Skip to main content

Advertisement

SpringerLink
Log in

Adaptive listen for energy-efficient medium access control in wireless sensor networks

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Wireless Sensor Networks (WSN) have nodes that are small in size and are powered by small batteries having very limited amount of energy. In most applications of WSN, the nodes in the network remain inactive for long periods of time, and intermittently they become active on sensing any change in the environment. The data sensed by the different nodes are sent to the sink node. In contrast to other infrastructure-based wireless networks, higher throughput, lower latency and per-node fairness in WSN are imperative, but their importance is subdued when compared to energy consumption. In this work, we have regarded the amount of energy consumption in the nodes to be of primary concern, while throughput and latency in the network to be secondary. We have proposed a protocol for energy-efficient adaptive listen for medium access control in WSN. Our protocol adaptively changes the slot-time, which is the time of each slot in the contention window. This correspondingly changes the cycle-time, which is the sum of the listen-time and the sleep-time of the sensors, while keeping the duty-cycle, which is the ratio between the listen-time and the cycle-time, constant. Using simulation experiments, we evaluated the performance of the proposed protocol, compared with the popular Sensor Medium Access Control (SMAC) (Ye et al. IEEE/ACM Trans Netw 12(3):493–506, 39) protocol. The results we obtained show a prominent decrease in the energy consumption at the nodes in the proposed protocol over the existing SMAC protocol, at the cost of decreasing the throughput and increasing the latency in the network. Although such an observation is not perfectly what is ideally desired, given the very limited amount of energy with which the nodes in a WSN operate, we advocate that increasing the energy efficiency of the nodes, thereby increasing the network lifetime in WSN, is a more important concern compared to throughput and latency. Additionally, similar observations relating energy efficiency, network lifetime, throughput and latency exist in many other existing protocols, including the popular SMAC protocol (Ye et al. IEEE/ACM Trans Netw 12(3):493–506, 39).

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. As we use only the Centroid Method, in the interest of brevity, we do not explain the other defuzzification methods in this paper. Interested readers are referred to [31] and [28] for getting an understanding of how the other defuzzification methods work. Although we believe that using the other defuzzification methods will not substantially influence the conclusions that we have drawn from this work, in the future, we plan to empirically investigate it.

  2. In our simulation, we chose to have 10 nodes. Such a setup is aligned with the experimental results reported in most of the existing works as well. The increase in the number of nodes can lead to an increase in the levels of congestion. The number of nodes can be varied by implementing appropriate modifications in the simulation code and transmission range of the nodes. However, sensor nodes have very short transmission range compared to the nodes in other wireless networks and it is typical that only a few nodes that are not too distant from any particular node can exist in its transmission range.

References

  1. Ai J, Kong J, Turgut D (2004) An adaptive coordinated medium access control for wireless sensor networks. In Proceedings of the International Symposium on Computers and Communications, Vol. 1, July 2004, pp 214–219

  2. Ali M, Suleman T, Uzmi ZA (2005) MMAC: A mobility-adaptive, collision-free MAC protocol for wireless sensor networks. In Proceedings of the 24th IEEE International Performance Computing and Communications Conference, Phoenix, April, 2005, pp 401–407

  3. Bennett F, Clarke D, Evans JB, Hopper A, Jones A, Leask D (1997) Piconet: embedded mobile networking. IEEE Pers Commun Mag 4:8–15

    Article  Google Scholar 

  4. Bharghavan V, Demers A, Shenker S, Zhang L (1994) Macaw: A media access protocol for wireless LANs. Proceedings of the ACM SIGCOMM Conference, London, pp 212–225

  5. Biaz S, Barowski YD (2004) GANGS: an energy efficient MAC protocol for sensor networks. In Proceedings of the Annual Southeast Regional Conference, April 2004, pp 82–87.

