Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network

Coverage and energy efficiency metrics are two fundamental issues for almost all types of application in wireless sensor network (WSNs).Coverage reflects how well an area is monitored by sensor nodes and in energy efficient networks where less energy is consumed to provide the same level of services...

Full description

Saved in:

Bibliographic Details
Main Author:	Halim, Nurul Hamimi
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/23296/1/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf http://eprints.utem.edu.my/id/eprint/23296/2/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.23296
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Halim, Nurul Hamimi Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
description	Coverage and energy efficiency metrics are two fundamental issues for almost all types of application in wireless sensor network (WSNs).Coverage reflects how well an area is monitored by sensor nodes and in energy efficient networks where less energy is consumed to provide the same level of services.These twin specifics are presented to evaluate the performance of a wireless sensor network.Due to its simplicity and ease of analysis,full coverage is widely implemented in many theoretical studies.However,sometimes full coverage is not the best way to represent some real-world application due to its strong restrictions and its deterministic characteristics.In this thesis,Modified Harmony Search algorithm (MHS) is proposed to achieve a sensor node deployment such that the covered area is optimal and data transfer has low energy consumption.Through computer simulations, experimental results verified that the proposed method improved the coverage of area in compare to some related methods.Based on the result obtain from every experiments,coverage area percentage performance is affected by the number of hotspots.This is shown by Harmony Search (HS) based method where the coverage area percentage increases as the number of hotspot increase. However,the sink node position and size of data transmitted will not affect the performance of coverage area.This is because the coverage area value is fluctuated as the parameters value increases.Throughout the experiment conducted,sensor nodes deployed using Modified Harmony Search algorithm (MHS) gives better coverage area compared to other existing methods.The average coverage area percentage obtained by Modified Harmony Search is 63 %.The average coverage area percentage obtained by Modified Random is 48 % and the average coverage area percentage obtained by Harmony Search is 46 %.The highest coverage area recorded for Modified Harmony Search is 70 %.To enhance the energy efficiency,shortest path distance finder is added to each method.Throughout the research,Modified Harmony Search with shortest path distance finder gives optimum results.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Halim, Nurul Hamimi
author_facet	Halim, Nurul Hamimi
author_sort	Halim, Nurul Hamimi
title	Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
title_short	Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
title_full	Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
title_fullStr	Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
title_full_unstemmed	Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network
title_sort	optimization method using modified harmony search for coverage and energy efficiency in wireless sensor network
granting_institution	UTeM
granting_department	Faculty Of Electronic And Computer Engineering
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23296/1/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf http://eprints.utem.edu.my/id/eprint/23296/2/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf
_version_	1747834029991788544
