CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Trends and Technologies Used for Mitigating Energy Efficiency Issues in Wireless Sensor Network

by Jyothi A.p, Usha Sakthivel
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 3
Year of Publication: 2015
Authors: Jyothi A.p, Usha Sakthivel
10.5120/19521-1150

Jyothi A.p, Usha Sakthivel . Trends and Technologies Used for Mitigating Energy Efficiency Issues in Wireless Sensor Network. International Journal of Computer Applications. 111, 3 ( February 2015), 32-40. DOI=10.5120/19521-1150

@article{ 10.5120/19521-1150,
author = { Jyothi A.p, Usha Sakthivel },
title = { Trends and Technologies Used for Mitigating Energy Efficiency Issues in Wireless Sensor Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 3 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number3/19521-1150/ },
doi = { 10.5120/19521-1150 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:56.381168+05:30
%A Jyothi A.p
%A Usha Sakthivel
%T Trends and Technologies Used for Mitigating Energy Efficiency Issues in Wireless Sensor Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 3
%P 32-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent times, various applications are conceptualized where wireless sensor network (WSN) is used either as a sub-network or as a complete domain. WSN having its unique characteristics, because of that the applicable protocols for congestion control, routing and security require distinguished mechanism as compared to other wireless networks such as WLAN, MANET, etc. One of the most irreversible resources is battery power. Since year 2000 a project called µAMS in Massachusetts Institute of technology (MIT), where Wendi Heizelman has introduced a communication protocol called Low Energy Adaptive clustered Hierarchy (LEACH). Since then, till today enormous amount of research schemes have been suggested to have different layers protocols in WSN, with optimal use of energy. This paper aims to study, investigate and analyze various contributions, limitations, technology used towards energy optimization based protocol development in WSN. The outcome of this paper will be quite valuable fro academicians, industries and researchers as a one hand tool to understand future research directions.

