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A Novel Version of GSA and its Application in the K-of-N Lifetime Problem in Two-Tiered WSN | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 5، دوره 51، شماره 1، شهریور 2019، صفحه 57-66 اصل مقاله (761.18 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2019.15273.5129 | ||
| نویسندگان | ||
| Sepehr Ebrahimi1؛ mohammad masoud javidi* 2 | ||
| 1Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 2Department of Computer Science ,Shahid Bahonar University,Kerman,Iran | ||
| چکیده | ||
| For the past decades, we have witnessed an extraordinary advancement in the field of Wireless Sensor Networks (WSNs) in both academic and industrial settings. Moreover, the network lifetime is regarded as one of the most critical issues in this field, and quite a large number of researchers are increasingly exploring this topic of interest. In this study, the linear-scaling method is initially implemented onto the Gravitational Search Algorithm (GSA) for the mass calculation. This is due to the fact that the exploitation and exploration abilities of the algorithm can be fully controlled using this approach. The results obtained from the simulation revealed that this novel GSA achieves the same level of performance as that of conventional GSA and significantly outperforms the state-of-the-art metaheuristic search algorithms. Additionally, this improved GSA can be readily utilized to solve the K-of-N lifetime problem in two-tiered WSN architecture. In our proposed method, the novel GSA was employed in order to find the optimum location of the base station to enhance network lifetime. Furthermore, the simulation results indicated that despite the simplicity in implementation, our proposed method has a higher level of performance compared to other approaches used to address K-of-N lifetime problem in two-tiered WSN architecture. | ||
| کلیدواژهها | ||
| Gravitational Search Algorithm (GSA)؛ Exploration and Exploitation؛ Wireless Sensor Network (WSN)؛ Network Lifetime؛ Energy Consumption؛ Linear Scaling | ||
| مراجع | ||
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[1] M.R.K. Aziz, K. Anwar, T. Matsumoto, Monitoring spot configuration of RSS-based factor graph geolocation technique in outdoor WSN environment, in: IEICE General Conference, 2015. [2] M. Amiribesheli, A. Benmansour, A. Bouchachia, A review of smart homes in healthcare, Journal of Ambient Intelligence and Humanized Computing, 6(4) (2015) 495-517. [3] A.B. Noel, A. Abdaoui, T. Elfouly, M.H. Ahmed, A. Badawy, M.S. Shehata, Structural health monitoring using wireless sensor networks: A comprehensive survey, IEEE Communications Surveys & Tutorials, 19(3) (2017) 1403-1423. [4] A. Jothimani, A.S. Edward, K.M. Gowthem, R. Karthikeyan, Implementation of smart sensor interface network for water quality monitoring in industry using iot, Indian Journal of Science and Technology, 10(6) (2017). [5] B. Prabhu, M. Pradeep, E. Gajendran, Military Applications of Wireless Sensor Network System, (2017). [6] T.R. Sheltami, S. Khan, E.M. Shakshuki, M.K. Menshawi, Continuous objects detection and tracking in wireless sensor networks, Journal of Ambient Intelligence and Humanized Computing, 7(4) (2016) 489-508. [7] T.R. Sheltami, A. Bala, E.M. Shakshuki, Wireless sensor networks for leak detection in pipelines: a survey, Journal of Ambient Intelligence and Humanized Computing, 7(3) (2016) 347-356. [8] O. Ojuroye, R. Torah, S. Beeby, A. Wilde, Smart textiles for smart home control and enriching future wireless sensor network data, in: Sensors for Everyday Life, Springer, 2017, pp. 159-183. [9] H.Y. Shwe, P.H.J. Chong, WSN-based energy-efficient data communication protocol for smart green building environment, in: Telecommunications Energy Conference (INTELEC), 2015 IEEE International, IEEE, 2015, pp. 1-5. [10] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in: System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on, IEEE, 2000, pp. 10 pp. vol. 12. [11] S.D. Muruganathan, D.C. Ma, R.I. Bhasin, A.O. Fapojuwo, A centralized energy-efficient routing protocol for wireless sensor networks, IEEE Communications Magazine, 43(3) (2005) S8-13. [12] D. Niculescu, B. Nath, Ad hoc positioning system (APS) using AOA, in: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, Ieee, 2003, pp. 