Advertisement

Quality of Service in Wireless Sensor Networks

  • Can Basaran
  • Kyoung-Don Kang
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

Although well studied for traditional computer networks, quality of service (QoS) concepts have not been applied to wireless sensor networks (WSNs) until recently. QoS support is challenging due to severe energy and computational resource constrains of wireless sensors. Moreover, certain service properties such as the delay, reliability, network lifetime, and quality of data may conflict by nature. Multi-path routing, for example, can improve the reliability; however, it can increase the energy consumption and delay due to duplicate transmissions. Also, high resolution sensor readings incur more energy consumptions and delays. Modeling such relationships, measuring the provided quality, and providing means to control the balance is essential for QoS support. In this context, this chapter discusses existing approaches for QoS support in WSNs and suggests directions for further research.

Keywords

Network Lifetime Sensor Reading Early Deadline First Schedule Geographic Forwarding Severe Resource Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    R. Braden, D. Clark, and S. Shenker. Integrated Services in the Internet Architecture: An Overview. IETF RFC 1633,Google Scholar
  2. 2.
    S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss. An Architecture for Differentiated Services. IETF RFC 2475,Google Scholar
  3. 3.
    P. Mahapatra, J. Li, and C. Gui. QoS in mobile ad hoc networks, IEEE Wireless Communication, vol. 10, no. 3, pp. 44–52,Google Scholar
  4. 4.
    K. Wu and J. Harm, QoS support in mobile ad hoc networks, Crossing Boundaries, vol. 1, no. 1, FallGoogle Scholar
  5. 5.
    I.F. Akyildiz et al. Wireless sensor networks: A survey, Computer Networks, Elsevier Science, vol. 38, no. 4, pp. 393–422,Google Scholar
  6. 6.
    Y. Wang, X. Liu, and J. Yin. Requirements of Quality of Service in Wireless Sensor Network. International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06), Mauritius,Google Scholar
  7. 7.
    N. Ota, D. Hooks, P. Wright, D. Auslander, and T. Peffer. Poster Abstract: Wireless Sensor Network Characterization – Application to Demand Response Energy Pricing, In Proceedings of the First international conference on Embedded Networked Sensor Systems, November 05–07,Google Scholar
  8. 8.
    M. Perillo and W. Heinzelman. Sensor Management, Wireless Sensor Networks, Kluwer Academic,Google Scholar
  9. 9.
    L. Song and D. Hatzinakos. A cross-layer architecture of wireless sensor networks for target tracking, IEEE/ACM Transactions on Networking, vol. 15, no. 1, pp. 145–158,Google Scholar
  10. 10.
    Q. Zao and L. Tong. QoS Specific Medium Access Control for Wireless Sensor Networks Fading, Eighth International Workshop on Signal Processing for Space Communications, Cataria, Italy, JulyGoogle Scholar
  11. 11.
    Y. Liu, I. Elhanany, and H. Qi. An Energy-Efficient QoS-Aware Media Access Control Protocol for Wireless Sensor Networks. In Proceedings of the IEEE International Conference on Mobile Adhoc and Sensor Systems, NovemberGoogle Scholar
  12. 12.
    M.C. Vuran and I.F. Akyildiz. Spatial correlation-based collaborative medium access control in wireless sensor networks, IEEE/ACM Transactions on Networking, vol. 14, no. 2, pp. 316–329,Google Scholar
  13. 13.
    M. Caccamo, L.Y. Zhang, L. Sha, and G. Buttazzo. An implicit prioritized access protocol for wireless sensor networks. In Proceedings of IEEE Real-Time Systems Symp., Dec. 2002, pp. 39–Google Scholar
  14. 14.
