Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

Mehdi, Hamid; Zarrabi, Houman; Khadem Zadeh, Ahmad; Rahmani, AmirMasoud

doi:10.29252/mjee.14.3.2

Document Type : Review Article

Authors

¹ Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² ICT Research Center, Tehran, Iran

³ Computer engineering department, Science and Research Branch, Islamic Azad University, Tehran, Iran

https://doi.org/10.29252/mjee.14.3.2

Abstract

Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is network lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase network lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity.

Keywords

References

[1] P. Charith, P. Prem and C. Peter, “MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices,” in Processing of the 47th Hawaii International Conference on System Sciences (HICSS), Kona, Hawaii, USA, 2015.

[2] K. Paridel, E. Bainomugisha, Y. Vanrompay, Y. Berbers and W.D. Meuter, “Middleware for the Internet of Things, design goals and challenges,” Electronic Communication, vol. 28, 2010.

[3] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, “Internet of Things: A vision, architectural elements, and future directions,” Future Generation of Computer System, vol 29, No 7, pp. 1645–1660, 2013.

[4] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey” Computer Network, vol 54, No 15, pp. 2787–2805, 2010.

[5] D. Le-Phuoc, A. Polleres, M. Hauswirth, G. Tummarello, and C. Morbidoni, "Rapid prototyping of semantic mashups through semantic web pipes,” In proceeding of 18th International Conference on World Wide Web, pp. 581– 590, 2009.

[6] A. Dohr, R. Modre-Opsrian, M. Drobics, D. Hayn, and G. Schreier, "The Internet of Things for ambient assisted living," In proceeding of 7th International Conference of Information Technology, New Generation (ITNG), 2010, pp. 804–809,

[7] J. I. Bangash, A. H. Abdullah, M. H. Anisi, and A. W. Khan, “A survey of routing protocols in wireless body sensor networks,” Sensors, vol. 14, no. 1, pp. 1322–1357, 2014.

[8] N. Bradai, L. C. Fourati, and L. Kamoun, “WBAN data scheduling and aggregation under WBAN/WLAN healthcare network,” Ad Hoc Netw., vol. 25, Part A, pp. 251–262, 2015.

[9] Lee, Changmin, and Jaiyong Lee. "Harvesting and Energy aware Adaptive Sampling Algorithm for guaranteeing self-sustainability in Wireless Sensor Networks." Information Networking (ICOIN), 2017 International Conference on.IEEE, 2017.

[10] Yoon, Ikjune, et al. "Adaptive sensing and compression rate selection scheme for energy-harvesting wireless sensor networks." International Journal of Distributed Sensor Networks 13.6 (2017): 1550147717713627.

[11] Zhu, Xing, et al. "A self-adaptive data acquisition technique and its application in landslide monitoring." Workshop on World Landslide Forum.Springer, Cham, 2017.

[12] Lu, Ting, et al. "Distributed sampling rate allocation for data quality maximization in rechargeable sensor networks." Journal of Network and Computer Applications 80 (2017): 1-9.

[13] Silva, João Marco C., et al. "LiteSense: An adaptive sensing scheme for WSNs." Computers and Communications (ISCC), 2017 IEEE Symposium on.IEEE, 2017.

[14] Fathy, Yasmin, PayamBarnaghi, and Rahim Tafazolli. "An Adaptive Method for Data Reduction in the Internet of Things." Proceedings of IEEE 4th World Forum on Internet of Things.IEEE, 2018.

[15]Diwakaran, S., Perumal, B., & Devi, K. V. (2018).A cluster prediction model-based data collection for energy efficient wireless sensor network. The Journal of Supercomputing, 1-15.

[16]Amarlingam, M., Mishra, P. K., Rajalakshmi, P., Giluka, M. K., &Tamma, B. R. (2018, February). Energy efficient wireless sensor networks utilizing adaptive dictionary in compressed sensing. In Internet of Things (WF-IoT), 2018 IEEE 4th World Forum on (pp. 383-388). IEEE.

[17]Papatsimpa, C., &Linnartz, J. P. (2018). Energy efficient communication in smart building WSN running distributed hidden Markov chain presence detection algorithm. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT 2018): Smart Cites and Nations. Institute of Electrical and Electronics Engineers (IEEE).

[18]Harb, H., &Makhoul, A. (2017).Energy Efficient Sensor Data Collection Approach for Industrial Process Monitoring.IEEE Transactions on Industrial Informatics.

[19] G. K. Ragesh and K. Baskaran, “A survey on futuristic health care system: WBAN,” Procedia Eng., vol. 30, pp. 889–896, 2012.

[20] National Early Warning Score (NEWS), Royal College of Physicians, London, U.K., May 2015. [Online]. Available: http://www.rcplondon. ac.uk/resources/national-early-warning-score-news

[21] Rault, Tifenn, et al. "A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications." Pervasive and Mobile Computing 37 (2017): 23-44.

[22] S. Elghers, A. Makhoul, and D. Laiymani, “Local emergency detection approach for saving energy in wireless body sensor networks,” in Proc. IEEE 10th Int. Conf. Wireless Mobile Comput., Netw. Commun., Oct. 2014, pp. 585–591.

[23] Habib, Carol, et al. "Self-adaptive data collection and fusion for health monitoring based on body sensor networks." IEEE transactions on Industrial Informatics 12.6 (2016): 2342-2352.

[24] A. Makhoul, H. Harb, and D. Laiymani, “Residual energy-based adaptive data collection approach for periodic sensor networks,” Ad Hoc Netw., vol. 35, pp. 149–160, 2015

[25] Y. Yin, C. Zhang, and Y. Li, “A twostage data fusion model for wireless sensor networks,” Int. J. Sensor Netw., vol. 15, no. 3, pp. 163–170, 2014.

[26] G. Li and Y. Wang, “Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks,” EURASIP J. Wireless Commun. Netw., vol. 2013, no. 1, pp. 1–13, 2013.

[27] J. Yang, T. S. Rosing, and S. S. Tilak, “Leveraging application context for efficient sensing,” in Proc. IEEE 9th Int. Conf. Intell. Sensors, Sensor Netw. Inf. Process., 2014, pp. 1–6.

Majlesi Journal of Electrical Engineering

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

References

References

Volume 14, Issue 3 - Serial Number 3
September 2020
Pages 11-22

Self-Adaptive Sampling Rate to Improve Network Lifetime using Watchdog Sensor and Context Recognition in Wireless Body Sensor Networks

References

References

Volume 14, Issue 3 - Serial Number 3September 2020Pages 11-22

Volume 14, Issue 3 - Serial Number 3
September 2020
Pages 11-22