مروری بر تجمیع داده مبتنی بر سنجش فشرده در شبکه های حسگر بیسیم
محورهای موضوعی : مهندسی الکترونیک
غلامرضا ایمانیان
1
,
محمد علی پورمینا
2
,
احمد صلاحی
3
1 - دانشجوی دکتری/دانشگاه آزاد اسلامی
واحد علوم و تحقیقات
دانشکده مهندسی برق و کامپیوتر-
گروه مهندسی برق و کامپیوتر،واحد تربت حیدریه، دانشگاه آزاد اسلامی، تربت حیدریه، ایران
2 - دانشیار، دانشکده مهندسی برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
3 - دانشیار، پژوهشگاه ارتباطات و فن‌آوری اطلاعات، تهران، ایران
کلید واژه: تجمیع داده, شبکه های حسگر بیسیم, فشرده سازی داده ها, سنجش فشرده,
چکیده مقاله :
در این مقاله مروری، هدف ما توصیف پیشرفت های اخیر تکنیک جمع آوری داده مبتنی بر سنجش فشرده در شبکه های حسگر بی سیم شامل تلاشهای صورت گرفته در تحقیقات جاری، چالش ها و روندهای تحقیقاتی است. سیگنالهای تنک و قابل فشرده شدن در بسیاری از زمینه های کاربردی شبکه های حسگر مانند نظارت محیطی و مراقبت از وسایل نقلیه حضور دارند. سنجش فشرده واجد ویژگیهای فراوانی از قبیل سادگی عملیات سنجش و فشرده سازی، عمومیت در سیگنالهای جمع آوری شده و افت قابل قبول در کیفیت بازسازی سیگنال می باشد که آن را برای استفاده در شبکه های حسگر جذاب می سازد. ازدست رفتن بسته ها نیز تقریباً به اندازه پروتکل های دیگر به شبکه آسیب نمی رساند و فقط برای هر اندازه گیری که به چاهک نرسیده است باعث کمی افت در کیفیت بازسازی سیگنال خواهد شد. بحث را با مقدمه ای مختصر بر نظریه سنجش فشرده آغاز می کنیم و سپس استفاده از این تکنیک را در شبکه های حسگر بیسیم شرح می دهیم. در نهایت، مسائل و چالش های تحقیقاتی پیش رو مورد بحث قرار می گیرد تا چشم اندازی جهت تحقیقات آینده فراهم شود
In this review article, we aim to describe recent advances in compressive sensing-based data aggregation techniques in wireless sensor networks, including current research efforts, challenges, and research trends. Sparse and compressible signals are present in many applications of sensor networks, such as environmental monitoring and vehicle surveillance. Compressive sensing has many properties such as simplicity of sensing and compression operations, universality, and an acceptable decrease in the quality of signal reconstruction, which makes it attractive for use in sensor networks. Packet drops do not damage the network as much as other protocols, and only for each measurement that does not reach the sink will cause a slight decrease in the quality of signal reconstruction. We begin the discussion with a brief introduction to compressive sensing theory and then describe the use of this technique in wireless sensor networks. Finally, the research issues and challenges ahead are discussed to provide a perspective for future research
[1] H. M. Haghighi, F. Bavi, and M. Etesami, "Trust-based routing in wireless sensor networks using fuzzy logic," Journal of Communication Engineering, vol. 10, no.37, pp. 43-54, 2020 (in Persian).
[2] H. Aminzadeh and A. Rezaee, "Energy Coverage Control in Wireless Sensor Networks Using Gravity Search Algorithm," Journal of Communication Engineering, vol. 9,no.36, pp. 1-16, 2020 (in Persian).
[3] M. Negahdari and M. Dadvar, "Energy-Efficient Wireless Sensor Networks Using Flat Cluster-based Routing Protocol and Evolutionary Algorithms," Journal of Communication Engineering, vol. 9,no.33, pp. 55-68, 2019 (in Persian).
[4] E. J. Candes and T. Tao, "Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?," in IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006.
[5] E. J. Candes and M. B. Wakin, "An Introduction To Compressive Sampling," in IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, 2008.
[6] D. L. Donoho, "Compressed sensing," in IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[7] E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, vol. 52, pp. 489-509, 2006.
[8] A. Beck and M. Teboulle, "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems," SIAM Journal on Imaging Sciences, vol. 2, pp. 183-202, 2009.
[9] J. A. Tropp and A. C. Gilbert, "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit," in IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, 2007.
[10] T. Blumensath and M. E. Davies, "Iterative hard thresholding for compressed sensing," Applied and Computational Harmonic Analysis, vol. 27, pp. 265-274, 2009.
[11] Eldar, Yonina C., and Gitta Kutyniok, eds. Compressed sensing: theory and applications. Cambridge university press, 2012.
[12] Y. Vardi, "Network tomography: Estimating source-destination traffic intensities from link data," Journal of the American statistical association, vol. 91, pp. 365-377, 1996.
