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06 10.22034/zagros.2020.0107.0950 https://rcsj.ir/user/articles/111 Farzanegan Pub mEDRA 01 01 111 zagros Publisher IR 01 111 JB 24 1 4 01 202605 7 10 01 1. American Heart Association. [On Line]. Available on URL : http://www.heart.org/ HEARTORG. 2. Amani F, Kazemnejad A, Habibi R, Hajizadeh E. Pattern of mortality trend in Iran during 1970-2009. Journal of Gorgan University of Medical Sciences. 2011;12(4). 3. Narouee.i and Zamani.B, Diagnosis of Cardiac Arrhythmia Using Fisher Method and Minimal Squares Support Mechanism, Electronic Conference on New Research in Science and Technology, 1394. 4. Samad S, Khan SA, Haq A, Riaz A. Classification of arrhythmia. International Journal of Electrical Energy. 2014 Mar;2(1):57-61. 5. Jadhav SM, Nalbalwar SL, Ghatol AA. ECG arrhythmia classification using modular neural network model. In2010 IEEE EMBS conference on biomedical engineering and sciences (IECBES) 2010 Dec 2 (pp. 62-66). IEEE. 6. S. M. Jadhav, S. L. Nalbalwar, and A. Ghatol, “Artificial neural network based cardiac arrhythmia classification using ECG signal data,” in Electronics and Information Engineering (ICEIE), 2010 International Conference On, 2010, vol. 1, pp. V1-228. 7. Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Transactions on Biomedical Engineering. 2007 Nov 19;54(12):2172-85. 8. Mannurmath JC, Raveendra M. MATLAB based ECG signal classification. International Journal of Science, Engineering and Technology Research (IJSETR). 2014 Jul;3(7):1946-50. 9. Mitra M, Samanta RK. Cardiac arrhythmia classification using neural networks with selected features. Procedia Technology. 2013 Jan 1;10:76-84. 10. Mahmoodabadi SZ, Ahmadian A, Abolhasani MD, Eslami M, Bidgoli JH. ECG feature extraction based on multiresolution wavelet transform. In2005 IEEE Engineering in Medicine and Biology 27th Annual Conference 2006 Jan 17 (pp. 3902-3905). IEEE. 11. Kohli N, Verma NK. Arrhythmia classification using SVM with selected features. International Journal of Engineering, Science and Technology. 2011;3(8):122-31. 12. Saritha C, Sukanya V, Murthy YN. ECG signal analysis using wavelet transforms. Bulg. J. Phys. 2008 Feb;35(1):68-77. 13. Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836. 2017 Jul 6. 14. Pyakillya B, Kazachenko N, Mikhailovsky N. Deep learning for ECG classification. InJournal of physics: conference series 2017 Oct (Vol. 913, No. 1, p. 012004). IOP Publishing. 15. K. Bache and M. Lichman, “UCI machine learning repository.” 2013. 16. Zhou ZH. Ensemble methods: foundations and algorithms. Chapman and Hall/CRC; 2012 Jun 6. 17. Leutheuser, H., Gradl, S., Kugler, P., Anneken, L., Arnold, M., Achenbach, S. and Eskofier, B.M., 2014, August. Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2690-2693). IEEE. 18. de Medeiros TF, de Lima Borges EV, Cavalcante BÉ, Cavalvanti AB, Andrezza IL, Batista LV. Heart arrhythmia classification using the PPM algorithm. InISSNIP Biosignals and Biorobotics Conference 2011 2011 Jan 6 (pp. 1-5). IEEE. 19. Dilmac S, Korurek M. A new ECG arrhythmia clustering method based on modified artificial bee colony algorithm, comparison with GA and PSO classifiers. In2013 IEEE INISTA 2013 Jun 19 (pp. 1-5). IEEE.. 20. Hadjem, M. and Naït-Abdesselam, F., 2015, June. An ECG T-wave anomalies detection using a lightweight classification model for wireless body sensors. In 2015 IEEE International Conference on Communication Workshop (ICCW) (pp. 278-283). IEEE.. 21. Zeraatkar E, Kermani S, Mehridehnavi A, Aminzadeh A, Zeraatkar E, Sanei H. Arrhythmia detection based on morphological and time-frequency features of T-wave in electrocardiogram. Journal of medical signals and sensors. 