spelling	my-utem-ep.232962022-03-15T09:28:15Z Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network 2018 Halim, Nurul Hamimi T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Coverage and energy efficiency metrics are two fundamental issues for almost all types of application in wireless sensor network (WSNs).Coverage reflects how well an area is monitored by sensor nodes and in energy efficient networks where less energy is consumed to provide the same level of services.These twin specifics are presented to evaluate the performance of a wireless sensor network.Due to its simplicity and ease of analysis,full coverage is widely implemented in many theoretical studies.However,sometimes full coverage is not the best way to represent some real-world application due to its strong restrictions and its deterministic characteristics.In this thesis,Modified Harmony Search algorithm (MHS) is proposed to achieve a sensor node deployment such that the covered area is optimal and data transfer has low energy consumption.Through computer simulations, experimental results verified that the proposed method improved the coverage of area in compare to some related methods.Based on the result obtain from every experiments,coverage area percentage performance is affected by the number of hotspots.This is shown by Harmony Search (HS) based method where the coverage area percentage increases as the number of hotspot increase. However,the sink node position and size of data transmitted will not affect the performance of coverage area.This is because the coverage area value is fluctuated as the parameters value increases.Throughout the experiment conducted,sensor nodes deployed using Modified Harmony Search algorithm (MHS) gives better coverage area compared to other existing methods.The average coverage area percentage obtained by Modified Harmony Search is 63 %.The average coverage area percentage obtained by Modified Random is 48 % and the average coverage area percentage obtained by Harmony Search is 46 %.The highest coverage area recorded for Modified Harmony Search is 70 %.To enhance the energy efficiency,shortest path distance finder is added to each method.Throughout the research,Modified Harmony Search with shortest path distance finder gives optimum results. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23296/ http://eprints.utem.edu.my/id/eprint/23296/1/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf text en public http://eprints.utem.edu.my/id/eprint/23296/2/Optimization%20Method%20Using%20Modified%20Harmony%20Search%20For%20Coverage%20And%20Energy%20Efficiency%20In%20Wireless%20Sensor%20Network.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112723 mphil masters UTeM Faculty Of Electronic And Computer Engineering 1. A.Razaque, K. M. E., 2014. ‘Energy-Efficient Boarder Node Medium Access Control Protocol for Wireless Sensor Networks’, Sensors (Basel, Switzerland), 14(3), pp. 5074– 5117. 2. A.Patel, 2006. Amit’s Thoughts on Grids. Available at: http://www-csstudents.stanford.edu/~amitp/game-programming/grids/. 3. Abdelaziz, A.Y., Osama, R.A., Elkhodary, S.M. and El-Saadany, E.F., 2012, July. Reconfiguration of distribution systems with distributed generators using ant colony optimization and harmony search algorithms. In Power and Energy Society General Meeting, 2012 IEEE (pp. 1-8). IEEE. 4. Aghaie, N. and Tinati, M. A., 2016. ‘Localization of WSN nodes based on NLOS identification using AOAs statistical information’, 2016 24th Iranian Conference on Electrical Engineering, ICEE 2016, pp. 496–501. doi: 10.1109/IranianCEE.2016.7585572. 5. Ahmad, I., Mohammad, M.G., Salman, A.A. and Hamdan, S.A., 2012. Broadcast scheduling in packet radio networks using Harmony Search algorithm. Expert Systems with Applications, 39(1), pp.1526-1535. 6. Ahmed, Ahmad Jamal, J. A., 2016. ‘Application Using AHAAR Wavelet Transform and’, in 2016 6th IEEE International Conference on Control System, Computing and Engineering, pp. 25–27.127 7. Ahriman, N., Yulianto, A. and Kusriyanto, M., 2015, November. Wireless sensor network application for carbon monoxide monitoring. In Telecommunication Systems Services and Applications (TSSA), 2015 9th International Conference on (pp. 1-4). IEEE. 8. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E., 2002. A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–105. https://doi.org/10.1109/MCOM.2002.1024422’, IEEE Communications Magazine, 40(8), pp. 102–105. doi: 10.1109/MCOM.2002.1024422. 9. Alkhathami, M., Alazzawi, L. and Elkateeb, A., 2017, January. Large scale border security systems modeling and simulation with OPNET. In Computing and Communication Workshop and Conference (CCWC), 2017 IEEE 7th Annual (pp. 1-8). IEEE. 10. Alsaidi, A.S., Wan, T.C. and Munther, A., 2015. Application of Harmony Search Optimization Algorithm to Improve Connectivity in Wireless Sensor Network with Nonuniform Density. J. Inf. Sci. Eng., 31(4), pp.1475-1489. 