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Telecommunication Systems

, Volume 58, Issue 2, pp 111–124 | Cite as

HEH-BMAC: Hybrid polling MAC protocol for WBANs operated by human energy harvesting

  • Ernesto Ibarra
  • Angelos Antonopoulos
  • Elli Kartsakli
  • Christos Verikoukis
Article

Abstract

This paper introduces human energy harvesting medium access control (MAC) protocol (HEH-BMAC), a hybrid polling MAC suitable for wireless body area networks powered by human energy harvesting. The proposed protocol combines two different medium access methods, namely polling (ID-polling) and probabilistic contention access, to adapt its operation to the different energy and state (active/inactive) changes that the network nodes may experience due to their random nature and the time variation of the energy harvesting sources. HEH-BMAC exploits the packet inter-arrival time and the energy harvesting rate information of each node to implement an efficient access scheme with different priority levels. In addition, our protocol can be applied dynamically in realistic networks, since it is adaptive to the topology changes, allowing the insertion/removal of wireless sensor nodes. Extensive simulations have been conducted in order to evaluate the protocol performance and study the throughput and energy tradeoffs.

Keywords

Energy efficiency WBAN  Energy harvesting MAC 

Notes

Acknowledgments

This work has been funded by the research projects CO2Green (TEC2010-20823), WSN4QoL (PIAP-GA-2011-286047), ENIAC ARTEMOS (EUI2010-04252 and EUI2011-4349) and Grant SENACYT-IFARHU (270-2009-173).