References
  1. R. Faludi, R. 2010. Building Wireless Sensor Networks: with ZigBee, XBee, Arduino, and Processing. O'Reilly Media, Inc. ", Computers. , 322 pages, 2010
  2. Heinzelman, W. R. , Chandrakasan, A. , and Balakrishnan, H. 2000. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. IEEE Proceedings of the Hawaii International Conference on System Sciences
  3. Gupta, A. , Nayyar, A. 2014. A Comprehensive Review of Cluster-Based Energy Efficient Routing Protocols in Wireless Sensor Networks. International Journal of Research in Computer and Communication Technology. Vol. 3, Issue. 1
  4. Mao, X. , Tang, S. , Xu, X. , Li, X-Y. , Ma, H. 2011. Energy Efficient Opportunistic Routing in Wireless Sensor Networks. IEEE Transactions On Parallel And Distributed Systems. vol. 22, Iss. 11, pp. 1934-1942
  5. Biswas, S. , and Morris, R. 2005. ExOR: Opportunistic MultiHop Routing for Wireless Networks. ACM-SIGGCOM-Proceedings of conference on Application, technologies, architectures, and protocols for computer communication, Vol. 35, Iss. 4, pp. 133-144
  6. Halkes, G. P. , and Langendoen, K. G. 2007. Crankshaft: An Energy-Efficient MAC-Protocol for Dense Wireless Sensor Networks. Springer-Verlag. pp. 228–244
  7. Francesco, M. D. , Shah, K. , Kumar, M. , and Anastasi, G. 2010. An Adaptive Strategy for Energy-Efficient Data Collection in Sparse Wireless Sensor Networks. Springer-Verlag, pp. 322–337
  8. Tang, L. , Sun, Y. , Gurewitz, O. , Johnson, D. B. 2011. EM-MAC: A Dynamic Multichannel Energy-Efficient MAC Protocol for Wireless Sensor Networks, ACM-MobiHoc
  9. Aziz, S. M. , Pham, D. M. 2013. Energy Efficient Image Transmission in Wireless Multimedia Sensor Networks. IEEE communications letters. Vol. 17, No. 6
  10. Long, J. , Dong, M. , Ota, K. , and Liu, A. 2014. Achieving Source Location Privacy and Network Lifetime Maximization Through Tree-Based Diversionary Routing in Wireless Sensor Networks. IEEE Access, Vol. 2
  11. Islam, A. B. M. A. A. , Hossain, M. S. , Raghunathan, V. , and Charlie, H. 2014. Backpacking: Energy-Efficient Deployment of Heterogeneous Radios in Multi-radio High-Data-Rate Wireless Sensor Networks. IEEE Access, Iss. 99
  12. Takaishi, D. , Nishiyama, H. , Kato, N. , and Miura, R. 2014. Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks. IEEE Transactions on Emerging Topics in Computing, Iss. 99
  13. Damaso, A. , Freitas, D. , Rosa, N. , Silva, B. , and Maciel, P. 2013. Evaluating the Power Consumption of Wireless Sensor Network Applications Using Models. Sensors, Vol. 13, pp. 3473-3500
  14. http://cpntools. org/
  15. Ashwini, K. B. , and Raju, G. T. 2014. Extending Network Lifetime by Time-Constrained Data Aggregation in Wireless Sensor Networks. Advances in Intelligent Systems and Computing, DOI: 10. 1007/978-81-322-1665-0_2
  16. Manzoor, B. , Javaid, N. , Rehman, O. , Akbar, M. , Nadeem, Q. , Iqbal, A. , Ishfaq, M. 2013. Q-LEACH: A New Routing Protocol for WSNs. Elsevier
  17. Guan, P. , and Li, X. 2007. Minimizing Distribution Cost of Distributed Neural Networks in Wireless Sensor Networks. 50th Annual IEEE Global Telecommunications Conference (GLOBECOM), Washington DC
  18. Hosseingholizadeh, A. 2009. A Neural Network approach for Wireless sensor network power management. Proc. 28th IEEE Inter. Symp. on Reliable Distributed Systems, USA
  19. Abbasi, A. A. , Kamal, A. 2011. An Intelligent Neural-Wireless Sensor Network Based Schema for Energy Resources Forecast. International Journal of Advanced Science and Technology, Vol. 33
  20. Bahanfar, S. , Kousha, H. , and Darougaran, L. 2011. Neural networks for error detection and data aggregation in wireless sensor network. International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3
  21. Zarafshan, F. , Karimi, A. , Al-Haddad, S. A. R. 2009. A Novel Fuzzy Diffusion Approach for Improving Energy Efficiency in Wireless Sensor Networks. ICEE
  22. Jiang, H. , Sun, Y. , Sun, R. , and Xu, H. 2013. Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks. Hindawi Publishing Corporation International Journal of Distributed Sensor Networks, Article ID 216561, pp. 8
  23. Hadjila, M. , Guyennet, H. , Feham, M. 2013. A Routing Algorithm based on Fuzzy Logic Approach to Prolong the Lifetime of Wireless Sensor Networks. International Journal of Open Scientific Research, Vol. 1, No. 5, 24-35
  24. Mostafa, B. , Saad, C. , and Abderrahmane, H. 2014. Fuzzy Logic Approach to Improving Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks. International Journal of Computer Science and Network Security, Vol. 14, No. 1
  25. Sonmez, C. , Incel, O. D. , Isik, S. , Donmez, M. Y. , and Ersoy, C. 2014. Fuzzy-based congestion control for wireless multimedia sensor networks. EURASIP Journal on Wireless Communications and Networking, Vol. 63