    S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong. “TinyDB: An Acquisitional Query Processing System for Sensor Networks,” in ACM Transactions on Database Systems,Google Scholar
  15. 15.
    W.S. Conner, J. Chhabra, M. Yarvis, and L. Krishnamurthy. Experimental Evaluation of Synchronization and Topology Control for In-Building Sensor Network Applications, in Proceedings of Wireless Sensor Networks and Applications, San Diego, CA, SeptemberGoogle Scholar
  16. 16.
    J. Beutel. Metrics for sensor network platforms. In ACM RealWSN’6, Uppsala, Sweden, JuneGoogle Scholar
  17. 17.
    E. Felemban, C. Lee, and E. Ekici. MMSPEED: Multipath multi-SPEED protocol for QoS guarantee of reliability and timeliness in wireless sensor networks, IEEE Transitions on Mobile Computing, vol. 5, no. 6, pp. 738–754,Google Scholar
  18. 18.
    D. Ganesan et al. Highly resilient, energy efficient multipath routing in wireless sensor networks, Mobile Computing and Communications Review, vol. 5, no. 4, pp. 11–25,Google Scholar
  19. 19.
    X. Huang and Y. Fang. Multiconstrained QoS multipath routing in wireless sensor networks, Wireless Networks Journal, vol. 14, no. 4, pp. 465–478,Google Scholar
  20. 20.
    N. Jain, D. Madathil, and D. Agrawal. Energy aware multi-path routing for uniform resource utilization in sensor networks. In International Workshop on Information Processing in Sensor Networks (IPSN), AprilGoogle Scholar
  21. 21.
    C. Lu et al., RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks, In Proceedings of the Eighth Real-Time and Embedded Technology and Applications Symposium, IEEE CS Press, Los Alamitos, CA,Google Scholar
  22. 22.
    B. Karp and H.T. Kung. GPSR: Greedy Perimeter Stateless Routing for Wireless Networks, In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networking, August 06–11, pp. 243–254,Google Scholar
  23. 23.
    Y.-B. Ko and N.H. Vaidya. Location-aided routing (LAR) in mobile ad hoc networks. In Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking, October 25–30, pp. 66–75,Google Scholar
  24. 24.
    J. Elson. Deborah Estrin, Time Synchronization for Wireless Sensor Networks. In Proceedings of the 15th International Parallel & Distributed Processing Symposium, April 23–27, 2001, p.Google Scholar
  25. 25.
    I. Aad and C. Castelluccia. Differentiation Mechanisms for IEEE 802.11. IEEE INFOCOM 2001, Anchorage, Alaska, April 20Google Scholar
  26. 26.
    T. He, J.A. Stankovic, C. Lu, and T.F. Abdelzaher. SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks, In Proceedings of International Conference on. Distributed Computing Systems (ICDCS ’03), Providence, RI, MayGoogle Scholar
  27. 27.
    K. Liu, N. Abu-Ghazaleh, and K.D. Kang. JiTS: Just-in-Time Scheduling for Real-Time Sensor Data Dissemination. In Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications (PERCOM’06), Washington, DC, IEEE Computer Society, Silver Spring, MD, 2006, pp. 42–Google Scholar
  28. 28.
    Q. Huang, C. Lu, and G.-C. Roman. Mobicast: Just-in-time multicast for sensor networks under spatiotemporal constraints. International Workshop on Information Processing in Sensor Networks, Palo Alto, CA, AprilGoogle Scholar
  29. 29.
    S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong. TAG: A Tiny AGgregation service for Ad-Hoc sensor networks, In Proceedings of the Fifth symposium on Operating Systems Design and Implementation, December 09–11,Google Scholar
  30. 30.
    R. Kumar, S. PalChaudhuri, D. Johnson, and U. Ramachandran. Network Stack Architecture for Future Sensors, Rice University, Computer Science, Technical Report, TR04-447,Google Scholar
  31. 31.
