Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Survey on WBAN Methodology and its Challenges


Affiliations
1 MIT World Peace University, Pune, Maharashtra, India
     

   Subscribe/Renew Journal


It has been observed from the last decade that IoT has gained the market. The main enablers of IoT technology being the wireless communication, cloud computing and embedded system has gained attraction in many different areas. The major sector for IoT applications had been military sector, aerospace industry, manufacturing sector, home and automation and not at a neglected one that is the healthcare. Healthcare sector is the place where major requirement of IoT is there. Wireless Body Sensor Network (WBSN) innovations are viewed as one of pulling in look into regions in software engineering. At the point when joined with the medicinal services application, it gives high worth innovation of exhaustive medicinal services checking arrangement in extraordinary circumstances including high height or hazardous situation empowering the ground controller to screen remote pilots or seismic tremor unfortunate casualties progressively by a mix of remote sensors and sensor systems. In previous decades, cell phones have steadily become irreplaceable electronic items in individuals’ day by day life. A cell phone has an assortment of implicit sensors, for example, Wi-Fi, GPS, amplifier, magnetometer, and inertial sensors. This paper focuses on WBAN methodology and its challenges. The 3 layer WBAN schematic tells about the WBAN to be thought for in three different sections viz. intra BAN, extra BAN and storage and analysis.

Keywords

Body Sensor Node, IoT, WBAN, WBSN.
Subscription Login to verify subscription
User
Notifications
Font Size


  • D. Dias, and J. P. S. Cunha, “Wearable health devices—Vital sign monitoring, systems and technologies,” Sensors, vol. 18, no. 8, 2414, MDPI, 2018. doi: 10.3390/s18082414.
  • X. Tian, M. Zhang, and J. S. Ho, “Robust and high-efficiency wireless body area networks with spoof surface plasmons on clothing,” 2019 IEEE/MTT-S International Microwave Symposium (IMS), Boston, MA, USA, 2-7 June 2019.
  • Q. Wang, Q. Guan, B. Ma, and J. Liu, “Direct-sequence ultrasonic wideband technology for intra-body communications,” IEEE Communications Letters, vol. 23, no. 10, pp. 1744-1747, October 2019.
  • C. Kissi, M. Särestöniemi, T. Kumpuniemi, M. Sonkki, S. Myllymäki, Mohd. N. Srifi, and C. Pomalaza-Raez, “Directive low-band UWB antenna for in-body medical communications,” IEEE Access, pp. 149026-149038, 2019. doi: 10.1109/ACCESS.2019.2947057.
  • V. S. Lakshmi, V. Nithya, K. Sripriya, C. Preethi, and K. Logeshwari, “Prediction of diabetes patient stage using ontology based machine learning system,” 2019 IEEE International Conference on Systems Computation Automation and Networking (ICSCAN), Pondicherry, India, 29-30 March 2019.
  • S. Shen, J. Qian, D. Cheng, K. Yang, and G. Zhang, “A sum-utility maximization approach for fairness resource allocation in wireless powered body area networks,” IEEE Access, pp. 20014-20022, 2019. doi: 10.1109/ACCESS.2019.2897576.
  • S. R. Zahran, M. A. Abdalla, and A. H. Gaafar, “New thin wide-band bracelet-like antenna with low SAR for on-arm WBAN applications,” IET Microwaves, Antennas and Propagation, vol. 13, no. 8, March 2019. doi: 10.1049/iet-map.2018.5801.
  • G. Lee, B. Garner, and Y. Li, “Development of a human body phantom model for wireless body area network applications,” IEEE Special Section on WBAN, 2019.
  • Y. Wang, J. Zhang, F. Peng, and S. Wu, “A glasses frame antenna for the applications in internet of things,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8911-8918, October 2019.
  • D. R. Seshadri, R. T. Li, J. E. Voos, J. R. Rowbottom, C. M. Alfes, C. A. Zorman, and C. K. Drummond, “Wearable sensors for monitoring the internal and external workload of the athlete,” npj Digital Medicine, vol. 2, article no. 71, Scripps Research Translational Institute, 29 July 2019.
  • D. R. Seshadri, R. T. Li, J. E. Voos, J. R. Rowbottom, C. M. Alfes, C. A. Zorman, and C. K. Drummond, “Wearable sensors for monitoring the physiological and biochemical profile of the athlete,” npj Digital Medicine, vol. 2, no. 1, article no. 72, Scripps Research Translational Institute, 22 July 2019.
  • A. Kamišali´c, I. Fister, Jr., Md. Turkanovi´c, and S. Karakatiˇc, “Sensors and functionalities of non-invasive wrist-wearable devices: A review,” Sensors, vol. 18, no. 6, 1714, MDPI, 2018. doi: 10.3390/s18061714.
  • Z. Ullah, I. Ahmed, F. A. Khan, Md. Asif, Md. Nawaz, T. Ali, Md. Khalid, “Energy-efficient harvested-aware clustering and cooperative routing protocol for WBAN (E-HARP),” IEEE Access, vol. 7, pp. 100036-100050, 23 July 2019. doi: 10.1109/ACCESS.2019.2930652.
  • A. Ali, K. Inoue, A. Shalaby, Md. S. Sayed, and S. M. Ahmed, “Efficient autoencoder-based human body communication transceiver for WBAN,” IEEE Access, vol. 7, pp. 117196-117205, 21 August 2019. doi: 10.1109/ACCESS.2019.2936796.
  • Md. A. A. Khan, and Md. A. Kabir, “Comparison among short range wireless networks: Bluetooth, Zigbee, & Wi-Fi,” Journal of Science and Technology, vol. 11, no. 1, January 2016.
  • E. Ezhilarasan, and M. Dinakaran, “A review on mobile technologies: 3G, 4G and 5G,” 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), Tindivanam, India, 3-4 February 2017.
  • Md. M. Hassan, Md. Z. Uddin, A. Mohamed, and A. Almogren, “A robust human activity recognition system using smartphone sensors and deep learning,” Future Generation Computer Systems, vol. 81, pp. 307-313, Elsevier, 2018.
  • C. Auffray, R. Balling, I. Barrosso, L. Bencze, M. Benson, J. Bergeron, … and C. Bock, “Making sense of big data in health research: Towards an EU action plan,” US National Library of Medicine National Institute of Health, 8-71, 23 June 2016. Available: http://dx.doi.org/10.1186/s13073-016-0323-y
  • P. Dawson, R. Gailis, and A. Meehan, “Detecting disease outbreaks using a combined Bayesian network and particle filter approach,” 2015. Available: http://izt.ciens.ucv.ve/ecologia/Archivos/ECO_POB%202015/EC0PO6_2015/Dawson%20et%20al%202015_II.pdf
  • S. Sodagari, B. Bozorgchami, and H. Aghvami, “Technologies and challenges for cognitive radio enabled medical wireless body area networks,” IEEE Access, vol. 6, pp. 29567-29586, 2018. doi: 10.1109/ACCESS.2018.2843259.

