Abstract
A central issue in Wireless Body Sensor Networks (WBSNs) is the large amount of measurement data for monitoring vital parameters, which need to be continuously measured, immediately processed and timely transmitted. This requires a big storage space and computing effort leading to a high-power consumption. Reducing the amount of transmitted data contributes significantly to an extension of the sensor operation time. In this contribution, we focus exactly at this aspect. We propose a data aggregation method based on Artificial Neural Networks (ANN) combining multiple physiological signals, which are the ElectroCardioGram (ECG), ElectroMyoGram (EMG) and Blood Pressure (BP), in one signal before transmission. The simulation and implementation results reveal a reduction of energy consumption to 87.32 %, ensuring a high accuracy level (80.53 %) and a relatively execution time (48.47 ms).
Zusammenfassung
Ein zentrales Thema in Wireless Body Sensor Networks (WBSNs) ist die große Menge an Messdaten zur Überwachung von Vitalparametern, die kontinuierlich gemessen, sofort verarbeitet und zeitnah übertragen werden müssen. Dies erfordert einen großen Speicherplatz und Rechenaufwand, der zu einem hohen Stromverbrauch führt. Die Reduzierung der übertragenen Datenmenge trägt wesentlich zu einer Verlängerung der Sensorbetriebszeit bei. In diesem Beitrag konzentrieren wir uns genau auf diesen Aspekt. Wir schlagen eine Datenaggregationsmethode vor, die auf künstlichen neuronalen Netzen (ANN) basiert und mehrere physiologische Signale kombiniert, die ElectroCardioGram (EKG), ElectroMyoGram (EMG) und Blutdruck (BP), in einem Signal vor der Übertragung. Die Simulations- und Umsetzungsergebnisse zeigen eine Reduzierung des Energieverbrauchs auf 87,32 %, was eine hohe Genauigkeit (80,53 %) und eine relativ hohe Ausführung gewährleistet Zeit (48,47 ms).
About the authors
Mbarka Belhaj Mohamed is PhD student in electrical engineering at National School of Engineers Gabes, Tunisia. She received the Master degree in communications (in 2015), and from the High School of Engineering in Electronics and Communications (ENET’com). Their master project presented and published at the 2016 IEEE Global Communications Conference in Washington DC. She is member of the Laboratory of Technologies for Smart Systems (LT2S) in the digital research center of Sfax (CRNS). In her research, she focuses on wireless sensor networks; healthcare applications; medical signal processing; decision making; network security; sensors and energy harvesting. She is author in a lot of international conferences. In 2020, she had the best paper award in the international multi-conference on systems, signals and devices (SSD). She has a very rich experience in higher education: In 2017, she works as a temporary teacher at ESPin Sfax; and in 2018 at ESIP Gafsa, (course: network and security). Then, she works as a contractual teacher in telecommunication at ISSAT Kasserine; and at SUPtech Sousse, in 2019 and 2020 respectively. She was a supervisor more than 7 end of studies project in fields of telecommunication, network security, signal preprocessing and smart grid. These experiences enrich their knowledge in various technological domain.
Amel Meddeb-Makhlouf is currently a Post-Doctoral Fellow at the High School of Engineering in Electronics and Communications (ENET’com), Sfax, Tunisia. She received the engineering degree (in 2001), the Master degree in communications (in 2003), and the Ph. D. degree (2010) from the Engineering School of Communications (SUP’COM, Tunisia). From September 2001 to August 2004, she worked as a project chief of the certification unit in NDCA (National Digital Certification Authority), the root certification authority in Tunisia, where she participates to the establishment of the Tunisian public key infrastructure. She also collaborates in the security audit projects.From September 2004 to September 2010, she worked as a teacher assistant in telecommunications in the Engineering School of Communications (SUP’COM, TUNISIA), where she teaches security courses and supervised Engineer projects. Since September 2010, she work as an assistant professor in the Engineering School of Electronics and Telecommunications of Sfax (ENET’com), where she supervised more than 40 projects. She is a member of NTS’COM Laboratory in ENET’com. He was a supervisor of more than 20 master projects and 9 PH. D thesis in fields of security of cloud computing, security of body sensor networks, security of 5G networks and the security of aeronautic networks. Since September 2018, she is responsible of the security branch in ENET’com and a head of the committee of research masters.She published 4 chapters and co-authored more than 40 papers that have been published in international journals and refereed conferences.Her research interests are in the area of network security with special emphasis on security of vehicular networks, security of cloud networks, authentication protocols and security of Body Sensor networks.