11. Ameen, A.M., Pasupuleti, J., Khatib, T., Elmenreich, W. and Kazem, H.A., 2015. Modeling and characterization of a photovoltaic array based on actual performance using cascade-forward back propagation artificial neural network. Journal of Solar Energy Engineering, 137(4), p.041010. 12. Ammari, H. M., 2010. ‘Coverage in Wireless Sensor Networks: A Survey’, Network Protocols and Algorithms, 2(2). doi: 10.5296/npa.v2i2.276.128 13. Anastasi, G., Conti, M., Di Francesco, M. and Passarella, A., 2009. Energy conservation in wireless sensor networks: A survey. Ad hoc networks, 7(3), pp.537-568. 14. Aziz, N.A.A., Aziz, K.A. and Ismail, W.Z.W., 2009. Coverage strategies for wireless sensor networks. World academy of science, Engineering and technology, 50, pp.145-150. 15. Badi, A., Mahgoub, I. and Ilyas, M., 2009, December. Using individualized link power settings for energy optimization in hierarchical wireless sensor networks (wsns). In HighCapacity Optical Networks and Enabling Technologies (HONET), 2009 6th International Symposium on (pp. 172-178). IEEE. 16. Baidya, S.S. and Bhattacharyya, C.K., 2012, December. Coverage and connectivity in wireless sensor networks: Their trade-offs. In Sensing technology (ICST), 2012 sixth international conference on (pp. 353-358). IEEE. 17. Barboni, L. and Valle, M., 2014. Experimental Assessment of the Battery Lifetime in WSN Based on the Duty-Cycle Current Average Method. Wireless Sensor Network, 6(10), p.212. 18. Basagni, S., 2013. ‘Wireless sensor networks with energy harvesting’, Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition., pp. 1–36. Available at: http://senseslab.di.uniroma1.it/administrator/components/com_jresearch/files/publications/ Wireless_Sensor_Networks_with_.pdf.129 19. Buratti, C., Conti, A., Dardari, D. and Verdone, R., 2009. An overview on wireless sensor networks technology and evolution. Sensors, 9(9), pp.6869-6896. 20. Burger, J. T., no date. A Basic Introduction To Neural Networks, University of WinconsinMadison. Available at: http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html (Accessed: 23 August 2017). 21. Chang-ri, L., Yun, Z., Xin-Hua, Z. and Zi-bo, Z., 2010, March. A clustering algorithm based on cell combination for wireless sensor networks. In Education Technology and Computer Science (ETCS), 2010 Second International Workshop on (Vol. 2, pp. 74-77). IEEE. 22. Da Cunha, A.B., De Almeida, B.R. and Da SilvaJr, D.C., 2009. Remaining capacity measurement and analysis of alkaline batteries for wireless sensor nodes. IEEE Transactions on Instrumentation and Measurement, 58(6), pp.1816-1822. 23. Dai, T. and Qiao, W., 2013. Trading wind power in a competitive electricity market using stochastic programing and game theory. IEEE Transactions on Sustainable Energy, 4(3), pp.805-815. 24. Dextreit, C. and Kolmanovsky, I.V., 2014. Game theory controller for hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 22(2), pp.652-663. 25. Dittrich, T., Menachem, C., Herzel, Y. and Lou, A., 2016. ‘Lithium Batteries for Wireless Sensor Network’. Technical report.130 ECE, S.F., 2013. A Survey on Energy Efficient Routing in Wireless Sensor Networks. International Journal, 3(7). 26. Fan, Z. and Zhao, W., 2011. Network coverage optimization strategy in wireless sensor networks based on particle swarm optimization. 27. Felemban, E., 2013. Advanced border intrusion detection and surveillance using wireless sensor network technology. International Journal of Communications, Network and System Sciences, 6(05), p.251. 28. G.Thirumalaimuthu, Lawrence, E. E. and S.Meenakshi, 2016. ‘Security in wireless sensor networks: issues and challenges’, International Journal of Computer Application, 6(2), p. 6 pp.-pp.1048. Available at: http://ieeexplore.ieee.org/document/1625756/. 29. Guo, H., 2012, June. Investigation on ant-colony based routing algorithm for wireless sensor networks. In World Automation Congress (WAC), 2012 (pp. 227-229). IEEE. 30. Gupta, S. and Roy, K.C., 2013. Comparison of different energy minimization techniques in wireless sensor network. International Journal of Computer Applications, 75(18). 