2011 May;1(2):99. 22. Waseem K, Javed A, Ramzan R, Farooq M. Using evolutionary algorithms for ecg arrhythmia detection and classification. In2011 Seventh International Conference on Natural Computation 2011 Jul 26 (Vol. 4, pp. 2386-2390). IEEE. 23. Khazaee A. Heart beat classification using particle swarm optimization. International Journal of Intelligent Systems and Applications. 2013 May 1;5(6):25. 24. Das MK, Ari S. ECG beats classification using mixture of features. International scholarly research notices. 2014;2014. 25. Jadhav SM, Nalbalwar SL, Ghatol AA. Artificial neural network models based cardiac arrhythmia disease diagnosis from ECG signal data. International Journal of Computer Applications. 2012 Apr 30;44(15):8-13. 26. Rai HM, Trivedi A, Shukla S, Dubey V. ECG arrhythmia classification using daubechies wavelet and radial basis function neural network. In2012 Nirma University International Conference on Engineering (NUiCONE) 2012 Dec 6 (pp. 1-6). IEEE. 27. Ahmed AF, Owis MI, Yassine IA. Novel Bayesian classifier discriminant function optimization strategies for arrhythmia classification. InIEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2014 Jun 1 (pp. 693-696). IEEE. 28. Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponce R. Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. Journal of applied research and technology. 2015;13(2):261-9. 29. Sutar RG, Kothari AG. Intelligent electrocardiogram pattern classification and recognition using low-cost cardio-care system. IET Science, Measurement & Technology. 2015 Feb 26;9(1):134-43. 30. Jatmiko W, Setiawan IM, Akbar MA, Suryana ME, Wardhana Y, Rachmadi MF. Automatic Arrhythmia beat detection: algorithm, system, and implementation. Makara Journal of Technology. 2016 Aug 30;20(2):82-92. 31. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, San Tan R. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Applied Intelligence. 2019 Jan 15;49(1):16-27. 32. Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, San Tan R, Chen M, Acharya UR. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in biology and medicine. 2018 Mar 1;94:19-26. 33. Ramirez F, Allende-Cid H, Veloz A, Allende H. Neuro-fuzzy-based arrhythmia classification using heart rate variability features. In2010 XXIX International Conference of the Chilean Computer Science Society 2010 Nov 15 (pp. 205-211). IEEE.. 34. Debnath T, Biswas T, Ashik MH, Dash S. Auto-Encoder Based Nonlinear Dimensionality Reduction of ECG data and Classification of Cardiac Arrhythmia Groups Using Deep Neural Network. In2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) 2018 Sep 13 (pp. 27-31). IEEE. 1 A01 امیراحمد نیری امیراحمد نیری C. امیراحمد نیری 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2019.1113.0822 https://rcsj.ir/user/articles/110 Farzanegan Pub mEDRA 01 01 110 zagros Publisher IR 01 110 JB 24 1 4 01 202605 7 10 01 s:3975:"[1] Chakravorty. P.(2018). What is a signal? [lecture notes]. IEEE Signal Processing Magazine.;35(5):175-177. DOI:10.1109/MSP.2018.2832195 [2] Gonzalez. R.(2018). Digital image processing. New York, NY: Pearson; ISBN: 978-0-13-335672-4. OCLC 966609831 [3] Sarfraz. M.(2020). Introductory Chapter: On Digital Image Processing. InDigital Imaging 2020 May 13. IntechOpen. [4] Josep. Arnal.. Luis. Súcar.(2020). Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images. Appl. Sci. 10, 243; doi:10.3390/app10010243. [5] Wang. C.. Yan. Z..& Pedrycz. W.(2020). Zhou M, Li Z. A weighted fidelity and regularization-based method for mixed or unknown noise removal from images on graphs. IEEE Transactions on Image Processing. 