1734-1743. [13] C. Sharma, V. Pattanik, Survey Paper of Energy Efficient Data Aggregation Protocol in Wireless Sensor Network, International Journal of Computer Applications, 150(8) (2016). [14] K. Vijayan, A. Raaza, A novel cluster arrangement energy efficient routing protocol for wireless sensor networks, Indian Journal of science and Technology, 9(2) (2016). [15] G. Kannan, T.S.R. Raja, Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network, Egyptian Informatics Journal, 16(2) (2015) 167-174. [16] J. Pan, L. Cai, Y.T. Hou, Y. Shi, S.X. Shen, Optimal base-station locations in two-tiered wireless sensor networks, IEEE Transactions on Mobile Computing, 4(5) (2005) 458-473. [17] T. Shankar, S. Shanmugavel, A. Rajesh, Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks, Swarm and Evolutionary Computation, 30 (2016) 1-10. [18] X. Yuan, M. Elhoseny, H.K. El-Minir, A.M. Riad, A genetic algorithm-based, dynamic clustering method towards improved WSN longevity, Journal of Network and Systems Management, 25(1) (2017) 21-46. [19] T.-P. Hong, G.-N. Shiu, Solving the K-of-N Lifetime Problem by PSO, International Journal of Engineering, Science and Technology, 1(1) (2009) 136-147. [20] M. Zain-Ul-Abidin, H. Maqsood, U. Qasim, Z.A. Khan, N. Javaid, Improved genetic algorithm based energy efficient routing in two-tiered wireless sensor networks, in: Network-Based Information Systems (NBiS), 2016 19th International Conference on, IEEE, 2016, pp. 382-386. [21] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm, Information sciences, 179(13) (2009) 2232-2248. [22] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, BGSA: binary gravitational search algorithm, Natural Computing, 9(3) (2010) 727-745. [23] S. Mirjalili, S.Z.M. Hashim, H.M. Sardroudi, Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm, Applied Mathematics and Computation, 218(22) (2012) 11125-11137. [24] C. Purcaru, R.-E. Precup, D. Iercan, L.-O. Fedorovici, R.-C. David, F. Dragan, Optimal robot path planning using gravitational search algorithm, Int. J. Artif. Intell, 10 (2013) 1-20. [25] J.R. Parvin, C. Vasanthanayaki, Gravitational search algorithm based mobile aggregator sink nodes for energy efficient wireless sensor networks, in: Circuits, Power and Computing Technologies (ICCPCT), 2013 International Conference on, IEEE, 2013, pp. 1052-1058. [26] M.K. Rafsanjani, M.B. Dowlatshahi, Using gravitational search algorithm for finding near-optimal base station location in two-tiered WSNs, International Journal of Machine Learning and Computing, 2(4) (2012) 377. [27] W. Zhao, Adaptive image enhancement based on gravitational search algorithm, Procedia Engineering, 15 (2011) 3288-3292. [28] B. González, F. Valdez, P. Melin, G. Prado-Arechiga, Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition, Expert Systems with Applications, 42(14) (2015) 5839-5847. [29] M. Behrang, E. Assareh, M. Ghalambaz, M. Assari, A. Noghrehabadi, Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm), Energy, 36(9) (2011) 5649-5654. [30] X. Li, K. Tang, M.N. Omidvar, Z. Yang, K. Qin, H. China, Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization, gene, 7(33) (2013) 8. [31] X. Li, A. Engelbrecht, M.G. Epitropakis, Benchmark functions for CEC’2013 special session and competition on niching methods for multimodal function optimization, RMIT University, Evolutionary Computation and Machine Learning Group, Australia, Tech. Rep, (2013). [32] J. Liang, B. Qu, P. Suganthan, A.G. Hernández-Díaz, Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212 (2013) 3-18. [33] J. Zhang, A.C. Sanderson, JADE: adaptive differential evolution with optional external archive, IEEE Transactions on evolutionary computation, 13(5) (2009) 945-958. [34] M.M. Noel, A new gradient based particle swarm optimization algorithm for accurate computation of global minimum, Applied Soft Computing, 12(1) (2012) 353-359. [35] S. Sarafrazi, H. Nezamabadi-Pour, S. Saryazdi, Disruption: a new operator in gravitational search algorithm, Scientia Iranica, 18(3) (2011) 539-548. [36] M. Shams, E. Rashedi, A. Hakimi, Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier, Applied Mathematics and Computation, 258 (2015) 436-453. [37] N. Cressie, H. Whitford, How to use the two sample t‐test, Biometrical Journal, 28(2) (1986) 131-148. | ||
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