  6. Chandrasekar R, Misra S, Obaidat MS (2008) A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks. Int J Commun Syst 21(10):1047–1073 (Wiley)

    Article  Google Scholar 

  7. Chandrasekar R, Obaidat MS, Misra S, Peña-Mora F (2008) A secure data-centric scheme for group-based routing in heterogeneous ad-hoc sensor networks and its simulation analysis. SIMULATION: Transactions of the Society for Modeling and Simulation International 84(2/3):131–146

    Google Scholar 

  8. Chatterjea S, van Hoesel LFW, Havinga PJM (2004) AI-LMAC: An adaptive, information-centric and lightweight MAC protocol for wireless sensor networks. In Proceedings of the Intelligent Sensors, Sensor Networks, and Information Processing Conference, pp 381–388, December 2004

  9. Dam TV, Langendoen K (2003) An adaptive energy-efficient MAC protocol for WSN. Proceedings of SenSys-03, Los Angeles, Nov. 5–7, 2003, pp 171, 180

  10. Dhurandher SK, Misra S, Obaidat MS, Khairwal S (2008) UWSim: an underwater sensor network simulator. SIMULATION: Transactions of the Society for Modeling and Simulation International 84(7):327–338

    Article  Google Scholar 

  11. Dhurandher SK, Misra S, Mittal H, Aggarwal A, Woungang I (2009) Ant colony optimization-based congestion control in ad-hoc wireless sensor networks. In Proceedings of the 7th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA-09), Rabat, Morocco, May 10–13, 2009.

  12. Dhurandher SK, Misra S, Obaidat MS, Gupta N (2009) An ant colony optimization approach for reputation and quality-of-service-based security in wireless sensor networks. Security and Communication Networks 2(2):215–224 (Wiley)

    Article  Google Scholar 

  13. Dhurandher SK, Misra S, Dhawan A, Tiwari A, Efficient solutions to various routing issues involved in mobile ad-hoc bio-sensor networks: applying appropriate motion trajectories. IET Communications Journal, U.K. 3(5):830–845

  14. Dhurandher SK, Misra S, Obaidat MS, Bansal V, Singh P, Punia V (2009) EEAODR: An energy-efficient on-demand routing protocol for wireless ad-hoc networks. International Journal of Communication Systems (Wiley), 22(7):789–817

    Google Scholar 

  15. Dhurandher SK, Obaidat MS, Misra S, Khairwal S, Efficient data acquisition in underwater wireless sensor ad-hoc networks. IEEE Wireless Communications. (Accepted)

  16. El-Hoiydi A, Decotignie J-D (2004) WiseMAC: An ultra low power MAC protocol for multi-hop wireless sensor networks. In Proceedings of the International Workshop on Algorithmic Aspects of Wireless Sensor Networks (Algosensors), pp 251–254, July 2004

  17. Gupta I, Riordan D, Sampalli S (2005) Cluster-head election using fuzzy logic for WSN. Proceedings of the 3rd Annual Communication Networks and Services Research Conference 2005, Halifax, pp 255–260

  18. Jurdak R, Lopes C, Baldi P (2004) A survey, classification and comparative analysis of medium access control protocols for ad hoc networks. IEEE Commun Surv Tutorials 6(1):2–16

    Article  Google Scholar 

  19. Kredo K II, Mohapatra P (2007) Medium access control in wireless sensor networks. Comput Netw 51(4):961–994

    Article  MATH  Google Scholar 

  20. LAN MAN Standards Committee of the IEEE Computer Society (1997) Wireless LAN medium access control (MAC) and physical layer (PHY) specification. IEEE, New York IEEE Std 802.11-1997 edition

    Google Scholar 

  21. Li G, Doss R, Energy-efficient medium access control in wireless sensor networks. In: Misra S, Woungang I, Misra SC (Eds) Guide to wireless sensor networks, Chapter 36. Springer-Verlag, London

  22. Lin P, Qiao C, Wang X (2004) Medium access control with a dynamic duty cycle for sensor networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Vol. 3, March 2004, pp 1534–1539.