2020 Feb 25;29:5229-43. [6] Jindal. K.. Kumar. R.(2016). A Note on “Data mining based noise diagnosis and fuzzy filter design for image processing”. Computers & Electrical Engineering. 2016 Jan 1;49:50-1. [7] Wang. Y.. Wu. G..& Chen. GS.(2014). Chai T. Data mining based noise diagnosis and fuzzy filter design for image processing. Computers & Electrical Engineering. 2014 Oct 1;40(7):2038-49. [8] Liu. J.. Huan. ZD.. Huang. HY..& Zhang. HL.(2009). An adaptive method for recovering image from mixed noisy data. Int J Comput Vis; 85(2):182–91. [9] Chan. RH.. Dong. Y.(2010). Hintermuller M. An efficient two-phase L1–TV method for restoring blurred images with impulse noise. IEEE Trans Image Process;19(7):1731–9. [10] Cai. JF.. Chan. R..& Nikolova. M.(2008). Two-phase methods for deblurring images corrupted by impulse plus gaussian noise. Inverse Problem Imag; 2(2):187–204. [11] Cai. JF.. Chan. RH..& Nikolova. M.(2010). Fast two-phase image deblurring under impulse noise. J Math Imag Vis;36(1):46–53. [12] Zhang. B.. Fadili. MJ.. Starck. JL..& Olivo-Marin. JC.(2007). Multiscale variance stabilizing transform for mixed-Poisson–Gaussian processes and its application in bioimaging. In: Proceedings of IEEE international conference on image processing; p. 233–6. [13] Morillas. S.. Gregori. V..& Hervas. A.(2009). Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images. IEEE Trans Image Process;18(7):1452–66. [14] Yang. JX.. Wu. HR.(2009). Mixed Gaussian and uniform impulse noise analysis using robust estimation for digital images. In: Proceedings of the 16th international conference on digital signal processing; p. 468–72. [15] Lopez-Rubio. E.(2010). Restoration of images corrupted by Gaussian and uniform impulsive noise. Pattern Recogn;43(5):1835–46. [16] Ji. LP.. Zhang. Y.(2008). A mixed noise image filtering method using weighted-linking PCNNs. Neurocomputing;71(13–15):2986–3000. [17] Huang. YM.. Ng. MK..& Wen. YW.(2009). Fast image restoration methods for impulse and Gaussian noise removal. IEEE Signal Proc Lett;16(6):457–60. [18] Ghita. O.. Whelan. PF.(2010). A new GVF-based image enhancement formulation for use in the presence of mixed noise. Pattern Recogn;43(8):2646–58. [19] Lin. CH.. Tsai. JS..& Chiu. CT.(2010). Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans Image Process;19(9):2307–20. [20] Miller. G.. Martz. HF.. Little. TT..& Guilmette. R.(2002). Using exact poisson likelihood functions in Bayesian interpretation of counting measurements. Health Phys;83(4):512–8. [21] Brodsky. A.(1992). Exact calculation of probabilities of false positives and false negatives for low background counting. Health Phys; 63(2):198–204. [22] Wang. LX..Mendel. JM.(1992). Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybernet; 22(6):1414–27. [23] Wang. LX.(2003). The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans Fuzzy Syst;11(6):768–82. [24] Wang. YF.. Wang. DH..& Chai. TY.(2011). Extraction and adaptation of fuzzy rules for friction modeling and control compensation. IEEE Trans Fuzzy Syst; 19(4):682–93."; 1 A01 محمدرضا فرهمند، الهام سادات حجازی محمدرضا فرهمند، الهام سادات حجازی C. محمدرضا فرهمند، الهام سادات حجازی 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2020.0107.0951 https://rcsj.ir/user/articles/112 Farzanegan Pub mEDRA 01 01 112 zagros Publisher IR 01 112 JB 24 1 4 01 202605 7 10 01 1. Ouriques, J.F., et al., On the Use of Fault Abstractions for Assessing System Test Case Prioritization Techniques. 2016. 1-10. 2. Gondra, I., Applying machine learning to software fault-proneness prediction. Journal of Systems and Software, 2008. 