بهینه‌سازی طرح تطبیقی شبکه حسگر بی‌سیم با استفاده از الگوریتم جستجوی گرانشی باینری کوانتومی
محورهای موضوعی : مهندسی برق و کامپیوترمینا میرحسینی 1 , فاطمه بارانی 2 , حسین نظام‌آبادی‌پور 3
1 - مجتمع آموزش عالی بم
2 - مجتمع آموزش عالی بم
3 - دانشگاه شهید باهنر کرمان
کلید واژه: الگوریتم جستجوی گرانشی الگوریتم جستجوی گرانشی باینری کوانتومی شبکه حسگر بی‌سیم, کشاورزی نظارت‌شده,
چکیده مقاله :
افزایش طول عمر، کارایی و کاهش مصرف انرژی در شبکه‌های حسگر بی‌سیم یک مسئله چندهدفه است که یکی از موضوعات چالش‌برانگیز در تحقیقات اخیر شده است. در این مقاله به منظور افزایش کارایی و طول عمر شبکه‌های حسگر بی‌سیم، با استفاده از الگوریتم جستجوی گرانشی باینری کوانتومی روشی پیشنهاد شده که علاوه بر کمینه‌کردن مصرف انرژی، محدودیت‌های ارتباطی شبکه و نیازمندی‌های کاربرد خاص آن نیز برآورده می‌گردد. این الگوریتم روی یک شبکه حسگر بی‌سیم در کاربرد کشاورزی و به منظور نظارت دقیق و اصولی شرایط محیطی استفاده شده است. نتیجه به کارگیری این الگوریتم روی شبکه حسگر بی‌سیم، یک طرح بهینه خواهد بود که در آن حالت عملیاتی هر حسگر شامل سرگروه، حسگر فعال با محدوده حسگری بلند، حسگر فعال با محدوده حسگری کوتاه و غیر فعال را با توجه به محدودیت‌های مسئله مشخص می‌نماید. نتایج شبیه‌سازی نشان می‌دهد که این الگوریتم در شبکه حسگر بی‌سیم در مقایسه با الگوریتم وراثتی و الگوریتم ازدحام جمعیت نتایج بهتری را ارائه می‌دهد و متعاقباً قادر است که طول عمر شبکه را نسبت به دو الگوریتم دیگر به نحو مطلوب‌تری افزایش دهد.
In this paper, the binary quantum-inspired gravitational search algorithm is adapted to dynamically optimize the design of a wireless sensor network towards improving energy consumption and extending the lifetime of the network, so that the application-specific requirements and communication constraints are fulfilled. The proposed approach is applied on a wireless sensor network used in the application of precise agriculture to monitor environmental conditions. This algorithm would present an optimal design detecting operational mode of each sensor including cluster head, high signal range, low signal range and inactive modes taking into consideration the constraints of the network. The simulation results indicate the most performance of the proposed method in comparison with binary genetic algorithm and particle swarm optimization.
[1] H. Alemdar and C. Ersoy, "Wireless sensor networks for healthcare: a survey," J. of Computer Networks, vol. 54, no. 15, pp. 2688-2710, Oct. 2010.
[2] V. L. Boginski, C. W. Commander, P. M. Pardalos, and Y. Ye, Sensors: Theory, Algorithms, and Applications, Springer Optimization and Its Applications, Springer-Verlag, New York, 2011.
[3] S. K. Das, G. Ghidini, A. Navarra, and C. M. Pinotti, "Localization and scheduling protocols for actor-centric sensor networks," J. of Networks, vol. 59, no. 3, pp. 299-319, May 2012.
[4] M. K. Rafsanjani, M. Mirhoseini, and R. Nourizadeh, "A multi-objective evolutionary algorithm for improving energy consumption in wireless sensor networks," Bull. Transilv. Univ. Brasov, vol. 6, no. 2, pp. 107-116, Jan. 2013.
[5] J. Park, S. Lee, and S. Yoo, "Time slot assignment for converge cast in wireless sensor networks," J. of Parallel Distrib. Comput, vol. 83, pp. 70-82, Sept. 2015.
[6] F. Carrabs, R. Cerulli, C. D'Ambrosio, M. Gentili, and A. Raiconi, "Maximizing lifetime in wireless sensor networks with multiple sensor families," Computers & Operations Research, vol. 60, pp. 121-137, Aug. 2015.
[7] M. Rebaia, M. Leberreb, H. Snoussic, F. Hnaiend, and L. Khoukhie, "Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks," Computers & Operations Research, vol. 59, pp. 11-21, Jul. 2015.
