Skip to main content
SpringerLink
Log in
Book cover

Theoretical Aspects of Distributed Computing in Sensor Networks pp 407–445Cite as

Scheduling Algorithms for Tree-Based Data Collection in Wireless Sensor Networks

  • Chapter
  • First Online:

Abstract

Data collection is a fundamental operation in wireless sensor networks (WSN) where sensor nodes measure attributes about a phenomenon of interest and transmit their readings to a common base station. In this chapter, we survey contention-free time division multiple access (TDMA)-based scheduling protocols for such data collection applications over tree-based routing topologies. We classify the algorithms according to their common design objectives, identifying the following four as the most fundamental and most studied with respect to data collection in WSNs: (i) minimizing schedule length, (ii) minimizing latency, (iii) minimizing energy consumption, and (iv) maximizing fairness. We also describe the pros and cons of the underlying design constraints and assumptions and provide a taxonomy according to these metrics. Finally, we discuss some open problems together with future research directions.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    There is no causality constraint, such that node d does not need to wait for data from its children before being scheduled, and the data collection is periodic.

References

  1. V. Annamalai, S. Gupta, L. Schwiebert. On tree-based convergecasting in wireless sensor networks. In: WCNC ’03, volume 3, pages 1942–1947, New Orleans, LA, USA, 2003.

    Google Scholar 

  2. K. Arisha, M. Youssef, M. Younis. Energy-aware tdma-based mac for sensor networks. System-level power optimization for wireless multimedia communication pages 21–40, 2002.

    Google Scholar 

  3. I.N. Baljeet Malhotra, M.A. Nascimento. Aggregation convergecast scheduling in wireless sensor networks. Technical report, University of Alberta, 2009.

    Google Scholar 

  4. CC2420: Single-chip 2.4 ghz ieee 802.15.4 compliant and zigbee(tm) ready rf transceiver. http://www.ti.com/lit/gpn/cc2420.

  5. D. Chafekar, V.A. Kumar, M. Marathe, S. Parthasarathy, A. Srinivasan. Cross-layer latency minimization in wireless networks with SINR constraints. In: MobiHoc ’07, ACM, New York, NY, pages 110–119, Montreal, Quebec, Canada, 2007.

    Google Scholar 

  6. D. Chafekar, V.S.A. Kumar, M.V. Marathe, 0002. S.Parthasarathy, A. Srinivasan. Approximation algorithms for computing capacity of wireless networks with SINR constraints. In: INFOCOM, pages 1166–1174, Phoenix, AZ, USA, 2008.

    Google Scholar 

  7. S. Chatterjea, L. van Hoesel, P. Havinga. Ai-lmac: An adaptive, information-centric and lightweight mac protocol for wireless sensor networks. In: Issnip ’04, Melbourne, Australia, 2004.

    Google Scholar 

  8. X. Chen, X. Hu, J. Zhu. Minimum data aggregation time problem in wireless sensor networks. In: MSN, pages 133–142, Wuhan, China, 2005.

    Google Scholar 

  9. K. Chintalapudi, T. Fu, J. Paek, N. Kothari, S. Rangwala, J. Caffrey, R. Govindan, E. Johnson, S. Masri. Monitoring civil structures with a wireless sensor network. IEEE Internet Computing, 10(2): 26–34, 2006.

    Article  Google Scholar 

  10. K.K. Chintalapudi, L. Venkatraman. On the design of mac protocols for low-latency hard real-time discrete control applications over 802.15.4 hardware. In: IPSN ’08, pages 356–367, St. Louis, MO, USA, 2008.

    Google Scholar 

  11. I. Chlamtac, S. Kutten. Tree-based broadcasting in multihop radio networks. IEEE Transactions on Computers 36(10): 1209–1233 (1987)

    Article  Google Scholar 

  12. H. Choi, J. Wang, E. Hughes. Scheduling for information gathering on sensor network. Wireless Networks (Online) (2007)

    Google Scholar 

  13. S. Cui, R. Madan, A. Goldsmith, S. Lall. Energy-delay tradeoffs for data collection in tdma-based sensor networks. In: ICC ’05, volume 5, pages 3278–3284, Seoul, Korea, 2005.