References

  1. 1.
    \(802.15. 6^{{\rm TM}}\)-2012 IEEE Stds. (2012). Standard for local and metropolitan area networks: Part 15.6: Wireless Body Area Networks.Google Scholar
  2. 2.
    11073 ISO/IEEE Stds. (2008). Health informatics PoC medical device communication, Part 00101: Guide–guidelines for the use of RF wireless technology (pp. 25-27). Piscataway: IEEE.Google Scholar
  3. 3.
    Ameen, M. A., Ullah, N., Chowdhury, M., Islam, S., & Kwak, K. (2012). A power efficient MAC protocol for wireless body area networks. EURASIP Journal on Wireless Communications and Networking. doi: 10.1186/1687-1499-2012-33.
  4. 4.
    Balpande, S., Lande, S., & Rungta, S. (2009). Modeling of PZT based power harvester and power control approach for pervasive node BSN. In Proceedings of the 2009 IEEE International Advance Computing Conference (IACC), 6–7 March (pp. 327–332).Google Scholar
  5. 5.
    Boulis, A., & Tselishchev, Y. (2010). Contention vs. Polling: A study in body area networks MAC design. In Proceedings of the \(7{\rm th}\) International Conference on Body Area Networks (BodyNets), 10–12 Sep. (pp. 98–104).Google Scholar
  6. 6.
    Chen, B., & Pompili, D. (2011). Transmission of patient vital signs using wireless body area networks. Journal of Mobile Networks and Application, 16(6), 663–682.CrossRefGoogle Scholar
  7. 7.
    Chen, X., Xu, S., Yao, N., & Shi, Y. (2010). 1.6V nanogenerator for mechanical energy harvesting using PZT nanofibers. Journal of Nano letters, 10(6), 2133–2137.CrossRefGoogle Scholar
  8. 8.
    Dargie, W., Chao, X., & Denko, M. (2011). Modeling the energy cost of a fully operational wireless sensor network. Journal of Telecommunication Systems, 44(1–2), 3–15.Google Scholar
  9. 9.
    Drude, S. (2007). Requirements and application scenarios for body area networks. In Proceedings of the \(16^{{\rm th}}\) Mobile and Wireless Communications Summit IST, 1–5 July (pp. 1–5).Google Scholar
  10. 10.
    Eu, Z. A., & Tan, H.-P. (2012) Probabilistic polling for multi-hop energy harvesting wireless sensor networks. In Proceedings of the 2012 IEEE International Conference on Communications (ICC), 10–15 June (pp. 271–275).Google Scholar
  11. 11.
    Eu, Z. A., Tan, H.-P., & Seah, W. K. G. (2011). Design and performance analysis of MAC schemes for wireless sensor networks powered by ambient energy harvesting. Journal of Ad Hoc Networks, 9(3), 300–323.CrossRefGoogle Scholar
  12. 12.
    Fang, G., & Dutkiewicz, E. (2009). BodyMAC: Energy efficient TDMA-based MAC protocol for wireless body area networks. In Proceedings of the \(9{\rm th}\) International Symposium on Communications and Information Technology (ISCIT), 28–30 Sept. (pp. 1455–1459).Google Scholar
  13. 13.
    Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Journal of Telecommunication Systems. doi: 10.1007/s11235-011-9568-3.
  14. 14.
    Gopalan, S. A., & Park J.-T. (2010). Energy-efficient MAC protocols for wireless body area networks: survey. In Proceedings of the 2010 International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT), 18–20 Oct. (pp. 739–744).Google Scholar
  15. 15.
    Gregori, M., & Payaro, M. (2011). Efficient data transmission for energy harvesting node with battery capacity constraint. In Proceedings of the 2011 IEEE Global Communications Conference (GLOBECOM), 5–9 Dec. (pp. 1–6).Google Scholar
  16. 16.
    Hansen, B. J., Liu, Y., & Wang, Z. L. (2010). Hybrid nanogenerator for concurrently harvesting biomechanical and biochemical energy. Journal of American Chemical Society, 4(7), 3647–3652.Google Scholar
  17. 17.
    He, Y., Zhu, W., & Guan, L. (2011). Optimal resource allocation for pervasive health monitoring systems with body sensor networks. IEEE Journal of Transactions On Mobile Computing, 10(11), 1558–1575.CrossRefGoogle Scholar
  18. 18.
    Hoang, D., & Tan, Y. (2009). Thermal energy harvesting from human warmth for wireless body area network in medical healthcare system. In Proceedings of the 2009 International Conference on Power Electronics and Drive Systems (PEDS), 2–5 Nov. (pp. 1277–1282).Google Scholar
  19. 19.
    Khan, J., Yuce, M., & Karami, F. (2008). Performance evaluation of a wireless body area sensor network for remote patient monitoring. In Proceedings of the \(30{\rm th}\) International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBS), 20–25 Aug. (pp. 1266–1269).Google Scholar
  20. 20.
    Latré, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2011). A survey on wireless body area networks. Journal of Wireless Networks, 17(1), 1–18.CrossRefGoogle Scholar
  21. 21.
    Li, H., Chan, E., & Chen, G. (2010). AEETC—adaptive energy-efficient timing control in wireless networks with network coding. Journal of Telecommunication Systems, 45(4), 289–301.CrossRefGoogle Scholar
  22. 22.
    Lim, J. C., & Bleakley, C. J. (2011). Trading sensing coverage for an extended network lifetime. Journal of Telecommunication Systems. doi: 10.1007/s11235-011-9595-0.
  23. 23.