81: p. 186-195. 3. Institute of, E. and E. Electronics, IEEE standard glossary of software engineering terminology. 1990. 4. H, W.E.I., et al., Software Defect Prediction via Deep Belief Network. Chinese Journal of Electronics, 2019. 28(5): p. 925-932. 5. Shuai, B., et al. Software Defect Prediction Using Dynamic Support Vector Machine. in 2013 Ninth International Conference on Computational Intelligence and Security. 2013. 6. Hassan, M.M., et al. Testability and Software Robustness: A Systematic Literature Review. in 2015 41st Euromicro Conference on Software Engineering and Advanced Applications. 2015. 7. Tomaszewski, P., et al., Statistical models vs. expert estimation for fault prediction in modified code – an industrial case study. Journal of Systems and Software, 2007. 80(8): p. 1227-1238. 8. Robitaille, N. and I.M. Harris, When more is less: Extraction of summary statistics benefits from larger sets. Journal of Vision, 2011. 11(12): p. 18-18. 9. Arisholm, E., L.C. Briand, and E.B. Johannessen, A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software, 2010. 83(1): p. 2-17. 10. Montufar, G., Restricted Boltzmann Machines: Introduction and Review. 2018. 11. Hinton, G., Deep belief networks. Scholarpedia, 2009. 4: p. 5947. 12. Léveillé, J., I. Hayashi, and K. Fukushima, A Probabilistic WKL Rule for Incremental Feature Learning and Pattern Recognition. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2014. 18(4): p. 672-681. 13. Hinton, G., S. Osindero, and Y.-W. Teh, A Fast Learning Algorithm for Deep Belief Nets. Neural computation, 2006. 18: p. 1527-54. 14. Hinton, G.E. and R.R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks. Science, 2006. 313(5786): p. 504. 15. Larochelle, H. and Y. Bengio, Classification using discriminative restricted Boltzmann machines. 2008. 536-543. 16. Salakhutdinov, R., A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering. Vol. 227. 2007. 791-798. 17. Coates, A., A. Ng, and H. Lee, An Analysis of Single-Layer Networks in Unsupervised Feature Learning. Journal of Machine Learning Research - Proceedings Track, 2011. 15: p. 215-223. 18. Salakhutdinov, R. and G. Hinton, Replicated Softmax: an Undirected Topic Model. 2009. 1607-1614. 19. Carleo, G. and M. Troyer, Solving the quantum many-body problem with artificial neural networks. Science, 2017. 355(6325): p. 602. 20. Melko, R., et al., Restricted Boltzmann machines in quantum physics. Nature Physics, 2019. 15. 21. Carreira-Perpinan, M. and G. Hinton, On contrastive divergence learning. 2005. 22. Hinton, G.E., A Practical Guide to Training Restricted Boltzmann Machines, in Neural Networks: Tricks of the Trade (2nd ed.), G. Montavon, G.B. Orr, and K.-R. Müller, Editors. 2012, Springer. p. 599-619. 23. Ackley, D.H., G.E. Hinton, and T.J. Sejnowski, A learning algorithm for boltzmann machines. Cognitive Science, 1985. 9(1): p. 147-169. 24. Hinton, G., A practical guide to training restricted boltzmann machines[J]. Momentum, 2010. 9: p. 926-947. 25. Ma, X. and X. Wang, Average Contrastive Divergence for Training Restricted Boltzmann Machines. Entropy, 2016. 18: p. 35. 26. Fischer, A. and C. Igel, Training restricted Boltzmann machines: An introduction. Pattern Recognition, 2014. 47(1): p. 25-39. 27. Emam, K.E., et al., The confounding effect of class size on the validity of object-oriented metrics. IEEE Transactions on Software Engineering, 2001. 27(7): p. 630-650. 28. Hinton, G., Training products of experts by minimizing contrastive divergence. Neural Computation, 2000. 14: p. 1711-1800. 29. Varastehpour, S., et al., An Adaptive Method for Vein Recognition Enhancement Using Deep Learning. 