    Google Scholar 

  14. M. Dalbro, E. Eikeland, A.J.i. Veld, S. Gjessing, T.S. Lande, H.K. Riis, O. Sør(\(\dot{a}\))sen. Wireless sensor networks for off-shore oil and gas installations. In: SENSORCOMM ’08, pages 258–263, Cap Esterel, France, 2008.

    Google Scholar 

  15. I. Demirkol, C. Ersoy, F. Alagoz. Mac protocols for wireless sensor networks: A survey. IEEE Communications Magazine 44(4): 115–121, 2006.

    Article  Google Scholar 

  16. P. Djukic, S. Valaee. Link scheduling for minimum delay in spatial re-use tdma. In: Infocom ’07, pages 28–36, IEEE, Anchorage, Alaska, USA, 2007.

    Google Scholar 

  17. E. Duarte-Melo, M. Liu. Data-gathering wireless sensor networks: Organization and capacity. Computer Networks 43(4): 519–537, 2003.

    Article  MATH  Google Scholar 

  18. T. ElBatt, A. Ephremides. Joint scheduling and power control for wireless ad-hoc networks. In: Infocom ’02, volume 2, pages 976–984, 2002.

    Google Scholar 

  19. J. Elson, L. Girod, D. Estrin. Fine-grained network time synchronization using reference broadcasts. SIGOPS Operator Systems Review, 36(SI): 147–163, 2002.

    Article  Google Scholar 

  20. S. Ergen, P. Varaja. Tdma scheduling algorithms for sensor networks. Technical report, University of California, Berkeley, 2005.

    Google Scholar 

  21. S. Fan, L. Zhang, Y. Ren. Approximation algorithms for link scheduling with physical interference model in wireless multi-hop networks. CoRR abs/0910.5215, 2009.

    Google Scholar 

  22. C. Florens, M. Franceschetti, R. McEliece. Lower bounds on data collection time in sensory networks. IEEE Journal on Selected Areas in Communications 22(6): 1110–1120, 2004.

    Article  Google Scholar 

  23. C. Florens, R. McEliece. Scheduling algorithms for wireless ad-hoc sensor networks. In: Globecom ’02, pages 6–10, IEEE, Taipei, Taiwan, 2002.

    Google Scholar 

  24. C. Florens, R. McEliece. Packets distribution algorithms for sensor networks. In: Infocom ’03, volume 2, pages 1063–1072, IEEE, San Francisco, CA, USA, 2003.

    Google Scholar 

  25. G. Foschini, Z. Miljanic. A simple distributed autonomous power control algorithm and its convergence. IEEE Transactions on Vehicular Technology 42(4): 641–646, 1993.

    Article  Google Scholar 

  26. S. Gandham, Y. Zhang, Q. Huang. Distributed minimal time convergecast scheduling in wireless sensor networks. In: ICDCS ’06, IEEE Computer Society, Washington, DC, page 50, 2006. DOI http://dx.doi.org/10.1109/ICDCS.2006.30

  27. S. Gandham, Y. Zhang, Q. Huang. Distributed time-optimal scheduling for convergecast in wireless sensor networks. Computer Networks 52(3): 610–629, 2008.

    MATH  Google Scholar 

  28. L. Gargano. Time optimal gathering in sensor networks. In: SIROCCO ’07, pages 7–10, 2007.

    Google Scholar 

  29. A. Ghosh. Estimating coverage holes and enhancing coverage in mixed sensor networks. In: LCN ’04. IEEE Computer Society, Washington, DC, pages 68–76, 2004. DOI http:// dx.doi.org/10.1109/LCN.2004.53

  30. A. Ghosh, O.D. Incel, V.A. Kumar, B. Krishnamachari. Multi-channel scheduling algorithms for fast aggregated convergecast in sensor networks. In: MASS ’09, pages 362–372, IEEE, Macau, China.