    Lossec, M., Multon, B., Ahmed, H. B., & Goupil, C. (2010). Thermoelectric generator placed on the human body: System modeling and energy conversion improvements. The European Physical Journal Applied Physics, 52(1), 11103(1)–11103(10).CrossRefGoogle Scholar
  24. 24.
    Marinkovic, S. J., Popovici, E., Spagnol, C., Faul, S., & Marnane, W. P. (2009). Energy-efficient low duty cycle MAC protocol for wireless body area network. IEEE Journal of Transactions on Information Technology in Biomedicine., 13(6), 915–925.CrossRefGoogle Scholar
  25. 25.
    Markys, C. (2010). State of art in human powering devices. Journal of Energy Harvesting, Resource document. In Proceedings of the \(1^{{\rm st}}\) Energy Harvesting Research Theme Workshop, 9 November. http://eh-network.org/events/workshop1/slides1.pdf. Accessed 12 July 2012
  26. 26.
    Otal, B., Alonso, L., & Verikoukis, C. (2009). Highly reliable energy saving MAC for wireless body sensor networks in healthcare systems. IEEE Journal on Selected Areas in Communications, 27(4), 553–565.CrossRefGoogle Scholar
  27. 27.
    Ozel, O., Tutuncuoglu, K., Yang, J., Ulukus, S., & Yener, A. (2011). Transmission with energy harvesting nodes in fading wireless channels: Optimal policies. IEEE journal on Selected Areas in Communication, 29(8), 1732–1743.CrossRefGoogle Scholar
  28. 28.
    Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In Proceedings of the 2004 ACM Conference on Embedded Networked Sensor Systems (ACM SenSys), 3–5 Nov. (pp. 95–107).Google Scholar
  29. 29.
    Rapoport, B. I., Kedsierski, J. T., & Sarpeeshkar, R. (2012). A glucose fuel cell for implantable brain-machine interfaces. Journal of Plos One, 7(6), 1–15.CrossRefGoogle Scholar
  30. 30.
    Renner, C., Jessen, J., & Tarau, V. (2009). Lifetime prediction for supercapacitor-powered wireless sensor nodes. In Proceedings of the \(8{\rm th}\) GI/ITG KuVS Fachgespräch “Drahtlose Sensornetze” (FGSN’09), 13 Aug. (pp. 55–58).Google Scholar
  31. 31.
    Rodoplu, V., & Meng, T. H. (2007). Bits-per-joule capacity of energy-limited wireless networks. In proceedings of IEEE Transactions on Wireless Communications, 6(3), 857–865.CrossRefGoogle Scholar
  32. 32.
    Seah, W. K. G., Eu, Z. A., & Tan, H.-P. (2009). Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)- survey and challenges. In Proceedings of the \(1{\rm st}\) International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), 17–20 May (pp. 1–5).Google Scholar
  33. 33.
    Seyedi, A., & Sikdar, B. (2010). Energy efficient transmission strategies for body sensor networks with energy harvesting. IEEE Journal of Transactions on Communications, 58(7), 2116–2126.CrossRefGoogle Scholar
  34. 34.
    Seyedi, A., & Sikdar, B. (2008). Modeling and analysis of energy harvesting nodes in body sensor networks. In Proceedings of the \(5{\rm th}\) International Workshop on Wearable and Implantable Body Sensor Networks (BSN), 1–3 June (pp. 175–178).Google Scholar
  35. 35.
    Simjee, F., & Chou, P.H. (2006). Everlast: long-life, supercapacitor-operated wireless sensor node. In Proceedings of the 2006 International Symposium on Low Power Electronics and Design (ISLPED), 4–6 Oct. (pp. 197–202).Google Scholar
  36. 36.
    Tachtatzis, C., Di Franco, F., Tracey, D., Timmons N., & Morrison, J. An energy analysis of IEEE 802.15.6 scheduled access modes. In Proceedings of the 2010 IEEE Global Communications Conference (GLOBECOM), 6–10 Dec. (pp. 1270–1275).Google Scholar
  37. 37.
    Timmons, N. F., & Scanlon, W. G. (2009). An adaptive energy efficient MAC protocol for the medical body area networks. In Proceedings of the \(1{\rm st}\) Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), 17–20 May (pp. 587–593).Google Scholar
  38. 38.
    Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the 2003 ACM Conference on Embedded Networked Sensor Systems (ACM SenSys), 5–7 Nov. (pp. 171–180).Google Scholar
  39. 39.
    Ventura, J., Chowdhury, K. (2011). Markov Modeling of Energy Harvesting Body Sensor Networks. In Proceedings of the \(21^{{\rm nd}}\) International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 11–14 Sept. (pp. 2168–2172).Google Scholar
  40. 40.
    Vullers, R. J. M., Schaijk, R. V., Visser, H. J., Penders, J., & Hoof, C. V. (2010). Energy harvesting for autonomous wireless sensor networks. IEEE Journal of Solid-State Circuits, 2(2), 29–38.CrossRefGoogle Scholar
  41. 41.
    Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the \(21{\rm th}\) IEEE International Conference on Computer Communications (INFOCOM), 23–27 June (pp. 1567–1576).Google Scholar
  42. 42.
    Yildiz, F. (2009). Potential ambient energy-harvesting sources and techniques. Journal of Technology Studies, 35(1), 40–48.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ernesto Ibarra
    • 1
  • Angelos Antonopoulos
    • 1
  • Elli Kartsakli
    • 2
  • Christos Verikoukis
    • 1
  1. 1.Telecommunications Technological Centre of Catalonia (CTTC)BarcelonaSpain
  2. 2.Signal Theory and Communications DepartmentTechnical University of Catalonia (UPC)BarcelonaSpain

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