Abstract Views: 296

PDF Views: 0




  • Survey on WBAN Methodology and its Challenges

Abstract Views: 296  |  PDF Views: 0

Authors

Deepali Pankaj Javale
MIT World Peace University, Pune, Maharashtra, India
Sharmishta Suhas Desai
MIT World Peace University, Pune, Maharashtra, India

Abstract


It has been observed from the last decade that IoT has gained the market. The main enablers of IoT technology being the wireless communication, cloud computing and embedded system has gained attraction in many different areas. The major sector for IoT applications had been military sector, aerospace industry, manufacturing sector, home and automation and not at a neglected one that is the healthcare. Healthcare sector is the place where major requirement of IoT is there. Wireless Body Sensor Network (WBSN) innovations are viewed as one of pulling in look into regions in software engineering. At the point when joined with the medicinal services application, it gives high worth innovation of exhaustive medicinal services checking arrangement in extraordinary circumstances including high height or hazardous situation empowering the ground controller to screen remote pilots or seismic tremor unfortunate casualties progressively by a mix of remote sensors and sensor systems. In previous decades, cell phones have steadily become irreplaceable electronic items in individuals’ day by day life. A cell phone has an assortment of implicit sensors, for example, Wi-Fi, GPS, amplifier, magnetometer, and inertial sensors. This paper focuses on WBAN methodology and its challenges. The 3 layer WBAN schematic tells about the WBAN to be thought for in three different sections viz. intra BAN, extra BAN and storage and analysis.

Keywords


Body Sensor Node, IoT, WBAN, WBSN.

References