2019. 1-6. 30. Dejaeger, K., T. Verbraken, and B. Baesens, Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers. IEEE Transactions on Software Engineering, 2013. 39(2): p. 237-257. 31. Czibula, G., Z. Marian, and I.G. Czibula, Software defect prediction using relational association rule mining. Information Sciences, 2014. 264: p. 260-278. 32. Hammouri, A., et al., Software Bug Prediction using Machine Learning Approach. International Journal of Advanced Computer Science and Applications, 2018. 9. 33. Lazic, L., Software Errors Analysis and Prevention. 2006. 34. Wang, T., et al., Software defect prediction based on classifiers ensemble. Journal of Information and Computational Science, 2011. 8: p. 4241-4254. 35. He, P., et al., An empirical study on software defect prediction with a simplified metric set. Inf. Softw. Technol., 2015. 59(C): p. 170–190. 36. Keerthi, S. and C.-J. Lin, Asymptotic Behaviors Of Support Vector Machines. Neural computation, 2003. 15: p. 1667-89. 37. Monden, A., et al., Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing. IEEE Transactions on Software Engineering, 2013. 39(10): p. 1345-1357. 38. Lessmann, S., et al., Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. Software Engineering, IEEE Transactions on, 2008. 34: p. 485-496. 39. Hall, T., et al., A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Transactions on Software Engineering, 2012. 38(6): p. 1276-1304. 40. Shepperd, M., et al., Data Quality: Some Comments on the NASA Software Defect Datasets. IEEE Transactions on Software Engineering, 2013. 39(9): p. 1208-1215. 41. Koru, A. and H. Liu, Building Defect Prediction Models in Practice. Software, IEEE, 2005. 22: p. 23-29. 42. Eusuff, M. and K. Lansey, Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. 2001. 1-8. 43. FARSHIDPOUR, S. and F. Keynia. Using Artificial Bee Colony Algorithm for MLP Training on Software Defect Prediction. 2012. 44. Alshehri, Y.A., et al. Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them. in SoutheastCon 2018. 2018. 45. Malhotra, R. and A. Jain, Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality. Journal of Information Processing Systems, 2012. 8. 46. Moradi, M., et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Reproduction in domestic animals = Zuchthygiene, 2020. 47. Catal, C. and B. Diri, A systematic review of software fault prediction studies. Expert Systems with Applications, 2009. 36: p. 7346-7354. 48. Gittens, M., Y. Kim, and D. Godwin, The vital few versus the trivial many: Examining the Pareto principle for software. Vol. 1. 2005. 179-185 Vol. 2. 49. Gao, K. and T. Khoshgoftaar, Count Models for Software Quality Estimation. 2008. 50. Wahono, R., N. Suryana, and S. Ahmad, Neural Network Parameter Optimization Based on Genetic Algorithm for Software Defect Prediction. Advanced Science Letters, 2014. 20: p. 1951-1955. 51. Yang, X., et al., Deep Learning for Just-in-Time Defect Prediction. 2015. 17-26. 52. Kumudha, P. and R. Venkatesan, Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction. The Scientific World Journal, 2016. 2016: p. 1-20. 53. Rong, X., F. Li, and Z. Cui, A model for software defect prediction using support vector machine based on CBA. International Journal of Intelligent Systems Technologies and Applications, 2016. 15: p. 19. 54. Rathore, S. and S. Kumar, A decision tree logic based recommendation system to select software fault prediction techniques. Computing, 2016. 99. 55. Bowes, D., T. Hall, and J. Petrić, Software defect prediction: do different classifiers find the same defects? Software Quality Journal, 2017. 