[13] R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, "A simple proof of the restricted isometry property for random matrices," Constructive Approximation, vol. 28, pp. 253-263, 2008.
[14] W. U. Bajwa, J. D. Haupt, A. M. Sayeed, and R. D. Nowak, "Joint Source-Channel Communication for Distributed Estimation in Sensor Networks," IEEE Transactions on Information Theory, vol. 53, pp. 3629-3653, 2007.
[15] C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks," in Proceedings of the 15th annual international conference on Mobile computing and networking, 2009, pp. 145-156.
[16] G. Shen, S. Y. Lee, S. Lee, S. Pattem, A. Tu, B. Krishnamachari, A. Ortega, M. Cheng, S. Dolinar, and A. Kiely, "Novel distributed wavelet transforms and routing algorithms for efficient data gathering in sensor webs," in NASA Earth Science Technology Conference (ESTC2008), 2008.
[17] L. Xiang, J. Luo, and A. Vasilakos, "Compressed data aggregation for energy efficient wireless sensor networks," in Sensor, mesh and ad hoc communications and networks (SECON), 8th annual IEEE communications society conference on, 2011, pp. 46-54.
[18] S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Compressed sensing and routing in multi-hop networks," Technical Report, University of Southern California, 2009.
[19] W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random projections for refinable approximation," in Proceedings of the 6th international conference on Information processing in sensor networks, 2007, pp. 331-339.
[20] G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "On the interplay between routing and signal representation for compressive sensing in wireless sensor networks," in information Theory and Applications Workshop, 2009, pp. 206-215.
[21] R. Masiero, G. Quer, D. Munaretto, M. Rossi, J. Widmer and M. Zorzi, "Data Acquisition through Joint Compressive Sensing and Principal Component Analysis," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009, pp. 1-6.
[22] G. Quer, R. Masiero, G. Pillonetto, M. Rossi, and M. Zorzi, "Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework," IEEE Transactions on Wireless Communications, vol. 11, pp. 3447-3461, 2012.
[23] M. Hooshmand, M. Rossi, D. Zordan, and M. Zorzi, "Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks," IEEE Sensors Journal, vol. 16, pp. 1716-1729, 2016.
[24] G. Imanian, M. A. Pourmina, and A. Salahi, "Improved Sensor Sampling Method for the Joint Dictionary Learning and Compressive Data Gathering in WSNs with the Aid of Information Theory," Journal of Intelligent Procedures in Electrical Technology, vol. 13, pp. 41-57, 2022 (in Persian).
[25] G. Imanian, M.A. Pourmina, and A. Salahi, "Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation," Wireless Personal Communication, vol. 118, No. 2, pp. 1635-1654, 2021.
[26] W. Chen, I. J. Wassell and M. R. D. Rodrigues, "Dictionary Design for Distributed Compressive Sensing," in IEEE Signal Processing Letters, vol. 22, no. 1, pp. 95-99, Jan. 2015.
[27] P. Zhang, J. Wang, and W. Li, "A learning based joint compressive sensing for wireless sensing networks," Computer Networks, vol. 168, p. 107030, 2020.
[28] A. Hyvarinen, J. Karhunen, E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.
[29] A. Cichocki, D. Mandic, A-H. Phan, C. Caiafa, G. Zhou, Q. Zhao, and L. De Lathauwer, "Tensor Decompositions for Signal Processing Applications: From Two-way to Multi-way Component Analysis," IEEE Signal Processing Magazine, vol. 32, No. 2, pp. 145-163, 2015.
[30] C. Caiafa and A. Cichocki, "Computing sparse representations of multidimensional signals using Kronecker bases," in Neural Computation. , vol. 25, no. 1, pp. 186–220, 2013.
[31] C. F. Caiafa and A. Cichocki, "Multidimensional compressed sensing and their applications," Wiley Int. Rev. Data Min. and Knowl. Disc., vol. 3, pp. 355–380, 2013.
_||_[1] H. M. Haghighi, F. Bavi, and M. Etesami, "Trust-based routing in wireless sensor networks using fuzzy logic," Journal of Communication Engineering, vol. 10, no.37, pp. 43-54, 2020 (in Persian).
[2] H. Aminzadeh and A. Rezaee, "Energy Coverage Control in Wireless Sensor Networks Using Gravity Search Algorithm," Journal of Communication Engineering, vol. 9,no.36, pp. 1-16, 2020 (in Persian).
[3] M. Negahdari and M. Dadvar, "Energy-Efficient Wireless Sensor Networks Using Flat Cluster-based Routing Protocol and Evolutionary Algorithms," Journal of Communication Engineering, vol. 9,no.33, pp. 55-68, 2019 (in Persian).
[4] E. J. Candes and T. Tao, "Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?," in IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006.
[5] E. J. Candes and M. B. Wakin, "An Introduction To Compressive Sampling," in IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, 2008.