[8] K. P. Ferentinos and T. A. Tsiligiridis, "Adaptive design optimization of wireless sensor networks using genetic algorithms," J. of Computer Networks, vol. 51, no. 4, pp. 1031-1051, Mar. 2007.
[9] K. P. Ferentinos and T. A. Tsiligiridis, "A memetic algorithm for optimal dynamic design of wireless sensor networks," J. of Computer Communications, vol. 33, no. 2, pp. 250-258, Feb. 2010.
[10] B. Krishnamachari and F. Ordonez, "Analysis of energy-efficient, fair routing in wireless sensor networks through non-linear optimization," in Proc. of IEEE Vehicular Technology Conf.-Fall, pp. 2844-2848, Orlando, FL, USA, Oct. 2003.
[11] K. P. Ferentinos and T. A. Tsiligiridis, "Evolutionary energy management and design of wireless sensor networks," in Proc. of Second Annual IEEE Communications Society Conf. on Sensor and Ad Hoc Communications and Networks, pp. 406-417, Sept. 2005.
[12] S. Hojjatoleslami, V. Aghazarian, M. Dehghan, and N. G. Motlagh, "PSO based node placement optimization for wireless sensor networks," in Proc. of Intelligent Systems and Applications, pp. 12-17, Sept. 2011.
[13] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "GSA: a gravitational search algorithm," J. of Information Sciences, vol. 179, no. 13, pp. 2232-2248, Jun. 2009.
[14] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "BGSA: binary gravitational search algorithm," J. of Nat Comput, vol. 9, no. 3, pp. 727-745, Sept. 2010.
[15] E. Rashedi and H. Nezamabadi-pour, "Feature subset selection using improved binary gravitational search algorithm," J. of Intelligent and Fuzzy Systems, vol. 26, no. 3, pp. 1211-1221, Apr. 2014.
[16] X. H. Han, X. M. Chang, L. Quan, X. Y. Xiong, J. X. Li, Z. X. Zhang, and Y. Liu, "Feature subset selection by gravitational search algorithm optimization," J. of Information Sciences, vol. 281, pp. 128-146, Oct. 2014.
[17] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, "Filter modeling using gravitational search algorithm," J. of Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 117-122, Feb. 2011.
[18] A. Chatterjee and G. K. Mahanti, "Comparative performance of gravitational search algorithm and modified particle swarm optimization algorithm for synthesis of thinned scanned concentric ring array antenna," Electromagnetics Research, vol. 25, pp. 331-348, Sept. 2010.
[19] M. Yin, Y. Hu, F. Yang, X. Li, and W. Gu, "A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering," J. of Expert Systems with Applications, vol. 38, no. 8, pp. 9319-9324, Aug. 2011.
[20] S. Sarafrazi and H. Nezamabadi-pour, "Facing the classification of binary problems with a GSA-SVM hybrid system," J. of Math. Comput. Model, vol. 57, no. 1-2, pp. 270-278, Jan. 2013.
[21] C. Li and J. Zhou, "Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm," Energy Conversion and Management, vol. 52, no. 1, pp. 374-381, Jan. 2011.
[22] M. Taghipour, A. R. Moradi, and M. Yazdani-Asrami, "Identification of magnetizing inrush current in power transformers using GSA trained ANN for educational purposes," in Proc. of IEEE Conf. on Open Systems, ICOS'10, pp. 23-27, Dec. 2010.
[23] M. Soleimanpour-Moghadam, H. Nezamabadipour, and M. M. Farsangi, "A quantum behaved gravitational search algorithm," Intelligent Information Management, vol. 4, no. 6, pp. 390-395, Nov. 2012.
[24] M. Soleimanpour-Moghadam and H. Nezamabadipour, "An improved quantum behaved gravitational search algorithm," in Proc. of 20th Iranian Conf. on Electrical Engineering, ICEE'12, pp. 711-715, May 2012.
[25] M. Soleimanpour-Moghadam, H. Nezamabadipour, and M. M. Farsangi, "A quantum inspired gravitational search algorithm for numerical function optimization," J. of Information Sciences, vol. 267, no. 20, pp. 83-100, May 2014.
[26] A. A. Ibrahim, A. Mohamed, and H. Shareef, "A novel quantum-inspired binary gravitational search algorithm in obtaining optimal power quality monitor placement," J. of Applied Science, vol. 12, no. 9, pp. 822-830, Jun. 2012.
[27] H. Nezamabadi-pour, "A quantum-inspired gravitational search algorithm for binary encoded optimization problems," J. of Engineering Application of Artificial Intelligence, vol. 40, pp. 62-75, Apr. 2015.