31. Hammoudeh, M., Al-Fayez, F., Lloyd, H., Newman, R., Adebisi, B., Bounceur, A. and Abuarqoub, A., 2017. A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols. IEEE Sensors Journal, 17(8), pp.2572-2582131 32. Hoang, D.C., Yadav, P., Kumar, R. and Panda, S.K., 2010, May. A robust harmony search algorithm based clustering protocol for wireless sensor networks. In Communications Workshops (ICC), 2010 IEEE International Conference on (pp. 1-5). IEEE. 33. Huang, C.F. and Tseng, Y.C., 2005. The coverage problem in a wireless sensor network. Mobile Networks and Applications, 10(4), pp.519-528. 34. Huzefa Ali, D.K.Mishra, A. S., 2016. What is the difference between wireless sensor networks and ad hoc?, Quora. Available at: https://www.quora.com/What-is-thedifference-between-wireless-sensor-networks-and-ad-hoc (Accessed: 1 January 2016). 35. Jiaying, D., Tianyun, S., Xiaojun, L. and Zhi, L., 2016, May. Optimal node deployment scheme for WSN-based railway environment monitoring system. In Control and Decision Conference (CCDC), 2016 Chinese (pp. 6529-6534). IEEE. 36. Katsuma, R., Murata, Y., Shibata, N., Yasumoto, K. and Ito, M., 2009, October. Extending k-coverage lifetime of wireless sensor networks using mobile sensor nodes. In Wireless and Mobile Computing, Networking and Communications, 2009. WIMOB 2009. IEEE International Conference on (pp. 48-54). IEEE. 37. Kim, Y., Yeo, M., Kim, D. and Chung, K., 2012. A node deployment strategy considering environmental factors and the number of nodes in surveillance and reconnaissance sensor networks. International Journal of Distributed Sensor Networks, 8(8), p.385282.132 38. Koubâa, A., Alves, M. and Tovar, E., 2005. ‘IEEE 802.15.4 for Wireless Sensor Networks: A Technical Overview’, CISTER-Research Centre in Realtime and Embedded Computing Systems. 39. Kristoffer Bonheur, 2016. ‘Lithium ion battery_ Advantages and disadvantages _ Version Daily’. AUstralia: Version Daily. Available at: http://www.versiondaily.com/lithium-ionbattery-advantages-disadvantages/. 40. Krogh, A., 2008. What are artificial neural networks?, Nat Biotechnol. doi: nbt1386 [pii]\n10.1038/nbt1386. 41. Kumari, P. and Singh, Y., 2012, January. Delaunay triangulation coverage strategy for wireless sensor networks. In Computer Communication and Informatics (ICCCI), 2012 International Conference on (pp. 1-5). IEEE. 42. Laturkar, A. P. and Malathi, P.,2015. ‘Coverage Optimization Techniques in WSN using PSO: A Survey’, International Journal of Computer Applications, 1(c), pp. 975–8887. 43. Leghari, M., Abbasi, S. and Dhomeja, L.D., 2010. Survey on packet size optimization techniques in wireless sensor networks. In Proceedings of the International Conference on Wireless Sensor Networks for Developing Countries (WSN4DC). 44. Liu, C., Neale, Z. G. and Cao, G., 2016. ‘Understanding electrochemical potentials of cathode materials in rechargeable batteries’, Materials Today. Elsevier Ltd., 19(2), pp. 109–123.133 45. Liu, S.H., Zeng, W., Lou, Y. and Zhai, J., 2015, October. A Reliable Multi-path Routing Approach for Medical Wireless Sensor Networks. In Identification, Information, and Knowledge in the Internet of Things (IIKI), 2015 International Conference on (pp. 126- 129). IEEE. 46. Magaia, N., Horta, N., Neves, R., Pereira, P.R. and Correia, M., 2015. A multi-objective routing algorithm for Wireless Multimedia Sensor Networks. Applied Soft Computing, 30, pp.104-112. 47. Mahamuni, C.V., 2016, December. A military surveillance system based on wireless sensor networks with extended coverage life. In Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016 International Conference on (pp. 375-381). IEEE. 48. Manjarres, D., Del Ser, J., Gil-Lopez, S., Vecchio, M., Landa-Torres, I., Salcedo-Sanz, S. and Lopez-Valcarce, R., 2013. On the design of a novel two-objective harmony search approach for distance-and connectivity-based localization in wireless sensor networks. Engineering Applications of Artificial Intelligence, 26(2), pp.669-676. 49. Messenger, R. and Dove, R., 2012, October. Basic genetic algorithm pattern for use in selforganizing agile security. In Security Technology (ICCST), 2012 IEEE International Carnahan Conference on (pp. 305-311). IEEE.134 50. Miller, N., Manz, D., Roedel, J., Marken, P. and Kronbeck, E., 2010, July. Utility scale battery energy storage systems. In Power and Energy Society General Meeting, 2010 IEEE (pp. 1-7). IEEE. 51. Muraki, S., Sakito, Y. and Sakamoto, H., Primearth Ev Energy Co., Ltd., 2015. Nickelmetal hydride battery. U.S. Patent Application 14/836,932. 