Skip to main content
SpringerLink
Log in

Heterogeneous MAC duty-cycling for energy-efficient Internet of Things deployments

  • Research Article
  • Published:
Networking Science

Abstract

The Internet of Things (IoT) paradigm aims at connecting any object to the Internet (i.e. to the IP world). Due to the physical constraints (limited energy capacities) and deployment conditions (numerous autonomous devices scattered into an area) of such Things, power management and scalability are key issues in IoT deployments. While the problematics of the IP addressing have been successfully transposed to IoT networks, the dedicated IEEE 802.15.4 Medium Access Control standard lacks of scalability, provides insufficient energy-efficiency and thus fails to fulfill their needs. In this paper, we consider alternative MAC protocols, compatible with IoT specificities. These protocols realize energy gains by asynchronously alternating active and passive periods at the radio scale, thus allowing both energy-efficiency and scalability. For the time being, most real IoT deployments implement static and homogeneous duty-cycling (i.e. invariant and identical for each node in the network). Although preventing any node isolation, such method fails to address the dynamics of the network efficiently. We propose a strategy to enable heterogeneous MAC duty-cycle configurations among nodes in the network. We aim at granting each node a specific sleep-depth, according to criteria specific to the deployment (e.g. applicative criteria, location in the routing structure). To implement this idea, the nodes are divided into disjoint subsets, each of them standing for a given duty-cycle configuration and leading to a network performance managed at its best (e.g. energy consumption, loss-rate, delays). We detail to what extent our approach preserves network connectivity with coherent heterogeneous duty-cycling, thus reaching a compromise between energy consumption and reactivity. The presented experimental campaign was led over the IoT SensLAB testbed. It demonstrates that our solutions provide up to 61% energy saving, preserve the loss-rate below 10% and guarantee the connectivity of the network. They thus offer a better compromise between energy-efficiency and network performances than any homogeneous MAC configuration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Comput. Netw., vol. 38, no. 4, pp. 393–422, Mar. 2002.

    Article  Google Scholar 

  2. G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Netw., vol. 7, no. 3, pp. 537–568, May 2009.

    Article  Google Scholar 

  3. L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2787–2805, Oct. 2010.

    Article  MATH  Google Scholar 

  4. C. Cano, B. Bellalta, A. Sfairopoulou, and M. Oliver, “Low energy operation in WSNs: A survey of preamble sampling MAC protocols,” Comput. Netw., vol. 55, no. 15, pp. 3351–3363, Oct. 2011.

    Article  Google Scholar 

  5. A. Dunkels, B. Gronvall, and T. Voigt, “Contiki-A lightweight and flexible operating system for tiny networked sensors,” in Proc. 29th Annu. IEEE Int. Conf. Local Computer Networks. Washington DC: IEEE, 2004, pp. 455–462.

    Chapter  Google Scholar 

  6. A. Dunkels, F. Osterlind, N. Tsiftes, and Z. He, “Software-based on-line energy estimation for sensor nodes,” in Proc. 4th Workshop Embedded Networked Sensors (EmNets). New York: ACM, 2007, pp. 28–32.

    Chapter  Google Scholar 

  7. T. W. Hnat, V. Srinivasan, J. Lu, T. I. Sookoor, R. Dawson, J. Stankovic, and K. Whitehouse, “The hitchhiker’s guide to successful residential sensing deployments,” in Proc. ACM Conf. Embedded Networked Sensor Systems (SenSys). New York: ACM, 2011, pp 232–245.

    Google Scholar 

  8. Wireless Medium Access Control and Physical Layer Specifications for Low-rate Wireless Personal Area Networks, IEEE Standard 802.15.4-2009, 2009.

  9. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” IEEE/ACM Trans. Netw., vol. 11, no. 1, pp. 2–16, Feb. 2003.

    Article  Google Scholar 

  10. A. J. Jara, M. A. Zamora, and A. F. G. Skarmeta, “An internet of things-based personal device for diabetes therapy management in ambient assisted living (AAL),” Pers. Ubiquit. Comput., vol. 15, no. 4, pp. 431–440, Apr. 2011.

    Article  Google Scholar 

  11. A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava “Power management in energy harvesting sensor networks,” ACM Trans. Embed. Comput. Syst., vol. 6, no. 4, Article no. 32, Sept. 2007.

