Skip to main content
SpringerLink
Log in

Reliability-Based Routing Algorithms for Energy-Aware Communication in Wireless Sensor Networks

  • Chapter
  • First Online:
Book cover Performance Models and Risk Management in Communications Systems

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 46))

  • 919 Accesses

Abstract

In this chapter, some novel routing algorithms and packet forwarding strategies are presented forWireless Sensor Networks (WSNs). The new algorithms provide energy balancing under reliability constraints, as opposed to traditional wireless routing protocols (e.g. PEDAP, Directed Diffusion and LEACH), which fail to provide guarantees for reliable communication while attempting to achieve energy balancing. As reliable packet transfer to the Base Station (BS) is instrumental in many applications when there are no redundancies in the observed signals collected by the sensors, these new protocols can be of better use in real systems targeting such applications. Reliability is defined in terms of keeping the packet loss ration under a predefined threshold. The novel contribution of this chapter lies in developing new routing and packet forwarding mechanisms which can achieve energy balancing and reliable communication in WSNs. In order to provide energy balancing under such reliability constraints, first a randomized packet forwarding mechanism is introduced termed as “random shortcut” protocol. The performance of the new protocol is optimized by using large deviation theory.

Secondly, new routing algorithms are developed which can select optimal paths to the BS ensuring minimum energy consumption and guarantee a given level of reliability at the same time. In this case, the wireless sensor network is modeled as a random graph, where optimal routes are found by the means of combinatorial optimization. The new algorithm OERA (Overall Energy Reliability Algorithm) selects a path which minimizes the overall energy needed to transfer a packet to the BS subject to a predefined reliability constraint, while algorithm BERA (Bottleneck Energy Reliability Algorithm) selects path over which the remaining energy of the bottleneck node (the one having the smallest energy) is maximized under the same constraint. It will be proven that these algorithms can find the optimal path in polynomial time and can be implemented in a distributed manner. The performance of the new methods compared to the traditional ones is also demonstrated by extensive numerical analysis and simulations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdi A, Tepedelenlioglu C, Member S, Member S, Kave M, Giannakis G (2001) On the estimation of the k parameter for the rice fading distribution. IEEE Commun Lett 5:92–94

    Article  Google Scholar 

  2. Abrach H, Bhatti S, Carlson J, Dai H, Rose J, Sheth A, Shucker B, Deng J, Han R (2003) Mantis: System support for multimodal networks of in-situ sensors. In: 2nd ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), San Diego, CA, USA, pp 50–59

    Google Scholar 

  3. Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Elsevier Ad Hoc Netw J 3:325–349. http://citeseerx.ist.psu.edu/viewdoc/summary? doi=10.1.1.85.3616

    Article  Google Scholar 

  4. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: A survey. Comput Netw 38:393–422

    Article  Google Scholar 

  5. Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: A survey. IEEE Wireless Commun 11(6):6–28. doi 10.1109/MWC.2004.1368893. http://dx.doi.org/10.1109/MWC.2004.1368893

    Article  Google Scholar 

  6. Arampatzis T, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and wireless sensor networks, pp 719–724. http://ieeexplore.ieee.org/xpls/abs all.jsp?arnumber=1467103

  7. Cauligi CA, Lindsey S, Raghavendra CS, Raghavendra CS Pegasis: Power-efficient gathering in sensor information systems, IEEE Aerospace Conf. Proc., 2002, vol. 3, 9–16, pp 1125–30

    Google Scholar 

  8. Chernoff H (1952) A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Ann Math Stat 23:493–509

    Article  MATH  MathSciNet  Google Scholar 

  9. Fournel N, Fraboulet A, Chelius G, Fleury E, Allard B, Brevet O (2007) Worldsens: From lab to sensor network application development and deployment. In: IPSN ’07: Proceedings of the 6th international conference on Information Processing in Sensor Networks. ACM, New York, NY, USA, pp 551–552. doi http://doi.acm.org/10.1145/1236360.1236435

  10. Haenggi M (2005) On routing in random Rayleigh fading networks. IEEE Trans Wireless Commun 4(4):1553–1562. Available at http://www.nd.edu/ mhaenggi/pubs/routing.pdf

    Article  Google Scholar 

  11. Heinzelman WR, Ch A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 4-7 Jan. 2000, vol.2, pp 10

    Google Scholar 

  12. Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, Silva F (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Netw 11(1):2–16. doi http://dx.doi.org/10.1109/TNET.2002.808417

    Article  Google Scholar 

  13. Kuorilehto M, Hännikäinen M, Hämäläinen TD (2005) A survey of application distribution in wireless sensor networks. EURASIP J Wirel Commun Netw 2005(5):774–788. doi: http://dx.doi.org/10.1155/WCN.2005.774

    Article  MATH  Google Scholar 

  14. Levendovszky J, Bojárszky A, Karlócai B, Oláh A (2008) Energy balancing by combinatorial optimization for wireless sensor networks. WTOC 7(2):27–32

    Google Scholar 

  15. Levendovszky J, Kiss G, Tran-Thanh L (2009) Energy balancing by combinatorial optimization for wireless sensor networks. In: Performance Modelling and Analysis of Heterogeneous Networks. River Publishers, Aalborg, Denmark, pp 169–182

    Google Scholar 

  16. Mani CS, Srivastava MB (2001) Energy efficient routing in wireless sensor networks. In: MILCOM Proceedings on Communications for Network-Centric Operations: Creating the Information Force, Washington, D.C., USA, pp 357–361

    Google Scholar 

  17. N. Narasimha Datta and K. Gopinath (2005) Indian Institute of Science: A survey of routing algorithms for wireless sensor networks. J. Indian Inst. Sci., Nov-Dec. 2006, 86, 569–598

