ارائه الگوریتم ردگیری هدف در شبکه های حسگر بی‌سیم با رعایت بهینگی مصرف توان با استفاده از کوانتیزاسیون مشاهدات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه جامع امام حسین(ع)

2 جامع امام حسین(ع)

چکیده

روش‌های متوسط اجماعی به دلیل تحمل‌پذیری خطای بالا، دقت ردگیری و مقیاس‌پذیری مناسب از متداول‌ترین روش‌های ردگیری در شبکه‌های حسگر بی‌سیم هستند. اما این روش‌ها به علت ایجاد سربار مخابراتی بالا، بهره‌وری انرژی و پهنای باند مناسبی را در این شبکه‌ها ندارند. الگوریتم ردگیری پیشنهادی با استفاده از خوشه‌بندی پویا (بر مبنای باند کرامر- رائوپسین) و کوانتیزاسیون وفقی مشاهدات، تعداد حسگرهای درگیر و سربار اطلاعاتی تبادل شده شبکه را کاهش می‌دهد. از سوی دیگر الگوریتم مذکور از ترکیب روش چندجانبه و فیلتر ذره‌ای برای ردگیری هدف بر اساس اطلاعات کوانتیزه دریافتی بهره می‌جوید. این موضوع باعث شده است که در عین کاهش دقت مشاهدات ارسالی به میزان 50 درصد (4 بیت)، خطای ردگیری فقط 10 درصد نسبت به الگوریتمی که در آن از کوانتیزاسیون استفاده نشده است بالاتر باشد.