56. Chen, X., et al., MULTI: Multi-objective effort-aware just-in-time software defect prediction. Information and Software Technology, 2018. 93: p. 1-13. 57. C, M., SOFTWARE DEFECT PREDICTION USING DEEP BELIEF NETWORK WITH L1-REGULARIZATION BASED OPTIMIZATION. International Journal of Advanced Research in Computer Science, 2018. 9: p. 864-870. 58. Dam, H.K., et al., Lessons learned from using a deep tree-based model for software defect prediction in practice, in Proceedings of the 16th International Conference on Mining Software Repositories. 2019, IEEE Press: Montreal, Quebec, Canada. p. 46–57. 59. Dam, H., et al., A deep tree-based model for software defect prediction. 2018. 60. Mori, T. and N. Uchihira, Balancing the trade-off between accuracy and interpretability in software defect prediction. Empirical Software Engineering, 2019. 24: p. 779-825. 61. Ahmed, M., et al., A modified Fuzzy C- Mean algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging, 2002. 21: p. 193-9. 62. Keerthi, S.S. and C.-J. Lin, Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput., 2003. 15(7): p. 1667–1689. 63. Zhang, Q., et al., A survey on deep learning for big data. Information Fusion, 2018. 42: p. 146-157. 64. Menditto, A., M. Patriarca, and B. Magnusson, Understanding the meaning of accuracy, trueness and precision. Accreditation and Quality Assurance, 2007. 12: p. 45-47. 65. Köhler, R., The International Vocabulary of Metrology, 3rd Edition: Basic and General Concepts and Associated Terms. Why? How? 2010. p. 233-238. 1 A01 امیراحمد نیری امیراحمد نیری C. امیراحمد نیری 01 eng 202605 2026 C. zagros 06 10.22034/zagros.2020.0203.0607 https://rcsj.ir/user/articles/115 Farzanegan Pub mEDRA 01 01 115 zagros Publisher IR 01 115 JB 24 1 4 01 202605 7 10 01 [1] D. Datla, X. Chen, T. Tsou, S. Raghunandan, S. Hasan, J. Reed, C. Dietrich, T. Bose, B. Fette, J. Kim (2012), Wireless distributed computing: a survey of research challenges, IEEE Commun. Mag. 50 (1), 144–152, doi: 10.1109/MCOM. 2012.6122545 . [2] J. Yick, B. Mukherjee, D. Ghosal (2008), Wireless sensor network survey, Comput. Netw. 52 (12), 2292–2330. [3] E. Fasolo, M. Rossi, J. Widmer, M. Zorzi (2007), In-network aggregation techniques for wireless sensor networks: a survey, IEEE Wirel. Commun. 14 (2), 70–87, doi: 10.1109/MWC.2007.358967. [4] Y. Jin, P. Kulkarni, S. Gormus, M. Sooriyabandara (2012), Content centric and load-balancing aware dynamic data aggregation in multihop wireless networks, in:Proceedings of the IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). [5] D.S.J. De Couto, D. Aguayo, J. Bicket, R. Morris (2003), A high-throughput path metric for multi-hop wireless routing, in: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom ’03), ACM, New York, NY, USA, pp. 134–146. [6] T. Winter, P. Thubert, etal (2012), Rpl: Ipv6 Routing Protocol for Low-power and Lossy Networks, (rfc 6550), http://www.ietf.org/rfc/rfc6550.txt. [7] Y. Wu, Z. Mao, S. Fahmy, N. Shroff (2010), Constructing maximum-lifetime data-gathering forests in sensor networks, IEEE/ACM Trans. Netw. 18 (5), 1571–1584, doi: 10.1109/TNET.2010.2045896. [8] N. Tsiftes, A. Dunkels (2011), A database in every sensor, in: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys ’11), ACM, New York, NY, USA, pp. 316–332, doi: 10.1145/2070942.2070974. [9] S. Nath (2009), Energy efficient sensor data logging with amnesic flash storage, in: Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN ’09), IEEE Computer Society, Washington, DC, USA, pp. 157–168. 1 A01 فریدون شمس علیئی، لیلا حیدری، محمود نشاطی فریدون شمس علیئی، لیلا حیدری، محمود نشاطی C. فریدون شمس علیئی، لیلا حیدری، محمود نشاطی 01 eng 202605 2026 C. zagros