  23. Mahlknecht S, Böck M (2004) CMSA-MPS: A minimum preamble sampling MAC protocol for low power wireless sensor networks. In Proceedings of the IEEE International Workshop on Factory Communication Systems, pp 73–80, September 2004

  24. Misra S, Abraham KI, Obaidat MS, Krishna PV (2009) LAID: a learning automata-based scheme for intrusion detection in wireless sensor networks. Security and Communication Networks 2(2):105–115 (Wiley)

    Article  Google Scholar 

  25. Misra S, Tiwari V, Obaidat MS (2009) LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE J Sel Areas Commun 27(4):466–479

    Article  Google Scholar 

  26. NS2 Simulator: http://www.isi.edu/nsnam/ns/

  27. Rajendran V, Obraczka K, Garcia-Luna-Aceves JJ (2003) Energy-efficient, collision-free medium access control for wireless sensor networks. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys), pp 181–192, November 2003

  28. Rajsekaran S, Vijayalakshmi Pai GA (2003) Neural networks, fuzzy logic and genetic algorithm, synthesis and application. Prentice Hall of India

  29. Ren Q, Lian Q (2005) Fuzzy logic-optimized secure media access control (FSMAC) protocol wireless sensor networks. In Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety

  30. Rhee I, Warrier A, Aia M, Min J (2005) Z-MAC: A hybrid MAC for wire-less sensor networks. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys), pp 90–101, November 2005

  31. Ross TJ (2005) Fuzzy logic with engineering applications. Wiley

  32. Singh S, Raghavendra CS (1998) PAMAS: power aware multi-access protocol with signalling for ad hoc networks. ACM Computer Communication Review 28(3):5–26

    Article  Google Scholar 

  33. Stemm M, Katz RH (1997) Measuring and reducing energy consumption of network interfaces in hand held devices. IEICE Trans Commun E80-B(8):1125–1131

    Google Scholar 

  34. van Dam T, Langendoen K (2003) An adaptive energy-efficient MAC protocol for wire-less sensor networks. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys), pp 171–180, November 2003

  35. van Hoesel LFW, Havinga PJM (2004) A TDMA-based MAC protocol for WSNs. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys), pp 303–304, November 2004

  36. van Hoesel, LFW, Havinga PJM (2004) A lightweight medium access protocol (LMAC) for wireless sensor networks: Reducing preamble transmissions and transceiver state switches. In Proceedings of the International Conference on Networked Sensing Systems (INSS), June 2004

  37. Wallace J, Pesch D, Rea S, Irvine J (2005) Fuzzy logic optimisation of MAC parameters and sleeping duty-cycles in wireless sensor networks. In Proceedings of the IEEE 62nd Vehicular Technology Conference, VTC-2005-Fall

  38. Xia F, Zhao W, Sun Y, Tian Y-C (2007) Fuzzy logic control based QoS management in wireless sensor/actuator networks. Sensors 7:3179–3191

    Article  Google Scholar 

  39. Ye W, Heidemann J, Estrin D (2004) Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Trans Netw 12(3):493–506

    Article  Google Scholar 

  40. Yusuf M, Haider T (2005) Energy-aware fuzzy routing for WSN. IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, Sept. 17–18 2005

  41. Zabin F, Misra S, Woungang I, Rashvand H, Ma N-W, Ali MA (2008) REEP: a data-centric, energy-efficient and reliable routing protocol for wireless sensor networks. IET Commun 2(8):995–1008 UK

    Article  Google Scholar 

  42. Zhao F, Guibas L (2004) Wireless sensor networks: an information processing approach. Morgan Kaufmann

Download references

Acknowledgement

The work of the first author was partly supported by a grant from the Department of Science & Technology, Government of India, Grant No. SR/FTP/ETA-36/08, which the author gratefully acknowledges.

Author information

Authors and Affiliations

  1. School of Information Technology, Indian Institute of Technology, Kharagpur, West Bengal, India

    Sudip Misra & Debashish Mohanta

  2. Department of Computer Science and Engineering, Indian School of Mines, Dhanbad, Jharkhand, India

    Debashish Mohanta

Authors
  1. Sudip Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debashish Mohanta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudip Misra.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Misra, S., Mohanta, D. Adaptive listen for energy-efficient medium access control in wireless sensor networks. Multimed Tools Appl 47, 121–145 (2010). https://doi.org/10.1007/s11042-009-0410-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-009-0410-9

Keywords

Search

Navigation