کلیدواژه‌ها


   [1]      A. Arora, P. Dutta, S. Bapat, V. Kulathumani, H. Zhang, V. Naik, et al., “A line in the sand: a wireless sensor network for target detection, classification, and tracking,” Computer Networks, vol. 46, pp. 605-634, 2004.##
   [2]      A. Nadeau, M. Hassanalieragh, G. Sharma, and T. Soyata, “Energy awareness for supercapacitors using Kalman filter state-of-charge tracking,” Journal of Power Sources, vol. 296, pp. 383-391, 2015.##
   [3]      W. Tang, G. Zhang, J. Zeng, and Y. Yue, “Information weighted consensus-based distributed particle filter for large-scale sparse wireless sensor networks,” IET Communications, vol. 8, pp. 3113-3121, 2014.##
   [4]       X. Hu, Y.-H. Hu, and B. Xu, “Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network,” Signal Processing, IET, vol. 8, pp. 467-474, 2014.##
   [5]      H. Zhu, “Distributed Tracking, Decoding, and Demodulation Using Wireless Sensor Networks,” University of Minnesota, 2009.##
   [6]      Y. Zhou and J. Li, “Distributed sigma-point Kalman filtering for sensor networks: Dynamic consensus approach,” in Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on, pp. 5178-5183, 2009.##
   [7]      H. Long, Z. Qu, X. Fan, and S. Liu, “Improved average consensus scalable algorithm of target tracking for wireless sensor network,” in Control and Decision Conference (CCDC), 2012 24th Chinese, pp. 3336-3341, 2012.##
   [8]      A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Computer communications, vol. 30, pp. 2826-2841, 2007.##
   [9]      M. Talasila, S. Fu, and Y. Wan, “Energy conservative distributed average consensus through connected dominating set," in Wireless Communications and Networking Conference (WCNC), 2015 IEEE, pp. 843-848, 2015.##
[10]      Y. Zhou, J. Xu, and Y. Jing, “Comparison of centralized multi-sensor measurement and state fusion methods with ensemble Kalman filter for process fault diagnosis,” in Control and Decision Conference (CCDC), 2010 Chinese, pp. 3302-3307, 2010.##
[11]      G.-r. Bian, H.-h. Zhang, F.-c. Kong, J.-R. Cao, and H.-Y. Shi, “Research on Warehouse Target Localization and Tracking Based on KF and WSN,” Sensors & Transducers pp. 1726-5479, 2014.##
[12]      P. Chen, H. Ma, S. Gao, and Y. Huang, “Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations,” Mathematical Problems in Engineering, vol. 501, p. 981727, 2015.##
[13]      S. Fan, C. Sun, C. Yang, and B. Ye, “Fast distributed Kalman-Consensus filtering algorithm with local feedback regulation,” in Information and Automation, 2015 IEEE International Conference on, pp. 2345-2350, 2015.##
[14]      S. Wen, Z. Cai, and X. Hu, “Constrained Extended Kalman Filter for Target Tracking in Directional Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 2015, 2015.##
[15]      Q. Wen, Y. Zhou, L. Hu, J. Li, and D. Wang, “Comparison of filtering techniques for simultaneous localization and tracking,” in Estimation, Detection and Information Fusion (ICEDIF), 2015 International Conference on, pp. 387-392, 2015.##
[16]      M. Hernandez, T. Kirubarajan, and Y. Bar-Shalom, “Multisensor resource deployment using posterior Cramér-Rao bounds,” IEEE Transactions on Aerospace and Electronic Systems, vol. 40, pp. 399-416, 2004.##
[17]      A. Keshavarz-Mohammadiyan and H. Khaloozadeh, “Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise,” International Journal of Systems Science, vol. 48, pp. 899-908, 2017.##
[18]      J. Read, K. Achutegui, and J. Míguez, “A distributed particle filter for nonlinear tracking in wireless sensor networks,” Signal Processing, vol. 98, pp. 121-134, 2014.##
[19]      G. Zhang, Y. Ding, J. Xu, and W. Xu, “Tracking Algorithm of WSN Based on Improved Particle Filter,” in Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2012 11th International Symposium on, pp. 149-152, 2012.##
[20]      X. Wang, M. Fu, and H. Zhang, “Target tracking in wireless sensor networks based on the combination of KF and MLE using distance measurements,” Mobile Computing, IEEE Transactions on, vol. 11, pp. 567-576, 2012.##
[21]      Z. Jia, M. Chen, and C. Wu, “A Distributed Estimation Algorithm in Binary Sensor Network for Tracking Moving Target,” in Business, Economics, Financial Sciences, and Management, ed: Springer, pp. 691-698, 2012.##
[22]      Y. Zhou, J. Li, and D. Wang, “Target tracking in wireless sensor networks using adaptive measurement quantization,” Science China Information Sciences, vol. 55, pp. 827-838, 2012.##
[23]      M. Mansouri, L. Khoukhi, H. Nounou, and M. Nounou, “Secure and robust clustering for quantized target tracking in wireless sensor networks,” Journal of Communications and Networks, vol. 15, pp. 164-172, 2013.##
[24]      P. Tichavsky, C. H. Muravchik, and A. Nehorai, “Posterior Cramér-Rao bounds for discrete-time nonlinear filtering,” IEEE Transactions on signal processing, vol. 46, pp.       1386-1396, 1998.##
[25]      M. Mirsadeghi and A. Mahani, “Energy efficient fast predictor for WSN-based target tracking,” annals of telecommunications-annales des télécommunications, vol. 70, pp. 63-71, 2014.##
[26]      A. Keshavarz-Mohammadiyan and H. Khaloozadeh, “Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise,” International Journal of Systems Science, pp. 1-10, 2016.##
 [27]      X. Yang, W.-A. Zhang, L. Yu, and K. Xing, “Multi-rate distributed fusion estimation for sensor network-based target tracking,” IEEE Sensors Journal, vol. 16, pp. 1233-1242, 2016.##
[28]      M. Mirsadeghi and A. Mahani, “Energy efficient fast predictor for WSN-based target tracking,” annals of telecommunications-annales des télécommunications, vol. 70, pp. 63-71, 2015.##
[29]      M. Davoodi Monfared, E. Delfaraz Pahlevanlo, S. Ghobadi Babi, and M. Masoori, “A centralized algorithm based on voronoi diagram for hole detection problem in Wireless Sensor Networks,” Journal Of Electronical & Cyber Defence, vol. 5, 2017. (In Persian)##
[30]      E. B. Mazomenos, J. S. Reeve, and N. M. White, “A       range-only tracking algorithm for wireless sensor networks,” in Advanced Information Networking and Applications Workshops, 2009. WAINA'09, International Conference on, pp. 775-780, 2009.##
[31]      X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking, Part I. Dynamic models,” IEEE Transactions on aerospace and electronic systems, vol. 39, pp. 1333-1364, 2003.##
[32]      B. P. Gibbs, “Advanced Kalman filtering, least-squares and modeling: a practical handbook, ” John Wiley & Sons, 2011.##
[33]      M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” Signal Processing, IEEE Transactions on, vol. 50, pp. 174-188, 2002.##
[34]      F. Liang, C. Liu, and R. Carroll, “Advanced Markov chain Monte Carlo methods,” learning from past samples vol. 714: John Wiley & Sons, 2011.##
[35]      M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Transactions on signal processing, vol. 50, pp. 174-188, 2002.##
[36]      E. J. Msechu, A. Ribeiro, S. I. Roumeliotis, and G. B. Giannakis, “Distributed Kalman filtering based on quantized innovations,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3293-3296, 2008.##
[37]      E. Masazade, R. Niu, and P. K. Varshney, “Dynamic bit allocation for object tracking in wireless sensor networks,” IEEE Transactions on Signal Processing, vol. 60, pp.      5048-5063, 2012.##