52. Nezhad, S.E., Kamali, H.J. and Moghaddam, M.E., 2010, November. Solving K-coverage problem in wireless sensor networks using improved harmony search. In Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on (pp. 49-55). IEEE. 53. Noguchi, S., Miyamoto, T. and Matsutomo, S., 2014. Meaning of the Rational Solution Obtained by Game Theory in a Multi-Objective Electromagnetic Apparatus Design Problem. IEEE Transactions on Magnetics, 50(2), pp.649-652. 54. Ongaro, F., Saggini, S. and Mattavelli, P., 2012. Li-ion battery-supercapacitor hybrid storage system for a long lifetime, photovoltaic-based wireless sensor network. IEEE Transactions on Power Electronics, 27(9), pp.3944-3952. 55. Othman, S.B., Trad, A. and Youssef, H., 2014, August. Security architecture for at-home medical care using Wireless Sensor Network. In Wireless Communications and Mobile Computing Conference (IWCMC), 2014 International (pp. 304-309). IEEE.135 56. Pandey, A. and Tripathi, R. C., 2010 ‘A Survey on Wireless Sensor Networks Security’, International Journal of Computer Applications, 3(2), pp. 43–49. 57. Panov, S. and Koceski, S., 2014, June. Area coverage in wireless sensor network by using harmony search algorithm. In Embedded Computing (MECO), 2014 3rd Mediterranean Conference on (pp. 210-213). IEEE. 58. Pardeep Kumar, Sang-Gon Lee, H.-J. L., 2012 ‘E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks’, Sensors (Basel, Switzerland), 12(2), pp. 1625–1647. Available at: http://www.mdpi.com/1424- 8220/12/2/1625/htm. 59. Payal, A., Rai, C.S. and Reddy, B.R., 2015. Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks. Wireless Personal Communications, 82(4), pp.2519-2536. 60. Payal, A., Rai, C.S. and Reddy, B.V.R., 2014, September. Artificial neural networks for developing localization framework in wireless sensor networks. In Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on (pp. 1-6). IEEE. 61. Peter Mills, 2016. ‘What is an acceptable percent error during scientific research?’ Available at https://www.quora.com/What-is-an-acceptable-percent-error-duringscientific-research136 62. Pieri, T. and Michaelides, M.P., 2016, April. Air pollution monitoring in lemesos using a wireless sensor network. In Electrotechnical Conference (MELECON), 2016 18th Mediterranean (pp. 1-6). IEEE. 63. Rakočević, G., 2009. Overview of Sensors for Wireless Sensor Networks. The IPSI BgD Transactions on Internet Research, p.13. 64. Ravindran, A. and Manikandan, M., 2013, December. A modest analysis of energy aware routing mechanisms for wireless sensor networks. In Advanced Computing (ICoAC), 2013 Fifth International Conference on (pp. 114-118). IEEE. 65. Rebai, M., Snoussi, H., Hnaien, F. and Khoukhi, L., 2015. Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Computers & Operations Research, 59, pp.11-21. 66. Sahin, D., Gungor, V.C., Kocak, T. and Tuna, G., 2014. Quality-of-service differentiation in single-path and multi-path routing for wireless sensor network-based smart grid applications. Ad Hoc Networks, 22, pp.43-60. 67. Sarwesh, P., Shet, N.S.V. and Chandrasekaran, K., 2017. Energy-Efficient Network Architecture for IoT Applications. In Beyond the Internet of Things (pp. 119-144). Springer International Publishing. 68. Sengupta, S., Das, S., Nasir, M.D. and Panigrahi, B.K., 2013. Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy137 consumption, and connectivity. Engineering Applications of Artificial Intelligence, 26(1), pp.405-416. 69. Sevgi, L. and Orbay, B. Z., 2014. ‘Game theory and engineering applications [testing ourselves]’, IEEE Antennas and Propagation Magazine, 56(3), pp. 255–267. 70. Sha, Z., Kannan, K.N. and Panchal, J.H., 2015. Behavioral experimentation and game theory in engineering systems design. Journal of Mechanical Design, 137(5), p.051405. 71. Shahrudin, H. S., 2016. ‘Malaysia’s crime index sees 4.6 per cent increase’, New Straits Times. Available at: https://www.nst.com.my/news/2016/05/144196/malaysias-crimeindex-sees-46-cent-increase. 72. Shao, H.J., Zhang, X.P. and Wang, Z., 2014, May. Novel closed-form auxiliary variables based algorithms for sensor node localization using AOA. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on (pp. 1414-1418). IEEE. 73. Shao, H.J., Zhang, X.P. and Wang, Z., 2014. Efficient closed-form algorithms for AOA based self-localization of sensor nodes using auxiliary variables. IEEE Transactions on Signal Processing, 62(10), pp.2580-2594. 