    Google Scholar 

  31. GINSENG: Performance control in wireless sensor networks. www.ict-ginseng.eu

  32. O. Goussevskaia, Y.A. Oswald, R. Wattenhofer. Complexity in geometric SINR. In: MobiHoc ’07, ACM, New York, NY, USA, pages 100–109, 2007. http://doi.acm.org/ 10.1145/1288107.1288122

  33. J. Grönkvist, A. Hansson. Comparison between graph-based and interference-based stdma scheduling. In: MobiHoc ’01, pages 255–258, ACM, Long Beach, CA, USA, 2001.

    Google Scholar 

  34. P. Gupta, P. Kumar. The capacity of wireless networks. IEEE Transactions on Information Theory IT-46(2): 388–404, 2000.

    Article  MathSciNet  Google Scholar 

  35. N.J. Harvey, R.E. Ladner, L. Lovász, T. Tamir. Semi-matchings for bipartite graphs and load balancing. Journal of Algorithms 59(1): 53–78, 2006. http://dx.doi.org/10.1016/j.jalgor.2005. 01.003

    Article  MATH  MathSciNet  Google Scholar 

  36. L. van Hoesel, P. Havinga. A lightweight medium access protocol (LMAC) for wireless sensor networks. In: INSS’ 04. SICE (Society of Instrument and Control Engineers), Tokyo, Japan, 2004.

    Google Scholar 

  37. D.O. Incel, A. Ghosh, B. Krishnamachari, K. Chintalapudi. Fast data collection in tree-based wireless sensor networks. IEEE Transactions on Mobile Computing (submitted), 2009.

    Google Scholar 

  38. O.D. Incel, B. Krishnamachari. Enhancing the data collection rate of tree-based aggregation in wireless sensor networks. In: SECON ’08, pages 569–577, IEEE, San Francisco, CA, USA, 2008.

    Google Scholar 

  39. K. Kalpakis, K. Dasgupta, P. Namjoshi. Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks. Computing Network 42(6): 697–716, 2003.

    Article  MATH  Google Scholar 

  40. A. Keshavarzian, H. Lee, L. Venkatraman. Wakeup scheduling in wireless sensor networks. In: MobiHoc ’06, ACM, New York, NY, pages 322–333, 2006. http://doi.acm.org/10.1145/ 1132905.1132941

  41. N. Lai, C.King, C. Lin. On maximizing the throughput of convergecast in wireless sensor networks. In: GPC ’08, pages 396–408, Kunming, China, 2008.

    Google Scholar 

  42. H. Lee, A. Keshavarzian. Towards energy-optimal and reliable data collection via collision-free scheduling in wireless sensor networks. In: INFOCOM, pages 2029–2037, Phoenix, AZ, USA, 2008.

    Google Scholar 

  43. H. Lee, A. Keshavarzian, H.K. Aghajan. Multi-cluster multi-parent wake-up scheduling in delay-sensitive wireless sensor networks. In: GLOBECOM, pages 430–435, New Orleans, LA, USA, 2008.

    Google Scholar 

  44. H. Li, P. Shenoy, K. Ramamritham. Scheduling messages with deadlines in multi-hop real-time sensor networks. In: RTAS 2005, pages 415–425, San Francisco, CA, USA, 2005.

    Google Scholar 

  45. X.Y. Li, Y. Wang. Simple heuristics and ptass for intersection graphs in wireless ad hoc networks. In: DIALM ’02, ACM, New York, NY, pages 62–71, 2002. http://doi.acm.org/10.1145/ 570810.570819

  46. G. Lu, B. Krishnamachari. Minimum latency joint scheduling and routing in wireless sensor networks. Ad Hoc Netw. 5(6): 832–843, 2007. http://dx.doi.org/10.1016/j.adhoc.2007.03.002

    Article  Google Scholar 

  47. G. Lu, N. Sadagopan, B. Krishnamachari, A. Goel. Delay efficient sleep scheduling in wireless sensor networks. In: INFOCOM ’05, pages 2470–2481, Miami, FL, USA, 2005.