  26. Behzadi, S. , Azad, M. 2014. Fault-tolerant in wireless sensor networks using fuzzy logic. International Research Journal of Applied and Basic Sciences, Vol. 8, Iss. 9, pp. 1276-1282
  27. Fard, G. H. E. , Monsefi, R. , Akbarzadeh, M-R. , Yaghmaee, M. H. 2010. A Multi-objective Genetic Algorithm based Approach for Energy Efficient QoS-Routing in Two-tiered Wireless Sensor Networks. 5th IEEE International Symposium on Wireless Pervasive Computing, pp. 80-85
  28. Liu, J-L. , and Ravishankar, C. V. 2010. LEACH-GA: Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Networks. International Journal of Machine Learning and Computing, Vol. 1, Iss. 1, pp. 79-85
  29. Heidari, E. , and Movaghar, A. 2011. An efficient method based on genetic algorithms to solve sensor network optimization problem. International journal on applications of graph theory in wireless ad hoc networks and sensor networks. Vol. 3, No. 1
  30. Zahhad, M. A. , Ahmed, S. M. , Sabor, N. , and Sasaki, S. 2014. A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks. International Journal of Energy, Information and Communications. Vol. 5, Issue 3, pp. 47-72
  31. Mehrjoo, S. , Aghaee, H. , Karimi, H. 2011. A Novel Hybrid GA-ABC based Energy Efficient Clustering in Wireless Sensor Network. Canadian Journal on Multimedia and Wireless Networks. Vol. 2, No. 2
  32. Haapola, J. , Shelby, Z. , Raez, C. P. , and Mahonen, P. 2005. Cross-layer Energy Analysis of Multi-hop Wireless Sensor Networks. Proceedings of EWSN, pp. 33-44
  33. Suh, C. , Ko, Y-B. , and Son, D-M. 2006. An Energy Efficient Cross-Layer MAC Protocol for Wireless Sensor Networks. Springer-Verlag, pp. 410–419
  34. Hurni, P. , Braun, T. , Bhargava, B. K. , Zhang, Y. 2008. Multi-Hop Cross-Layer Design in Wireless Sensor Networks: A Case Study. IEEE International Conference on networking and communication
  35. Tsai, C-H. , Hsu, T-W. , Pan, M-S. , and Tseng, Y-C. 2009. Cross-Layer, Energy-efficient Design for Supporting Continuous Queries in Wireless Sensor Networks: A Quorum-Based Approach. Wireless personal communications, Vol. 51, Iss. 3, pp. 411-426
  36. Almiani, K. , Selvakennedy, S. , and Viglas, A. 2008. RMC: An Energy-Aware Cross-Layer Data-Gathering Protocol for Wireless Sensor Networks. 22nd IEEE International Conference on Advanced Information Networking and Applications, pp. 410-417
  37. Hamid, Z. , and Bashir, F. 2013. XL-WMSN: cross-layer quality of service protocol for wireless multimedia sensor networks. EURASIP Journal onWireless Communications and Networking
  38. Espes, D. , Lagrange, X. , Suarez, L. 2014. A cross-layer MAC and routing protocol based on slotted Aloha for Wireless Sensor Networks. Springer annals of telecommunications. pp. ISSN 0003-4347
  39. Truong, C. D. , Khan, M. A. , Sivrikaya, F. 2010. Cooperative Game Theoretic Approach to Energy-Efficient Coverage in Wireless Sensor Networks. IEEE International Conference on Networked Sensing System, pp. 73-76
  40. Koltsidas, G. , and Pavlidou, F-N. 2011. A Game Theoretical Approach to Clustering of Ad-Hoc and Sensor Networks", Telecommunication Systems, Vol. 47, No 1-2, pp. 81-93, DOI: 10. 1007/s11235-010-9303-5
  41. Xu, Z. , Yin, Y. , Chen, X. , and Wang, J. 2013. A Game-theory Based Clustering Approach for Wireless Sensor Networks. NGCIT-ASTL, Vol. 27, pp. 58 - 66
  42. Asadi, M. , Zimmerman, C. , and Agah, A. 2013. A Game-theoretic Approach to Security and Power Conservation in Wireless Sensor Networks. International Journal of Network Security. Vol. 15, No. 1, pp. 50-58
  43. Yang, G. , and Guan, X. 2014. A Non-cooperative Game Theoretic Approach to Energy-efficient Power Control in Wireless Sensor Networks. International Journal of Future Generation Communication and Networking. Vol. 7, No. 1, pp. 169-180
  44. Rahman, M. N. , Matin, M. A. 2011. Efficient Algorithm for Prolonging Network Lifetime of Wireless Sensor Networks. IEEE- Tsinghua Science And Technology, Vol. 16, No. 6, pp. 561-568
  45. Loscrí, V. , Natalizio, E. , Guerriero, F. , Aloi, G. 2012. Particle Swarm Optimization Schemes Based on Consensus for Wireless Sensor Networks. ACM
  46. Poostfroushan, S. , Sarram, M. A. , and Sheikhpour, R. 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
  47. Zhong, J-H. , and Zhang, J. 2012. Ant Colony Optimization Algorithm for Lifetime Maximization in Wireless Sensor Network with Mobile Sink. ACM
  48. Ping, W. J. , Yan, L. J. 2013. Wireless Sensor Network Mobile Agent routing based on the Improved Ant Colony Algorithm. Journal of Convergence Information Technology. Vol. 8, No. 5
Index Terms

Computer Science
Information Sciences

Keywords

Battery Energy Efficiency LEACH Network Lifetime Wireless Sensor Network

Powered by PhDFocusTM