74. Singh Manshahia, M., 2016. ‘Wireless Sensor Networks: A Survey’, International Journal of Scientific & Engineering Research, 7(4), pp. 393–422. Available at: http://www.ijser.org.138 75. Tang, M., Yan, F., Deng, S., Shen, L., Kuang, S. and Xing, S., 2016, October. Coverage optimization algorithms based on voronoi diagram in software-defined sensor networks. In Wireless Communications & Signal Processing (WCSP), 2016 8th International Conference on (pp. 1-5). IEEE. 76. Tang, Q., Sun, C., Wen, H. and Liang, Y., 2010, April. Cross-layer energy efficiency analysis and optimization in WSN. In Networking, Sensing and Control (ICNSC), 2010 International Conference on (pp. 138-142). IEEE. 77. Tao, D. and Wu, T.Y., 2015. A survey on barrier coverage problem in directional sensor networks. IEEE sensors journal, 15(2), pp.876-885. 78. Tellez, M., El-Tawab, S. and Heydari, M.H., 2016, December. IoT security attacks using reverse engineering methods on WSN applications. In Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on (pp. 182-187). IEEE. 79. The Difference Between Line Of Sight and Non-Line Of Sight _ TELETOPIX, 2016. Teletopix.org. Available at: http://www.teletopix.org/wimax/the-difference-between-lineof-sight-and-non-line-of-sight/. 80. Trigonometry_For Enthusiasts_Delaunay triangulation, 2013. WikiMedia. Available at: https://en.wikibooks.org/wiki/Trigonometry/For_Enthusiasts/Delaunay_triangulation (Accessed: 4 August 2017).139 81. Tu, M., Xu, J. and Diao, P., 2016, May. A method of secure data transmission in WSN based on certificateless signature technology. In Computer Supported Cooperative Work in Design (CSCWD), 2016 IEEE 20th International Conference on (pp. 504-508). IEEE. 82. Tuba, E., Tuba, M. and Simian, D., 2016, September. Wireless sensor network coverage problem using modified fireworks algorithm. In Wireless Communications and Mobile Computing Conference (IWCMC), 2016 International (pp. 696-701). IEEE. 83. Umale, M.D., Awate, S.S. and Gavali, V.N., 2015. Energy Efficient Techniques in WSN: A Review. 84. Wang, J., Scutari, G. and Palomar, D.P., 2011. Robust MIMO cognitive radio via game theory. IEEE Transactions on Signal Processing, 59(3), pp.1183-1201. 85. Wei, M. and Shi, Y., 2015, November. Brain storm optimization algorithms for optimal coverage of wireless sensor networks. In Technologies and Applications of Artificial Intelligence (TAAI), 2015 Conference on (pp. 120-127). IEEE. 86. Yadav, J. and Mann, S., 2013. Coverage in wireless sensor networks: A survey. Int. J. Electron. Comput. Sci. Eng, 2, pp.465-471. 87. Yadav, P., Kumar, R., Panda, S.K. and Chang, C.S., 2012. Energy-efficient thrust allocation for semi-submersible oil rig platforms using improved harmony search algorithm. IEEE Transactions on Industrial Informatics, 8(4), pp.913-924.140 88. Yetgin, H., Cheung, K.T.K., El-Hajjar, M. and Hanzo, L., 2014. Cross-layer network lifetime optimisation considering transmit and signal processing power in wireless sensor networks. IET Wireless Sensor Systems, 4(4), pp.176-182. 89. Yinbiao, S., 2014. ‘Internet of Things: Wireless Sensor Networks’, Internation Electronic Commision, (December), pp. 1–78. 90. Young, J. K., 2014. ‘Batteries A Practical Guide to Battery Technologies for Wireless Sensor Networking’. 275 Grove Street, Suite 2-130 Newton: Questex, pp. 1–6. Available at: http://www.sensorsmag.com/components/a-practical-guide-to-battery-technologies-forwireless-sensor-networking. 91. Zalyubovskiy, V. and Hwang, D.J., 2011, February. Energy-efficient area coverage by sensors with two sensing ranges. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication (p. 134). ACM. 92. Zeng, B. and Dong, Y., 2016. An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing, 41, pp.135-147. 93. Zhang, B., Simon, R. and Aydin, H., 2013. Harvesting-aware energy management for time-critical wireless sensor networks with joint voltage and modulation scaling. IEEE Transactions on Industrial Informatics, 9(1), pp.514-526.141 94. Zheng, K., Wang, H., Li, H., Xiang, W., Lei, L., Qiao, J. and Shen, X.S., 2017. EnergyEfficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks. IEEE Transactions on Vehicular Technology, 66(3), pp.2714-2726.

Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network

Similar Items