-efficient Management in Wireless Sensor Networks, Sensors & Transducers, vol. 22, pp. 8-14
  21. Deng, H. , Yang,C. , Sun,Y. 2013. A Novel Algorithm for Optimized Cluster Head Selection, Science Journal of Electrical & Electronic Engineering
  22. Moh'd A- O. , Al-A ,Alaa. 2013. Extending Wireless Sensor Network Lifetime by Relocating of Base Station using Harmony Search Algorithm, Wireless Sensors and Cellular Systems
  23. John,J. T. , Ramson, S. R. J. 2013. Energy-Aware Duty Cycle Scheduling for Efficient Data Collection in Wireless Sensor Networks, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2, Issue. 2
  24. Zhao, J. , Y. Lirong. 2014. LEACH-A: An Adaptive Method for Improving LEACH Protocol, Sensors & Transducers, vol. 162 , Issue. 1, pp. 136-140
  25. Kim, J-Y. , Sharma, T. , Kumar, B. , Tomar, G. S. , Berry, K. , Lee, W-H. 2014. IC-ACO: Inter cluster Ant colony Optimization Algorithm for Wireless Sensor Network in Dense Environment, International Journal of Distributed Sensor Network
  26. Zungeru, A. M. , Yahaya, E. A. 2013. Caroline Omoanatse Alenoghena3, Performance Evaluation of Energy-aware Swarm Intelligence Based Routing Protocols for Wireless Sensor Networks Based on Different Radio Models, International Journal of Computing, Communications and Networking, Vol. 2,no. 4
  27. Kaur,R. , Sharma, D. , Kaur, N. 2013. Comparative Analysis Of Leach And Its Descendant Protocols In Wireless Sensor Network, International Journal of P2P Network Trends and Technology, vol. 3,Issue-1
  28. Kour, H. , Sharma, A. K. 2010. Hybrid Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network, International Journal of Computer Applications (0975 – 8887), vol. 4, no. 6
  29. Yoon, S. , Shahabi, C. 2007. The Clustered AGgregation (CAG) TechniqueLeveraging Spatial and Temporal Correlations in Wireless Sensor Networks, ACM Transactions on Sensor Networks, vol. 3, no. 1
  30. Bonabeau, E. , Dorigo, M. , Theraulaz, G. 1999. Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York, USA.
  31. Engelbrecht, A. 2007. Computational Intelligence: An Introduction, second ed. , Wiley
  32. Kennedy, J. , Eberhart, R. C. , Shi, Y. 2001. Swarm Intelligence, Morgan Kaufman, San Francisco, USA
  33. Dorigo, M. , Stützle, T. 2004. (Eds. ), Ant Colony Optimization, MIT press
  34. Dorigo, M. , G. A. Di. 1999. The ant colony optimization metaheuristic, in: D. Corne, M. Dorigo (Eds. ), New Ideas in Optimization, McGraw-Hill, pp. 11–32
  35. Bashyal, S. , Kumar, G. , Venayagamoorthy. 2007. Collaborative routing algorithm for wireless sensor network longevity, in: Proceedings of the IEEE, International Conference on Intelligent Sensors, Sensor Networks and Information
  36. Kennedy, J. , Eberhart, R. C. , Shi, Y. 2001. Swarm Intelligence, Morgan Kaufman, San Francisco, USA
  37. Schoonderwoerd, R. , Holland, O. E. , Bruten, J. L. , Rothkrantz, L. J. M. 1996. Ant-based load balancing in telecommunications networks, Adaptive Behavior, vol. 5(2), pp. 169–207
  38. Caro, G. A. D. 2004. Ant Colony Optimization and Its Application to Adaptive Routing in Telecommunication Networks, Ph. D. Thesis, Faculté des Sciences, Appliquées, Université Libre de Bruxelles (ULB), Brussels, Belgium
  39. Caro, G. A. D. , Dorigo, M. 1998. AntNet: distributed stigmergetic control for communication networks, Journal of Artificial Intelligence Research (JAIR), vol. 9,pp. 317–365.
  40. Farooq, M. , Caro, G. A. D. 2008. Routing protocols inspired by insect societies, in: C. Blum, D. Merkle (Eds. ), Swarm Intelligence, Introduction and Applications, Natural Computing Series, Springer-Verlag, pp. 101–160
  41. Wedde, H. F. , Farooq, M. 2006. A comprehensive survey of nature-inspired routing protocols for telecommunication networks, Journal of System Architecture,vol. 52 (8),pp. 461–484.
  42. Sim, K. M. , Sun, W. H. 2003. Ant colony optimization for routing and load balancing: survey and new directions, IEEE Transactions on System, Man and Cybernetics, vol. 33 (5), pp. 560–572.
  43. Ren, H. , Meng, M. Q. -H. 2006. Biologically inspired approaches for wireless sensor networks, in: Proceedings of IEEE the International Conference on Mechatronics and Automation
  44. Iyengar, S. S. , Wu, H. C. , Balakrishnan, N. , Chang, S. Y. 2007. Biologically inspired cooperative routing for wireless mobile sensor networks, IEEE Systems Journal, vol. 1 (1), pp. 29–37
  45. Ducatelle, F. , Caro, G. A. D. , Gambardella, L. 2010. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intelligence, in press. doi:10. 1007/s11721-010-0040-x.
  46. Sausen, P. S. , Spohn, M. A. , Perkusich, A. 2010. Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones, Information Sciences, vol. 180 (5) pp. 653–663.
  47. Marcelloni, F. , Vecchio, M. 2010. Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization, Information Sciences, vol. 180 (10),pp. 1924–1941
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Network Hierarchical Routing Protocol LEACH battery power depletion Swarm Intelligence

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