    Google Scholar 

  18. Pereira PR, Grilo A, Rocha F, Nunes MS, Casaca A, Chaudet C, Almström P, Johansson M (2007) End-to-end reliability in wireless sensor networks: Survey and research challenges. In: Pereira PR (ed) EuroFGI workshop on IP QoS and Traffic Control. EuroFGI 2007 Workshop on IP QoS and Traffic Control, Lisbon, Portugal

    Google Scholar 

  19. Perkins C, Royer E (1997) Ad-hoc on-demand distance vector routing. In: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, LA, USA, pp 90–100

    Google Scholar 

  20. Polastre J, Szewczyk R, Culler D (2005) Telos: enabling ultra-low power wireless research. In: IPSN’05: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, IEEE, Piscataway, NJ, USA. http://portal.acm.org/citation.cfm?id=1147685.1147744

  21. Ramamurthy H, Prabhu BS, Gadh R, Madni AM (2007) Wireless industrial monitoring and control using a smart sensor platform. IEEE Sensors J 7(5):611–618. doi 10.1109/JSEN.2007.894135. URL http://dx.doi.org/10.1109/JSEN.2007.894135

    Article  Google Scholar 

  22. Rappaport T (2001) Wireless communications: principles and practice. Prentice Hall PTR, Upper Saddle River, NJ

    Google Scholar 

  23. Rmer K (2004) Tracking real-world phenomena with smart dust. In: EWSN 2004, Springer, Berlin, Germany, pp 28–43

    Google Scholar 

  24. Schwiebert L, Gupta SK, Weinmann J (2001) Research challenges in wireless networks of biomedical sensors. In: MobiCom’01: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking. ACM, New York, NY, USA, pp 151–165. doi http://doi.acm.org/10.1145/381677.381692

  25. Sohrabi K, Gao J, Ailawadhi V, Pottie GJ (2000) Protocols for self-organization of a wireless sensor network. IEEE Pers Commun 7:16–27

    Article  Google Scholar 

  26. Srivastava M, Muntz R, Potkonjak M (2001) Smart kindergarten: Sensor-based wireless networks for smart developmental problem-solving environments. In: MobiCom’01: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, ACM, New York, NY, USA, pp. 132–138. doi http://doi.acm.org/10.1145/381677.381690

  27. Stüber GL (2001) Principles of mobile communication, 2nd edn. Kluwer, Norwell, MA

    Google Scholar 

  28. Tan HO, Körpeoğlu I (2003) Power efficient data gathering and aggregation in wireless sensor networks. ACM SIGMOD Rec 32(4):66–71. doi http://doi.acm.org/10.1145/959060.959072

    Article  Google Scholar 

  29. Tran-Thanh L, Levendovszky J (2009) A novel reliability based routing protocol for power aware communications in wireless sensor networks. In: WCNC’09: Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference, IEEE, Piscataway, NJ, USA, pp 2308–2313

    Google Scholar 

  30. Vieira MAM, da Silva Jr, DC, Coelho Jr, CJN, da Mata JM (2003) Survey on wireless sensor network devices. In: Proceedings of the 9th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’03), Lisbon, Portugal

    Google Scholar 

  31. Wyne S, Santos T, Tufvesson F, Molisch AF (2007) Channel measurements of an indoor office scenario for wireless sensor applications. In: GLOBECOM, 2007, Washington, D.C., USA, pp 3831–3836. IEEE. http://dblp.uni-trier.de/db/conf/globecom/globecom2007.html

  32. Ning Xu. A survey of Sensor Network Applications. IEEE. Communications Magazine, pp 102–114, 2002. Proceedings of the Electronics, Robotics and Automotive Mechanics Conference, Washington, DC, USA

    Google Scholar 

  33. Xu Y, Heidemann J, Estrin D (2001) Geography-informed energy conservation for ad hoc routing. In: MobiCom’01: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, ACM, New York, NY, USA, pp 70–84. doi http://doi.acm.org/10.1145/381677.381685

  34. Ye NF, Chen FYA (2001) A scalable solution to minimum cost forwarding in large sensor, 10th International Conference on Computer Communications and Networks Proceedings, 2001, Scottsdale, Arizona USA, pp 304–309

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Telecommunications, Budapest University of Technology and Economics, H-1117 Magyar tud. krt. 2, Budapest, Hungary

    Janos Levendovszky & Long Tran-Thanh

  2. Faculty of Information Technology, Peter Pazmany Catholic University, H-1083 Práter u. 50/A, Budapest, Hungary

    Andras Olah & Gergely Treplan

Authors
  1. Janos Levendovszky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andras Olah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gergely Treplan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Long Tran-Thanh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Janos Levendovszky , Andras Olah , Gergely Treplan or Long Tran-Thanh .

Editor information

Editors and Affiliations

  1. The University of Warwick, Warwick Business School, Coventry, CV4 7AL, United Kingdom

    Nalân Gülpınar

  2. , Department of Computing, Imperial College London, Queen's Gate 180, London, SW7 2BZ, United Kingdom

    Peter Harrison

  3. , Department of Computing, Imperial College London, Queen's Gate 180, London, SW7 2BZ, United Kingdom

    Berç Rüstem

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Levendovszky, J., Olah, A., Treplan, G., Tran-Thanh, L. (2011). Reliability-Based Routing Algorithms for Energy-Aware Communication in Wireless Sensor Networks. In: Gülpınar, N., Harrison, P., Rüstem, B. (eds) Performance Models and Risk Management in Communications Systems. Springer Optimization and Its Applications, vol 46. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0534-5_5

Download citation

Publish with us

Policies and ethics

Search

Navigation