[6] D. L. Donoho, "Compressed sensing," in IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[7] E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, vol. 52, pp. 489-509, 2006.
[8] A. Beck and M. Teboulle, "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems," SIAM Journal on Imaging Sciences, vol. 2, pp. 183-202, 2009.
[9] J. A. Tropp and A. C. Gilbert, "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit," in IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655-4666, 2007.
[10] T. Blumensath and M. E. Davies, "Iterative hard thresholding for compressed sensing," Applied and Computational Harmonic Analysis, vol. 27, pp. 265-274, 2009.
[11] Eldar, Yonina C., and Gitta Kutyniok, eds. Compressed sensing: theory and applications. Cambridge university press, 2012.
[12] Y. Vardi, "Network tomography: Estimating source-destination traffic intensities from link data," Journal of the American statistical association, vol. 91, pp. 365-377, 1996.
[13] R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, "A simple proof of the restricted isometry property for random matrices," Constructive Approximation, vol. 28, pp. 253-263, 2008.
[14] W. U. Bajwa, J. D. Haupt, A. M. Sayeed, and R. D. Nowak, "Joint Source-Channel Communication for Distributed Estimation in Sensor Networks," IEEE Transactions on Information Theory, vol. 53, pp. 3629-3653, 2007.
[15] C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks," in Proceedings of the 15th annual international conference on Mobile computing and networking, 2009, pp. 145-156.
[16] G. Shen, S. Y. Lee, S. Lee, S. Pattem, A. Tu, B. Krishnamachari, A. Ortega, M. Cheng, S. Dolinar, and A. Kiely, "Novel distributed wavelet transforms and routing algorithms for efficient data gathering in sensor webs," in NASA Earth Science Technology Conference (ESTC2008), 2008.
[17] L. Xiang, J. Luo, and A. Vasilakos, "Compressed data aggregation for energy efficient wireless sensor networks," in Sensor, mesh and ad hoc communications and networks (SECON), 8th annual IEEE communications society conference on, 2011, pp. 46-54.
[18] S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Compressed sensing and routing in multi-hop networks," Technical Report, University of Southern California, 2009.
[19] W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random projections for refinable approximation," in Proceedings of the 6th international conference on Information processing in sensor networks, 2007, pp. 331-339.
[20] G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi, "On the interplay between routing and signal representation for compressive sensing in wireless sensor networks," in information Theory and Applications Workshop, 2009, pp. 206-215.
[21] R. Masiero, G. Quer, D. Munaretto, M. Rossi, J. Widmer and M. Zorzi, "Data Acquisition through Joint Compressive Sensing and Principal Component Analysis," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009, pp. 1-6.
[22] G. Quer, R. Masiero, G. Pillonetto, M. Rossi, and M. Zorzi, "Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework," IEEE Transactions on Wireless Communications, vol. 11, pp. 3447-3461, 2012.
[23] M. Hooshmand, M. Rossi, D. Zordan, and M. Zorzi, "Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks," IEEE Sensors Journal, vol. 16, pp. 1716-1729, 2016.
[24] G. Imanian, M. A. Pourmina, and A. Salahi, "Improved Sensor Sampling Method for the Joint Dictionary Learning and Compressive Data Gathering in WSNs with the Aid of Information Theory," Journal of Intelligent Procedures in Electrical Technology, vol. 13, pp. 41-57, 2022 (in Persian).
[25] G. Imanian, M.A. Pourmina, and A. Salahi, "Information-Based Node Selection for Joint PCA and Compressive Sensing-Based Data Aggregation," Wireless Personal Communication, vol. 118, No. 2, pp. 1635-1654, 2021.
[26] W. Chen, I. J. Wassell and M. R. D. Rodrigues, "Dictionary Design for Distributed Compressive Sensing," in IEEE Signal Processing Letters, vol. 22, no. 1, pp. 95-99, Jan. 2015.
[27] P. Zhang, J. Wang, and W. Li, "A learning based joint compressive sensing for wireless sensing networks," Computer Networks, vol. 168, p. 107030, 2020.
[28] A. Hyvarinen, J. Karhunen, E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.
[29] A. Cichocki, D. Mandic, A-H. Phan, C. Caiafa, G. Zhou, Q. Zhao, and L. De Lathauwer, "Tensor Decompositions for Signal Processing Applications: From Two-way to Multi-way Component Analysis," IEEE Signal Processing Magazine, vol. 32, No. 2, pp. 145-163, 2015.
[30] C. Caiafa and A. Cichocki, "Computing sparse representations of multidimensional signals using Kronecker bases," in Neural Computation. , vol. 25, no. 1, pp. 186–220, 2013.
[31] C. F. Caiafa and A. Cichocki, "Multidimensional compressed sensing and their applications," Wiley Int. Rev. Data Min. and Knowl. Disc., vol. 3, pp. 355–380, 2013.