    Google Scholar 

  48. M. Macedo, A. Grilo, M. Nunes. Distributed latency-energy minimization and interference avoidance in tdma wireless sensor networks. Computing Network 53(5): 569–582, 2009. http:// dx.doi.org/10.1016/j.comnet.2008.10.015

    Article  Google Scholar 

  49. S. Madden, M. Franklin, J. Hellerstein, W. Hong. Tinydb: An acquisitional query processing system for sensor networks. ACM Transactions on Database Systems 30(1): 122–173, 2005.

    Article  Google Scholar 

  50. A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, J. Anderson. Wireless sensor networks for habitat monitoring. In: WSNA ’02, pages 88–97, Atlanta, GA, USA, 2002.

    Google Scholar 

  51. J. Mao, Z. Wu, X. Wu. A tdma scheduling scheme for many-to-one communications in wireless sensor networks. Computer Communications 30(4): 863–872, 2007.

    Article  Google Scholar 

  52. T. Moscibroda. The worst-case capacity of wireless sensor networks. In: IPSN ’07, pages 1–10, Cambridge, MA, USA, 2007.

    Google Scholar 

  53. T. Moscibroda, R. Wattenhofer, A. Zollinger. Topology control meets SINR: The scheduling complexity of arbitrary topologies. In: MobiHoc ’06, pages 310–321, 2006.

    Google Scholar 

  54. F. Osterlind, A. Dunkels. Approaching the maximum 802.15.4 multi-hop throughput. In: HotEmNets 2008, Charlottesville, VA, page 6, 2008. http://eprints.sics.se/3426/01/osterlind08approaching.pdf

  55. Y.A. Oswald, S. Schmid, R. Wattenhofer. Tight bounds for delay-sensitive aggregation. In: PODC ’08, ACM, New York, NY, pages 195–202, 2008. http://doi.acm.org/10.1145/ 1400751.1400778

  56. M. Pan, Y. Tseng. Quick convergecast in zigbee beacon-enabled tree-based wireless sensor networks. Computer Communications 31(5): 999–1011, 2008.

    Article  Google Scholar 

  57. C. Papadimitriou. The complexity of the capacitated tree problem. Networks 8(3): 217–230, 1978.

    Article  MathSciNet  Google Scholar 

  58. V. Rajendran, K. Obraczka, J. Garcia-Luna-Aceves. Energy-efficient, collision-free medium access control for wireless sensor networks. In: SenSys ’03, pages 181–192, 2003.

    Google Scholar 

  59. S. Ramanathan, E. Lloyd. Scheduling algorithms for multihop radio networks. IEEE/ACM Transactions on Networking 1(2): 166–177, 1993.

    Article  Google Scholar 

  60. Y. Revah, M. Segal. Improved lower bounds for data-gathering time in sensor networks. In: ICNS ’07, IEEE Computer Society, Washington, DC, page 76, 2007. http://dx.doi.org/ 10.1109/ICNS.2007.71

  61. I. Rhee, A. Warrier, M. Aia, J. Min. Z-mac: A hybrid mac for wireless sensor networks. In: SenSys ’05, pages 90–101, 2005.

    Google Scholar 

  62. Nordic Semi Conductors, nrf905 multiband transceiver. http://www.nordicsemi.com

  63. W. Shang, P. Wan, X. Hu. Approximation algorithm for minimal convergecast time problem in wireless sensor networks. Wireless Networks, 2009. 10.1007/s11276-009-0207-9

    Google Scholar 

  64. F. Sivrikaya, B. Yener. Time synchronization in sensor networks: A survey. IEEE Network 18(4): 45–50, 2004. 10.1109/MNET.2004.1316761

    Article  Google Scholar 

  65. J. Song, S. Han, A. Mok, D. Chen, M. Lucas, M. Nixon. Wirelesshart: Applying wireless technology in real-time industrial process control. In: RTAS ’08, pages 377–386, St. Louis, MO, USA, 2008.