Ahmed Fakhfakh is professor for Engineering-of-Electronic-and-Communication-Systems at ENET’com Sfax, Tunisia. He has obtained his engineering degree in electrical engineering from ENIS in 1997, his DEA and PhD thesis in electronics both from Bordeaux university respectively in 1998 and 2002. In 2009, he has obtained the HDR diploma in electrical engineering from ENIS. From 2002 to 2016, he was member of the Laboratory of Electronics and Information Technologies (LETI) in ENIS. In 2012, he is the director at ENET’com. Since 2016, he is member of the Laboratory of Technologies for Smart Systems (LT2S) in the Digital research center of Sfax (CRNS) and head of the research team “Design and implementation of communicating systems”. He was a supervisor of 20 PH. D thesis in fields of smart grid, vehicular communications and wireless sensor network applications. He published more than 25 papers that have been published in international journals and refereed conferences.
Olfa Kanoun is professor for measurement and sensor technology since 2007 at TU Chemnitz, Germany. She graduated in electrical engineering at the Technische Universitat Munchen from in 1996, where she specialized in the field of electronics. Her PhD at the University of the Bundeswehr in Munich was awarded in 2001 by the Commission of Professors in Metrology (AHMT e. V.) in Germany. In 2015 she was awarded by the Tunisian ministry of social affairs for her scientific excellence and outstanding achievements. In her research she focuses on sensors, measurement systems and measurement methods. Since 2001 she is developing new sensors and measurement solutions based on impedance spectroscopy in the fields of battery diagnosis, bio-impedance spectroscopy, inductive sensors, capacitive sensors, conductivity sensors and material testing. She has a deep expertize in the field of energy harvesting and energy transmission and develops since many years successfully flexible nanocomposite sensors for force, temperature and humidity measurements.As senior IEEE member, she volunteers for the Instrumentation and Measurement Society and for IEEE. In 2004 she founded an IEEE IM Chapter and in 2014 she initiated a student branch at TU Chemnitz. She serves as co-chair of the Technical Committee on nanotechnology in instrumentation and measurement (TC 34). In 2001 she was co-founder of the international multi-conference on systems, signals and devices (SSD) and in 2008 she initiated the annual International Workshop on Impedance Spectroscopy (IWIS). She is author or co-author of 7 books, 52 papers in international journals with peer review, 110 papers in proceedings of international conferences and 6 journal special issues. She is member of the editorial board of Technisches Messen (De Gruyter) and associate editor of the journal on Digital Signals and Smart Systems (IJDSSS, Inderscience).
Acknowledgment
This research is a collaboration with the National School of Engineering of Gabes, the Digital Research Center of Sfax (CRNS), Tunisia and Chemnitz University of Technology, Germany. The authors would like to thank DAAD for the support of the cooperation. They draw up many thanks to the European exchange program for supporting their work.
References
1. https://www.afro.who.int/health-topics/health-information-and-knowledge-management, 30 april 2019.Search in Google Scholar
2. K. Al-Aubidy, A. Derbas and A. Al-Mutairi, Real-time patient health monitoring and alarming using wireless-sensor-network, SSD 2016, pp. 416–423, 2016.10.1109/SSD.2016.7473672Search in Google Scholar
3. I. Mahmoud, I. Chihi and A. Abdelkrim, Adaptive control design for human handwriting process based on electromyography signals, Complex. 2020, 5142870:1–5142870:14, 2020.10.1155/2020/5142870Search in Google Scholar
4. M. Hessinger, E. Christmann, R. Werthschützky and M. Kupnik, User interaction measurements of an exoskeleton using EMG and joint torque, tm – Technisches Messen, vol. 85, no. 7–8, 2018, pp. 487–495. https://doi.org/10.1515/teme-2017-0133, 2018.10.1515/teme-2017-0133Search in Google Scholar
5. P.Gope and T.Hwang, BSN-Care: a secure iot-based modern healthcare system using body sensor network, IEEE, Sensors Journal, vol. 16, 2015, 1-1. 10.1109/JSEN.2015.2502401.Search in Google Scholar
6. S. G. Mavinkattimath, R. Khanai and D. A. Torse, A survey on secured wireless body sensor networks, International Conference on Communication and Signal Processing, April 4–6, 2019.10.1109/ICCSP.2019.8698032Search in Google Scholar
7. A. Alkhayyat, A. A. Thabit, F. A. Al-Mayali and Q. H. Abbasi, WBSN in iot health-based application: toward delay and energy consumption minimization, Journal of Sensors, vol. 2019, Article ID 2508452, 14 pages, 2019. https://doi.org/10.1155/2019/2508452, 2019.10.1155/2019/2508452Search in Google Scholar
8. A. Schütze and N. Helwig, Sensors, instrumentation and measurement science for “Industrie 4.0”, tm – Technisches Messen, vol. 84, no. 5, 2017, pp. 310–319. https://doi.org/10.1515/teme-2016-0047, 2017.10.1515/teme-2016-0047Search in Google Scholar
9. R. H. Schmitt and C. Voigtmann, Sensor information as a service – Component of the networked production, tm – Technisches Messen, vol. 84, no. 5, 2017, pp. 296–309. https://doi.org/10.1515/teme-2017-0006, 2017.10.5194/jsss-7-389-2018Search in Google Scholar
10. S. Pushpalatha and K. S. Shivaprakasha, Energy-efficient communication using data aggregation and data compression techniques in wireless sensor networks: a survey, Advances in Communication, Signal Processing, VLSI, and Embedded Systems, pp. 161–179, DOI: 10.1007/978-981-15-0626-0-14, 01 December 2019.Search in Google Scholar
11. S. A. Dehkordi, K. Farajzadeh, J. Rezazadeh, et al., A survey on data aggregation techniques in IoT sensor networks, Wireless Netw, vol. 26, 2020, pp. 1243–1263. https://doi.org/10.1007/s11276-019-02142-z, 2020.10.1007/s11276-019-02142-zSearch in Google Scholar
12. N. Bradai, E. Charfi, L. C. Fourati and L. Kamoun, Priority consideration in inter-wban data scheduling and aggregation for monitoring systems, Trans Emerging Tel Tech. vol. 27, 2016, pp. 589–600. DOI: 10.1002/ett.2995.Search in Google Scholar
13. S. Khriji, R. Cheour, M. Goetz, D. El Houssaini, I. Kammoun and O. Kanoun, Redundancy elimination for data aggregation in wireless sensor networks, 15th International Multi-Conference on Systems, Signals Devices (SSD), DOI: 10.1109/SSD.2018.8570459, 2018.Search in Google Scholar
14. S. Boubiche, D. E. Boubiche and A. Bilami, Big data challenges and data aggregation strategies in wireless sensor networks, Special Section On Real-Time Edge Analytics For Big DHomero Toral-Cruzata In Internet Of Things, https://doi.10.1109/ACCESS.2018.2821445, 2018.Search in Google Scholar
15. N. Jan and S. Khan, Energy-efficient source location privacy protection for network lifetime maximization against local eavesdropper in wireless sensor network (EeSP), Trans Emerging Tel Tech, https://doi.org/10.1002/ett.3703, 2019.10.1002/ett.3703Search in Google Scholar
16. S. Ben Othman, A. A. Bahattab, A. Trad and H. Youssef, Lightweight and confidential data aggregation in healthcare wireless sensor networks, Trans Emerging Tel Tech, vol. 27, 2016, pp. 576–588. DOI: 10.1002/ett.2993.Search in Google Scholar
17. J. Azar, C. Habib, R. Darazi, A. Makhoul and J. Demerjian, Using adaptive sampling and dwt lifting scheme for efficient data reduction in wireless body sensor networks, International Conference on Wireless and Mobile Computing, Networking and Communications, Limassol, Cyprus, ffhal-02182803f, 2018.10.1109/WiMOB.2018.8589093Search in Google Scholar
18. A. S. Jaber and A. K. Idriss, Energy-saving multisensor data sampling and fusion with decision-making for monitoring health risk using wbsns, Journal of Software: Practice and Experience, https://doi.org/10.1002/spe.2904, 2020.10.1002/spe.2904Search in Google Scholar
19. I. Ullah and H. Y. Youn, Efficient data aggregation with node clustering and extreme learning machine for WSN, The Journal of Supercomputing, vol. 76, 2020, pp. 10009–10035. https://doi.org/10.1007/s11227-020-03236-8.10.1007/s11227-020-03236-8Search in Google Scholar
20. J. A. Jahanshahi, H. Danyali and M. S. Helfroush, Compressive sensing based the multi-channel ecg reconstruction in wireless body sensor networks, Biomedical Signal Processing and Control, vol. 61, 2020, 102047, ISSN 1746-8094. https://doi.org/10.1016/j.bspc.2020.102047, 2020.10.1016/j.bspc.2020.102047Search in Google Scholar
21. A. Ghosh, A. Raha and A. Mukherjee, Energy-efficient IoT-health monitoring system using approximate computing, Internet of Things, vol. 9, 2020. https://doi.org/10.1016/j.iot.2020.100166.10.1016/j.iot.2020.100166Search in Google Scholar
22. K. Dhakal, A. Alsadoon, P. W. C. Prasad, R. S. Ali, L. Pham and A. Elchouemi, A novel solution for a wireless body sensor network: telehealth elderly people monitoring, Egyptian Informatics Journal, https://doi.org/10.1016/j.eij.2019.11.004, 2019.10.1016/j.eij.2019.11.004Search in Google Scholar
23. S. Kunal Kishan and D. Mayank, Privacy preserving data aggregation in wireless body sensor network, Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics Cloud in Computational Vision Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3734802 or http://dx.doi.org/10.2139/ssrn.3734802, November 21, 2020.10.2139/ssrn.3734802Search in Google Scholar
24. M. Cicioğlu and A. Çalhan, Energy efficiency solutions for IEEE 802.15.6 based wireless body sensor networks, Wireless Pers Commun (2021), https://doi.org/10.1007/s11277-021-08292-8, 2021.10.1007/s11277-021-08292-8Search in Google Scholar
25. A. S. Jaber and A. K. Idrees, Adaptive rate energy-saving data collecting technique for health monitoring in wireless body sensor networks, International Journal of Communication Systems, https://doi.org/10.1002/dac.4589, 21 August 2020.Search in Google Scholar
26. PhysioNet. Available online: http://www.physionet.org/physiobank/database/mimicdb/ (accessed on 15 December 2019).Search in Google Scholar
27. M. B. Mohamed, A. Meddeb-Makhlouf and A. Fakhfakh, Intrusion cancellation for anomaly detection in healthcare applications, 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC), Tangier, Morocco, 2019, pp. 313–318, doi: 10.1109/IWCMC.2019.8766592, 2019.Search in Google Scholar
28. N. Khadka, General machine learning practices using Python. Bachelor’s Thesis, Information Technology, Oulu University of Applied Sciences, Spring, 2019.Search in Google Scholar
29. A. Taspinar, Machine learning with signal processing techniques, ataspinar.com, April 4, 2018.Search in Google Scholar
30. M. B. Mohamed, A. M. Makhlouf and A. Fakhfakh, Correlation for efficient anomaly detection in medical environment, 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC), Limassol, Cyprus, 2018, pp. 548–553, doi: 10.1109/IWCMC.2018.8450283, 2018.Search in Google Scholar
31. M. B. Mohamed, A. Meddeb-Makhlouf, A. Fakhfakh and O. Kanoun, Intrusion detection based on correlation of multiple health signals in wbsn, 2020 17th International Multi-Conference on Systems, Signals Devices (SSD), Monastir, Tunisia, 2020, pp. 372–377, doi: 10.1109/SSD49366.2020.9364227, 2020.Search in Google Scholar
32. J. Shi, D. Zuo, Z. Zhang and D. Luo, Sensor-based activity recognition independent of device placement and orientation, Trans Emerging Tel Tech, https://doi.org/10.1002/ett.3823, 2019.10.1002/ett.3823Search in Google Scholar
33. V. Vladuta, et al., A proposed solution for analyzing the behavior of a wsn using a secured neural network approach, International Symposium on Electronics and Telecommunications (ISETC), DOI: 10.1109/isetc.2018.858385, 2018.Search in Google Scholar
34. M. Massaoudi, I. Chihi, L. Sidhom, M. Trabelsi, S. S. Refaat and F. S. Oueslati, A novel approach based deep RNN using hybrid NARX-LSTM model for solar power forecasting, CoRR abs/1910.10064, 2019.Search in Google Scholar
35. M. Massaoudi, I. Chihi, L. Sidhom, M. Trabelsi, S. S. Refaat, H. Abu-Rub and F. S. Oueslati, An effective hybrid NARX-LSTM model for point and interval PV power forecasting, IEEE Access, vol. 9, 2019, pp. 36571–36588.10.1109/ACCESS.2021.3062776Search in Google Scholar
36. T. Batiha, M. Prauzek and P. Krömer, Intrusion detection in wireless sensor networks by an ensemble of artificial neural networks, Czarnowski I., Howlett R., Jain L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol. 142. Springer, Singapore, https://doi.org/10.1007/978-981-13-8311-328, 2019.Search in Google Scholar
37. M. Massaoudi, I. Chihi, L. Sidhom, M. Trabelsi, S. S. Refaat and F. S. Oueslati, PV power forecasting using weighted features for enhanced ensemble method, CoRR abs/1910.09404, 2019.Search in Google Scholar
38. G. Maier, F. Pfaff, F. Becker, C. Pieper, R. Gruna, B. Noack, H. Kruggel-Emden, T. Längle, U. D. Hanebeck, S. Wirtz, V. Scherer and J. Beyerer, Motion-based material characterization in sensor-based sorting, tm – Technisches Messen, vol. 85, no. 3, 2018, pp. 202–210. https://doi.org/10.1515/teme-2017-0063, 2018.10.1515/teme-2017-0063Search in Google Scholar
39. M. Rao, T. Newe and I. Grout, AES implementation on Xilinx FPGA suitable for FPGA based WBSN, 2015 9th International Conference on Sensing Technology (ICST), Auckland, New Zealand, 2015, pp. 773–778, doi: 10.1109/ICSensT.2015.7438501, 2015.Search in Google Scholar
40. Y. Yang, D. He, N. Kumar and S. Zeadally, Compact hardware implementation of a sha-3 core for wireless body sensor networks, IEEE Access, vol. 6, pp. 40128–40136, 2018, doi: 10.1109/ACCESS.2018.2855408, 2018.Search in Google Scholar
41. V. K. Sarker, T. N. Gia, H. Tenhunen and T. Westerlund, Lightweight security algorithms for resource-constrained iot-based sensor nodes, ICC 2020–2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1–7, doi: 10.1109/ICC40277.2020.9149359, 2020.Search in Google Scholar
42. A. N. Abdulraheem and B. M. Nema, Secure iot model based on PRESENT lightweight modified and chaotic key generator, 2020 1st. Information Technology To Enhance e-learning and Other Application (IT-ELA), Baghdad, Iraq, 2020, pp. 12–18, doi: 10.1109/IT-ELA50150.2020.9253079, 2020.Search in Google Scholar
43. Y. Zhang, H. Chen and T. Gao, WSN Routing Algorithm Based on Energy Approximation Strategy, Communications and Networking, 2018.10.1007/978-3-319-78139-6_1Search in Google Scholar
44. M. B. Mohamed, A. Meddeb-Makhlouf, A. Fakhfakh and O. Kanoun, Wireless body sensor networks with enhanced reliability by data aggregation based on machine learning algorithms, Smart Sensors, Measurement and Instrumentation, ISSN 2194-8402 ISSN 2194-8410 (electronic), ISBN 978-3-030-71224-2 ISBN 978-3-030-71225-9 (eBook), https://doi.org/10.1007/978-3-030-71225-9, pp. 67–81, 2021.10.1007/978-3-030-71225-9Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