    S. Goel and T. Imielinski. Prediction-based monitoring in sensor networks: Taking lessons from MPEG, ACM SIGCOMM Computer Communication Review, vol. 31, no. 5, OctoberGoogle Scholar
  32. 32.
    J. Watkinson, MPEG-2, Butterworth-Heinemann, Newton, MA,Google Scholar
  33. 33.
    M.A. Sharaf, J. Beaver, A. Labrinidis, and P.K. Chrysanthis. TiNA: A scheme for temporal coherency-aware in-network aggregation. In Proceedings of the Third ACM International Workshop on Data Engineering for Wireless and Mobile Access, San Diego, CA, September 19–19,Google Scholar
  34. 34.
    S. Santini and K. Romer. An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks. In Proceedings of the Third International Conference on Networked Sensing Systems (INSS’06), Chicago, IL,Google Scholar
  35. 35.
    S. Haykin. Least-Mean-Square Adaptive Filters. Edited by S. Haykin, New York: Wiley-Interscience,Google Scholar
  36. 36.
    Embedded WiSeNts. http://www.embedded-wisents.org/, December
  37. 37.
    K. Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie, Protocols for self-organization of a wireless sensor network, IEEE Personal Communications Magazine, vol. 7, no. 5, pp. 16–27, Oct.Google Scholar
  38. 38.
    E. Cheong and J. Liu. galsC: A Language for Event-Driven Embedded Systems. In Proceedings of the Conference on Design, Automation and Test in Europe, March 07–11, 2005, pp. 1050–Google Scholar
  39. 39.
    D. Gay, P. Levis, R. von Behren, M. Welsh, E. Brewer, and D. Culler. The nesC Language: A Holistic Approach to Networked Embedded Systems. In Proceedings of Programming Language Design and Implementation (PLDI) 2003, San Diego, CA, JuneGoogle Scholar
  40. 40.
    D. Janakiram and R. Venkateswarlu. A Distributed Compositional Language for Wireless Sensor Networks. In Proceedings of IEEE Conference on Enabling Technologies for Smart Appliances (ETSA), Hyderabad, IndiaGoogle Scholar
  41. 41.
    Srisathapornphat, C. Jaikaeo, and C. Chien-Chung Shen Sensor Information Networking Architecture. International Workshops on Parallel Processing, pp. 23–30,Google Scholar
  42. 42.
    B. Greenstein, E. Kohler, and D. Estrin. A Sensor Network Application Construction Kit (SNACK), In Proceedings of the Second International Conference on Embedded Networked Sensor Systems, November 03–05,Google Scholar
  43. 43.
    I.F. Akyildiz, T. Melodia, and K.R. Chowdhury. A survey on wireless multimedia sensor networks, Computer Networks, vol. 51, 921–960,Google Scholar
  44. 44.
    Y. Gu, Y. Tian, and E. Ekinci. Real-time multimedia processing in video sensor networks, Image Communication, Elseiver Science, vol. 22, no. 3,Google Scholar
  45. 45.
    Y. Wang, R.R. Reibman, and S. Lin. Multiple description coding for video delivery, In Proceedings of the IEEE, vol. 93, no. 1, pp 57–70, JanuaryGoogle Scholar
  46. 46.
    M. Chu, J.E. Reich, and F. Zhao. Distributed attention for large video sensor networks. In Proceedings of the Institute of Defence and Strategic Studies (IDSS), London, UK, FebruaryGoogle Scholar
  47. 47.
    S. Kompella, S. Mao, Y.T. Hou, and H.D. Sherali. Cross-layer optimized multipath routing for video communications in wireless networks, IEEE Journal on Selected Areas in Communications, vol. 25, no. 4, pp. 831–840, MayGoogle Scholar
  48. 48.
    B. Krishnamachari, D. Estrin, S.B. Wicker. The Impact of Data Aggregation in Wireless Sensor Networks. In Proceedings of the 22nd International Conference on Distributed Computing Systems, July 02–05, 2002, pp. 575–578Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Can Basaran
    • 1
  • Kyoung-Don Kang
    • 1
  1. 1.Department of Computer ScienceThomas J. Watson School of Engineering and Applied ScienceNYUSA

Personalised recommendations