    Google Scholar 

  66. W.Z. Song, F. Yuan, R. LaHusen, B. Shirazi. Time-optimum packet scheduling for many-to-one routing in wireless sensor networks. International Journal Parallel Emergent Distributed Systems 22(5): 355–370, 2007. http://dx.doi.org/10.1080/17445760601111459

    Article  MATH  MathSciNet  Google Scholar 

  67. A. Sridharan, B. Krishnamachari. Max-min fair collision-free scheduling for wireless sensor networks. In: IPCCC ’04, pages 585–590, Austin, TX, USA, 2004.

    Google Scholar 

  68. N. Trigoni, Y. Yao, A. Demers, J. Gehrke, R. Rajaraman. Wave scheduling and routing in sensor networks. ACM Transactions on Sensor Networks 3(1): 2, 2007.

    Article  Google Scholar 

  69. H.W. Tsai, T.S. Chen. Minimal time and conflict-free schedule for convergecast in wireless sensor networks. In: ICC ’08, pages 2808–2812, 2008.

    Google Scholar 

  70. S. Upadhyayula, S. Gupta. Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (dac) in wireless sensor networks. Ad Hoc Networks 5(5): 626–648, 2007.

    Article  Google Scholar 

  71. T. Wang, Z. Wu, J. Mao. A new method for multi-objective tdma scheduling in wireless sensor networks using pareto-based pso and fuzzy comprehensive judgement. In: HPCC ’07, Springer, Berlin, pages 144–155, 2007.

    Google Scholar 

  72. B. Yu, J. Li, Y. Li. Distributed data aggregation scheduling in wireless sensor networks. In: Infocom ’09, Rio de Janeiro, Brazil, 2009.

    Google Scholar 

  73. L. Yu, N. Wang, X. Meng. Real-time forest fire detection with wireless sensor networks. In: WiCom, volume 2, pages 1214–1217, 2005.

    Google Scholar 

  74. Y. Yu, B. Krishnamachari, V.K. Prasanna. Energy-latency tradeoffs for data gathering in wireless sensor networks. In: INFOCOM, Hong Kong, China, 2004.

    Google Scholar 

  75. H. Zhang, F. Österlind, P. Soldati, T. Voigt, M. Johansson. Time-optimal convergecast with separated packet copying. Technical report, Royal Institute of Technology (KTH) (2009)

    Google Scholar 

  76. H. Zhang, P. Soldati, M. Johansson. Optimal link scheduling and channel assignment for convergecast in linear wirelessHART networks. In: WiOPT ’09, Seoul, Korea, 2009.

    Google Scholar 

  77. Y. Zhang, S. Gandham, Q. Huang. Distributed minimal time convergecast scheduling for small or sparse data sources. In: RTSS ’07, IEEE Computer Society, Washington, DC, pages 301–310, 2007. http://dx.doi.org/10.1109/RTSS.2007.19

Download references

Author information

Authors and Affiliations

  1. NETLAB, Department of Computer Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey

    Ozlem Durmaz Incel

  2. Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA

    Amitabha Ghosh & Bhaskar Krishnamachari

Authors
  1. Ozlem Durmaz Incel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amitabha Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bhaskar Krishnamachari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozlem Durmaz Incel .

Editor information

Editors and Affiliations

  1. Computer Technology Institute, N. Kazantzaki Street 1, Rion, Patras, 265 04, Greece

    Sotiris Nikoletseas

  2. Centre Universitaire d'Informatique, Université de Genève, rte de Drize 7, Carouge, 1227, Switzerland

    José D.P. Rolim

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Incel, O.D., Ghosh, A., Krishnamachari, B. (2011). Scheduling Algorithms for Tree-Based Data Collection in Wireless Sensor Networks. In: Nikoletseas, S., Rolim, J. (eds) Theoretical Aspects of Distributed Computing in Sensor Networks. Monographs in Theoretical Computer Science. An EATCS Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14849-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14849-1_14

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14848-4

  • Online ISBN: 978-3-642-14849-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation