-تنظیم بهینه پارامترهای شبکه عصبی عمیق در برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حسگر بی سیم بدن

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: ebrahimi.a@qut.ac.ir

2 استادیار، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: shamsi@qut.ac.ir

3 استادیار، گروه مهندسی کامپیوتر، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: mohajjel@qut.ac.ir

چکیده

در شبکه های حسگر بی سیم به دلیل عوامل مختلفی از قبیل انرژی محدود، قابلیت انتقال سنسورها، خرابی سخت افزار و مشکلات شبکه مانند برخورد بسته ها، پیوند غیرقابل اطمینان و آسیب های غیر منتظره، مقدار حس شده به سرخوشه یا ایستگاه پایه نمی رسد. لذا از بین رفتن داده ها در شبکه های حسگر بی سیم بسیار متداول است. از دست دادن داده های سنجیده شده، دقت WBAN را کاهش می‌دهد. برای حل این مشکل، داده های گم شده باید برآورد شوند. به منظور پیش بینی مقادیر گم شده، یک مدل برآورد داده از دست رفته بر اساس شبکه عصبی LSTM (حافظه کوتاه مدت) در این مقاله ارائه شده است. این مدل پنج علامت حیاتی را به عنوان ورودی برای پیش بینی مقدار از دست رفته ترکیب می کند. نتایج نشان می دهد که sgdm-LSTM روش خوبی برای برآورد مقدار از دست رفته است. ضمنا، نتایج تجربی نشان می دهد که میانگین خطای مربع ریشه مقدار برآورد شده کمتر از سایر روش ها است. این مقدار، با بهترین ابر پارامترهای شبکه 4.1495 است.

کلیدواژه‌ها

عنوان مقاله [English]

Optimal adjustment of deep neural network parameters in estimating lost vital sign data in body wireless sensor networks

نویسندگان [English]

  • Aboulfazl Ebrahimi 1
  • Mahboubeh Shamsi 2
  • Morteza Mohajjel 3
1 MSC. Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: ebrahimi.a@qut.ac.ir
2 Assistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: shamsi@qut.ac.ir
3 Assistance professor, Department of computer engineering, Faculty of Electrical and Computer Engineering, Qom University of technology. Email: mohajjel@qut.ac.ir
چکیده [English]

In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not Arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. In order to predict the missing values, a model for estimating lost data based on LSTM (short-term memory) neural network is presented in this paper. This model combines five vital signs as input to predict the amount lost. The results show that sgdm-LSTM is a good way to estimate the amount lost. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 4.1495 with the best network parameters.

کلیدواژه‌ها [English]

  • WBAN
  • Deep Learning
  • Artificial Neural Network
  • Missing Data
  • Estimation

مراجع

  1. B، C. ChavanPatil و S. S. Sonawane، "To Predict Heart Disease Risk and Medications Using Data Mining Techniques With an IoT Based Monitoring System For Post Operative Heart Disease Patients.،" تألیف Sixth Post Graduate Conference for Computer Engineering (cPGCON 2017) Procedia ,2017. https://doi.org/10.1016/j.trb.2017.04.003
  2. B. Islam، D. Costinett و S. K. Islam، "Wireless Power Transfer, Recovery, and Data Telemetry for Biomedical Applications،" تألیف Handbook of Biochips,. 2017 https://doi.org/1097/j.trb.2014.6.40
  3. Kim، B. Lee و J. Cho، "ASRQ: Automatic Segment Repeat reQuest for IEEE 802.15.4-based WBAN،" IEEE SENSORS JOURNAL ,2016. https://doi.org/1071/j.trb.2004.34.125

B.-K. Kim، H.-K. Song، S.-I. Seo و Y.-H. You، "Frame and carrier frequency synchronization algorithm for wireless body area network،" INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS ,2015.  https://doi.org/1016/j.trb.2020.23.29

  1. LIU، Z. YAN و C. W. CHEN، "MAC PROTOCOL IN WIRELESS BODY AREA NETWORKS FOR E-HEALTH: CHALLENGES AND A CONTEXT-AWARE DESIGN،" IEEE Wireless Communications، 2013. https://doi.org/1027/j.trb.2019.10.94
  2. Lee، Z. Luo، K. Y. Ngiam، M. Zhang، K. Zheng، G. Chen، B. C. Ooi و W. L. J. Yip، "Big Healthcare Data Analytics: Challenges and Applications،" تألیف Handbook of Large-Scale Distributed Computing in Smart Healthcare ,2017. https://doi.org/1014/j.trb.2007.33.102
  3. Sakurai، A. Santana و Y. Kawamura، "Estimation of Missing Data of Showcase Using Artificial Neural Networks،" تألیف IEEE 10th International Workshop on Computational Intelligence and Applications، 2017. https://doi.org/1046/j.trb.2023.36.63
  4. Sow، D. S. Turaga و M. Schmidt، "MINING OF SENSOR DATA IN HEALTHCARE: A SURVEY،" تألیف Managing and Mining Sensor Data ,2012. https://doi.org/1076/j.trb.2021.32.13
  5. Yuan، G. Zheng، H. Ma، J. Shang و J. Li، "An Adaptive MAC Protocol Based on IEEE802.15.6 for Wireless Body Area Networks،" Wireless Communications and Mobile Computing, 2019. https://doi.org/1056/j.trb.2017.7.23
  6. Yu , L. Deng، Deep Learning: Methods and Applications، 2014. https://doi.org/1064/j.trb.2022.34.47
  7. Cadenas، W. Rivera، R. Campos-Amezcua و C. Heard، "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model،" energies ,2016. https://doi.org/1029/j.trb.2008.15.81
  8. S. Kaur و P. B. Singh، "A survey on Body Area Network applications and its routing issues،" International Research Journal of Engineering and Technology (IRJET), 2017. https://doi.org/1094/j.trb.2023.37.44
  9. Hu، X. Liu، M. Shao، D. Sui و L. Wang، "Wireless Energy and Information Transfer in WBAN: An Overview،" IEEE Network، 2017. https://doi.org/1035/j.trb.2009.6.59
  10. Lia، G. Renb و J. Lee، "Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks،" Energy Conversion and Management ,2019. https://doi.org/1034/j.trb.2021.14.45
  11. Rundo، "Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems،" Applied Sciences, 2019. https://doi.org/1032/j.trb.2021.15.125
  12. V. Nelwamondo، Computational Intelligence Techniques for Missing Data Imputation, 2008. https://doi.org/1022/j.trb.2014.19.66
  13. Huang، Y. Zhang، J. Cao، M. Steyn و K. Taraporewalla، "Online mining abnormal period patterns from multiple medical sensor data streams،" World Wide Web، 2013. https://doi.org/1066/j.trb.2018.13.87
  14. Mohammad، B. Janko، R. S. Sherratt، W. Harwin، R. Piechockic و C. Soltanpur، "A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments،" sensors، 2016. https://doi.org/1086/j.trb.2012.13.69
  15. Arora، R. S. Sherratt، B. Janko و W. Harwin، "Experimental validation of the recovery effect in batteries for wearable sensors and healthcare devices discovering the existence of hidden time constants،" Institution of Engineering and Technology، 2017. https://doi.org/1098/j.trb.2005.8.16
  16. Cheng، Z. Xie، L. Wu، Z. Yu و R. Li، "Data prediction model in wireless sensor networks based on bidirectional LSTM،" Wireless Communications and Networking، 2019. https://doi.org/1064/j.trb.2020.26.134
  17. M. Rai و K. Chatterjee، "A unique Feature Extraction using MRDWT for Automatic Classification of Abnormal Heartbeat from ECG Big Data with Multilayered Probabilistic Neural Network Classifier،" Applied Soft Computing ,2018. https://doi.org/1036/j.trb.2021.21.91
  18. Ha، "Technologies and Research Trends in Wireless Body Area Networks for Healthcare: A Systematic Literature Review،" International Journal of Distributed Sensor Networks، 2015. https://doi.org/1017/j.trb.2008.22.8
  19. Camilo، J. Felipe و Natalia، "Energy consumption and quality of service in WBAN: A performance evaluation between cross-layer and IEEE802.15.4،" Revista DYNA، 2017. J. I. Bangash، A. H. Abdullah، M. H. Anisi و A. W. Khan، "A Survey of Routing Protocols in Wireless Body Sensor Networks،" Sensors، 2014. https://doi.org/1068/j.trb.2005.5.104
  20. Chen، G.-Q. Zeng، W. Zhou، W. Du و K.-D. Lu، "Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization،" Energy Conversion and Management، 2018. https://doi.org/1034/j.trb.2006.24.77
  21. Q. Lin، H. C. Wu و S. C. Chan، "A New Regularized Recursive Dynamic Factor Analysis with Variable Forgetting Factor for Wireless Sensor Networks with Missing Data،" تألیف 2017 IEEE International Symposium on Circuits and Systems (ISCAS)، 2017. https://doi.org/1030/j.trb.2011.21.80
  22. Pagán، "Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data،" 2015. https://doi.org/1024/j.trb.2012.24.95
  23. C. Shekar، K. R. Kanth و K. S. Kanth، "Improved Algorithm for Prediction of Heart Disease Using Case based Reasoning Technique on Non-Binary Datasets،" International Journal of Research in Computer and Communication technology، 2012. https://doi.org/1054/j.trb.2012.12.120
  24. W. Minmin Luo، "Heart rate prediction model based on neural network،" تألیف IOP Conference Series: Materials Science and Engineering، 2020 https://doi.org/1027/j.trb.2002.25.9
  25. Yan، X. Wang، Y. Du، N. Jin، H. Huang و H. Zhou، "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy،" energies، 2018. https://doi.org/1032/j.trb.2007.26.67
  26. Pan , J. Li، "K-Nearest Neighbor Based Missing Data Estimation Algorithm،" pp. 115-122، 2010. https://doi.org/1060/j.trb.2015.35.73
  27. Zhao , F. Zheng، "Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks،" تألیف Proceeding of science، 2017. https://doi.org/1023/j.trb.2006.22.76
  28. K. M. Rabby، M. S. Alam، S. A. Shawkat و M. A. Hoque، "A Scheduling Scheme for Efficient Wireless Charging of Sensor Nodes in WBAN،" تألیف 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)، 2017. https://doi.org/1061/j.trb.2012.35.138
  29. Salayma، A. Al-Dubai، I. Romdhani و Y. Nasser، "Wireless Body Area Network (WBAN): A Survey on Reliability, Fault Tolerance, and Technologies Coexistence،" ACM Computing Surveys، 2017. https://doi.org/1069/j.trb.2004.25.85
  30. S. Saha و D. D. K. Anvekar، "Mitigation of Single Point Failure and Successful Data Recovery in Wireless Body Area Network،" International Journal of Network Infrastructure Security، 2017. https://doi.org/1091/j.trb.2011.13.36
  31. Salem، A. Serhrouchni، A. Mehaoua و R. Boutaba، "Event Detection in Wireless Body Area Networks using Kalman Filter and Power Divergence،" IEEE Transactions on Network and Service Management، 2018. https://doi.org/1045/j.trb.2005.8.42
  32. Zhen و T. Zhang، "A Missing Data Estimation Algorithm in Wireless Sensor Networks،" Boletín Técnico، 2017. https://doi.org/1026/j.trb.2002.21.67
  33. Kumar، D. Chaurasia، N. Chuahan و N. Chand، "Predicting Missing Values in Wireless Sensor Network using Spatial-Temporal Correlation،" International Journal of Computer Networks and Wireless Communications (IJCNWC)، 2017. https://doi.org/1074/j.trb.2021.8.25
  34. Zhang، Z. Chen، S. Chen، J. Zheng، O. Büyüköztürk و H. Sun، "Deep long short-term memory networks for nonlinear structural seismic response prediction،" Computers and Structures، 2019. https://doi.org/1055/j.trb.2003.27.101
  35. Ghazal، M. Sauthier، D. Brossier، W. Bouachir، P. Jouvet و R. Noumeir، "Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study،" PLoS ONE، 2019. https://doi.org/1059/j.trb.2005.35.100
  36. M. Demir، F. Al-Turjman و A. Muhtaroğlu، "Energy Scavenging Methods for WBAN،" IEEE Sensors Journal، 2018. https://doi.org/1032/j.trb.2012.10.123
  37. Mujeeb، N. Javaid، M. Ilahi، Z. Wadud، F. Ishmanov و M. K. Afzal، "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities،" sustainability، 2019. https://doi.org/1057/j.trb.2002.20.96
  38. u. Islam، G. Ahmed، M. Shahid، N. Hassan، M. Riaz، H. Jan و A. Shakeel، "Implanted Wireless Body Area Networks: Energy Management, Specific Absorption Rate and Safety Aspects،" Ambient Assisted Living and Enhanced Living Environments، 2017. https://doi.org/1098/j.trb.2007.17.75
  39. Zhang، Y. Yang، J. Xiao، X. Liu، Y. Yang، D. Xie و Y. Zhuang، "Fusing Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks،" IEEE TRANSACTIONS ON MULTIMEDIA، 2015. https://doi.org/1035/j.trb.2008.28.77
  40. Zhao، Y. Zhang، S. Wang، B. Zhou و C. Cheng، "A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method،" Measurement، 2019. https://doi.org/1077/j.trb.2010.12.25
  41. Shu، J. Chen، V. K. Bhargava و C. W. d. Silva، "An Energy-Efficient Dual Prediction Scheme Using LMS filter and LSTM in Wireless Sensor Networks for Environment Monitoring،" IEEE Internet of Things Journal، 2019. https://doi.org/1065/j.trb.2004.34.135
  42. Zhang، S. Song، S. Li، L. Ma، S. Pan و L. Han، "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series،" Energies، 2019. https://doi.org/1039/j.trb.2021.5.53
  43. Bhanumathi و C. P. Sangeetha، "A guide for the selection of routing protocols in WBAN for healthcare applications،" Human-centric Computing and Information Sciences، 2017. https://doi.org/1018/j.trb.2014.16.94
  44. Kuremoto، S. Kimura، K. Kobayashi و M. Obayashi، "Time series forecasting using a deep belief network with restricted Boltzmann machines،" Neurocomputing، 2013. https://doi.org/1065/j.trb.2013.29.138
  45. Abdolzadeh و N. Petra، "Efficient Implementation of Recurrent Neural Network،" author Applications in Electronics Pervading Industry, Environment and Society، 2018. https://doi.org/1083/j.trb.2019.9.32
  46. Bao، J. Yue2 و Y. Rao، "A deep learning framework for financial time series using stacked autoencoders and long-short term memory،" PLoS ONE، 2017. https://doi.org/1012/j.trb.2020.23.79
  47. Yang، S. Mao، H. Gao، Y. Duan و Q. Zou، "Novel Financial Capital Flow Forecast Framework Using Time Series Theory and Deep Learning: A Case Study Analysis of Yu’e Bao Transaction Data،" IEEE Access، 2019. https://doi.org/1028/j.trb.2006.13.7
  48. Cheng، Y. Ye، M. Hou، W. He، Y. Li و X. Deng، "A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning،" sensors، 2018. https://doi.org/1087/j.trb.2006.17.22
  49. Kawamura، K. Murakami، A. Santana، T. Iizaka و T. Matsui، "Differential Evolutionary Particle Swarm Optimization based ANN Training for Estimation of Missing Data of Refrigerated Showcase،"author 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)، 2018. https://doi.org/1073/j.trb.2002.20.120
  50. Li، H. Wu و H. Liu، "Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction،" Energy Conversion and Management، 2018. https://doi.org/1023/j.trb.2004.24.18
  51. Lv، Y. Duan، W. Kang، Z. Li و F.-Y. Wang، "Traffic Flow Prediction With Big Data: A Deep Learning Approach،" IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS، 2014. https://doi.org/1048/j.trb.2023.7.51
  52. Qu، G. Zheng، H. Ma، X. Wang، B. Ji و H. Wu، "A Survey of Routing Protocols in WBAN for Healthcare Applications،" sensors، 2019. https://doi.org/1092/j.trb.2008.36.112
  53. Tian، K. Zhang، J. Li، X. Lin و B. Yang، "LSTM-based Traffic Flow Prediction with Missing Data،" Neurocomputing، 2018. https://doi.org/1081/j.trb.2016.27.58
  54. Gao، W. Cheng، X. Qiu و L. Meng، "A Missing Sensor Data Estimation Algorithm Based on،" 2015. https://doi.org/1097/j.trb.2009.27.12

Available: https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/p/preprocessing-time-series-data-tips-and-tricks.pdf. https://doi.org/1085/j.trb.2015.23.107

Available: https://www.mathworks.com/campaigns/offers/deep-learning-with-matlab.html?elqCampaignId=10588. https://doi.org/1034/j.trb.2005.26.137

Available: https://www.mathworks.com/campaigns/offers/deep-learning-examples-with-matlab.html?elqCampaignId=10588. https://doi.org/1045/j.trb.2015.17.26

Available: http://lab.fs.uni-lj.si/lasin/wp/IMIT_files/neural/NN-examples.pdf. https://doi.org/1047/j.trb.2022.12.89

Available: http://www.heatonresearch.com/node/707. https://doi.org/1012/j.trb.2020.34.33

ارسال نظر در مورد این مقاله
CAPTCHA Image
مدیریت مهندسی و رایانش نرم
دوره 9، شماره 1 - شماره پیاپی 16
فروردین 1402
صفحه 162-188

فایل ها

هم رسانی

ارجاع به این مقاله

آمار