    Google Scholar 

  12. F. Khadar and T. Razafindralambo, “Performance evaluation of gradient routing strategies for wireless sensor networks,” in Proc. 8th Int. Conf. IFIP-TC 6 Networking. Berlin: Springer, 2009, pp 535–547.

    Google Scholar 

  13. R. Kuntz, A. Gallais, and T. Noël, “Medium access control facing the reality of WSN deployments,” ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 3, pp. 22–27, Jul. 2009.

    Article  Google Scholar 

  14. R. Kuntz, A. Gallais, and T. Noël, “From versatility to auto-adaptation of the medium access control in wireless sensor networks,” J. Parallel Distrib. Comput., vol. 71, no. 9, pp. 1236–1248, Sept. 2010.

    Article  Google Scholar 

  15. M. Buettner, G. V. Yee, E. Anderson, and R. Han, “X-MAC: A short preamble MAC protocol for duty-cycled wireless sensor networks,” in Proc. 4th ACM Int. Conf. Embedded Networked Sensor Systems (SenSys). New York: ACM, 2006, pp. 307–320.

    Google Scholar 

  16. D. Niyato, E. Hossain, and S. Camorlinga, “Remote patient monitoring service using heterogeneous wireless access networks: Architecture and optimization,” IEEE J. Sel. Areas Commun., vol. 27, no. 4, pp. 412–423, May 2009.

    Article  Google Scholar 

  17. J. Polastre, J. Hill, and D. Culler, “Versatile low power media access for wireless sensor networks,” in Proc. Int. Conf. Embedded Networked Sensor Systems (Sensys), New York: ACM, 2004, pp. 95–107.

    Chapter  Google Scholar 

  18. C. B. des Roziers, G. Chelius, T. Ducrocq, E. Fleury, A. Fraboulet, A. Gallais, N. Mitton, T. Noël, and J. Vandaele, “Using SensLAB as a first class scientific tool for large scale wireless sensor network experiments,” in Proc. 10th Int. Conf. IFIP-TC 6 Networking. Berlin: Springer, 2011, pp. 147–159.

    Google Scholar 

  19. G. Schiele, C. Becker, and K. Rothermel, “Energy-efficient cluster-based service discovery for ubiquitous computing,” in Proc. 11th Workshop ACM SIGOPS European Workshop. New York: ACM, Article no. 14, 2004.

    Google Scholar 

  20. F. Sivrikaya and B. Yener, “Time synchronization in sensor networks: A survey,” IEEE Network, vol. 18, no. 4, pp. 45–50, Jul.–Aug. 2004.

    Article  Google Scholar 

  21. T. Voigt, H. Ritter, and J. Schiller, “Utilizing solar power in wireless sensor networks,” in Proc. 28th Annu. IEEE Int. Conf. Local Computer Networks (LCN). Washington DC: IEEE, 2003, pp. 416–422.

    Google Scholar 

  22. T. Winter, P. Thubert, A. Brandt, J. Hui, R. Kelsey, P. Levis, K. Pister, R. Struik, J. P. Vasseur, and R. Alexander, “RFC 6550: RPL: IPv6 Routing Protocol for Low power and Lossy Networks,” IETF, Mar. 2012.

    Google Scholar 

  23. H. Yoo, M. Shim, and D. Kim, “Dynamic duty-cycle scheduling schemes for energy-harvesting wireless sensor networks,” IEEE Commun. Lett., vol 16, no. 2, pp. 202–204, Feb. 2012.

    Article  Google Scholar 

  24. M. Zimmerling, F. Ferrari, L. Mottola, T. Voigt, and L. Thiele, “pTunes: Runtime parameter adaptation for low-power MAC protocols,” in Proc. 11th Int. Conf. Information Processing in Sensor Networks (IPSN). New York: ACM, 2012, pp. 173–184.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICube laboratory (UMR CNRS 7357), University of Strasbourg, Strasbourg, France

    Julien Beaudaux, Antoine Gallais & Thomas Noël

Authors
  1. Julien Beaudaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antoine Gallais
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Noël
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julien Beaudaux.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Beaudaux, J., Gallais, A. & Noël, T. Heterogeneous MAC duty-cycling for energy-efficient Internet of Things deployments. Netw.Sci. 3, 54–62 (2013). https://doi.org/10.1007/s13119-013-0016-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13119-013-0016-4

Keywords

Search

Navigation