Skip to main content
SpringerLink
Log in

Data Gathering in Wireless Sensor Networks

  • Chapter
  • First Online:
Book cover The Art of Wireless Sensor Networks

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Data gathering is one of the primary operations carried out in Wireless Sensor Networks (WSNs). It involves data collection with aggregation and data collection without aggregation, referred to as data aggregation and data collection respectively. In the last decade, many techniques for these two applications are proposed, with different focuses, such as accuracy, reliability, time complexity, and so on. This chapter reviews the state of the art of data aggregation and data collection techniques in order to present a comprehensive guidance on how to choose a more appropriate approach for different applications. The definitions of data aggregation and data collection are firstly introduced. Subsequently, the challenges of designing effective data aggregation and data collection methods are discussed. Then some typical data aggregation techniques and their classifications are presented. Particularly, a latest distributed data aggregation algorithm (DAS) is illustrated in details. For data collection, we begin with some new advances and then introduce several new tree-based and cell-based data collection algorithms. Finally, this chapter is ended by pointing out some possible future research directions.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
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

Notes

  1. 1.

    Currently, some works have been proposed on the time synchronization issue for wireless sensor networks. For instance, in [23], a flooding-based time synchronization method, named Glossy, was designed. By testbed experiments (the testbed is a wireless sensor network consisting of 39–94 sensor nodes) and analysis, it can be shown that Glossy can achieve high-accurate network-wide time synchronization (at the millisecond level). Here, we emphasize that for large-scale wireless sensor networks deployed in an open/outdoor environment, e.g., GreenOrbs, a practical wireless sensor network consisting of 1000\(\, +\, \) sensor nodes deployed in a forest [32], it might be difficult to achieve network-wide accurate time synchronization.

  2. 2.

    As indicated by some recent research, the protocol interference model may not be practical in realistic WSN applications. However, it has been employed since it simplifies the quantitative analysis process of a designed protocol which might provide some general guiding rules and insights on designing practical protocols for WSNs.

  3. 3.

    We use link and edge interchangeably in this chapter.

  4. 4.

    Under the physical interference model, a node \(u\) can successfully transmit data to another node \(v\) only if the Signal-to-Interference-plus-Noise Ratio (SINR) at \(v\) associated with \(u\) is no less than a threshold value. Thus, the data transmission under the physical interference model can also be viewed as a binary function, i.e., to be failed or to be successful. Under the generalized physical interference model, instead of modeling a data transmission as a binary function, the data receiving rate at \(v\) from \(u\) is a continuous function depending on the SINR value at \(v\).

  5. 5.

    The network considered in [57] is a single-hop wireless sensor network where all the nodes can communicate directly. When considering the lifetime of the last node, it is defined as the time duration from the network being deployed to the time that the last node exhausts its energy. When considering the lifetime of the first node, it is defined as the time duration from the network being deployed to the time that a node exhausts its energy.

  6. 6.

    In this chapter, if we say two nodes \(u\) and \(v\) are adjacent/connected, we mean \(u\) and \(v\) are within the communication range of each other, i.e., \(\left\| u-v\right\| \le r\), where \(r\) is the transmission range of sensor nodes.

  7. 7.

    In a wireless network, if a node works on the half-duplex mode, then, at any time, this node can either transmit data to some other node, or receive data from some other node, but not both. If a node works on the full-duplex mode, then, this node can transmit and receive data simultaneously without any confliction or interference.

References

  1. I. Abraham, D. Malkhi, Probabilistic quorums for dynamic systems. Distrib. Comput. 18(2), 113–124 (2005)

    Article  MATH  Google Scholar 

  2. K. Akkaya, M. Demirbas, R.S. Aygun, The impact of data aggregation on the performance of wireless sensor networks. Wireless Commun. Mob. Comput. 8, 171–193 (2008)

    Article  Google Scholar 

  3. H. Alzaid, E. Foo, and J.G. Nieto, Secure data aggregation in wireless sensor networks: a survey, in CRPIT (2008)

    Google Scholar 

  4. T. Arici, B. Gedik, Y. Altunbasak, and L. Liu, PINCO: a pipelined in-network compression scheme for data collection in wireless sensor networks, in ICCCN (2003)

    Google Scholar 

  5. Z. Bar-Yossef, R. Friedman, and G. Kliot, RaWMS—Random walk based lightweight membership service for wireless ad hoc networks. ACM Trans. Comput. Syst. 26(2), 1–66 (2008)

    Google Scholar 

  6. D. Borsetti, C. Casetti, C.-F. Chiasserini, M. Fiore, J. María, Virtual data mules for data collection in road-side sensor networks, in ACM MobiOpp (2010)

    Google Scholar 

  7. Z. Cai, S. Ji, J. He, A.G. Bourgeois, Optimal distributed data collection for asynchronous cognitive radio networks, in IEEE ICDCS (2012)

    Google Scholar 

  8. I. Chatzigiannakis, A. Kinalis, S. Nikoletseas, Sink mobility protocols for data collection in wireless sensor netowrks, in ACM MOBIWAC (2006)

    Google Scholar 

  9. S. Chen, Y. Wang, X.-Y. Li, X. Shi, Order-optimal data collection in wireless sensor networks: delay and capacity, in IEEE SECON (2009)

    Google Scholar 

  10. X. Chen, X. Hu, J. Zhu, Minimum data aggregation time problem in wireless sensor networks, in IEEE MSN (2005)

    Google Scholar 

  11. S. Chen, Y. Wang, X.-Y. Li, X. Shi, Data collection capacity of random-deployed wireless sensor networks, in IEEE GLOBECOM (2009)

    Google Scholar 

  12. S. Chen, S. Tang, M. Huang, Y. Wang, Capacity of data collection in arbitrary wireless sensor networks, in IEEE INFOCOM (2010)

    Google Scholar 

  13. S. Chen, M. Huang, S. Tang, Y. Wang, Capacity of data collection in arbitrary wireless sensor networks, in IEEE Transactions on Parallel and Distributed Systems (2011)

    Google Scholar 

  14. S. Chen, Y. Wang, X.-Y. Li, X. Shi, Capacity of data collection in randomly-deployed wireless sensor networks. Wireless Netw. 17, 305–318 (2011)

    Article  Google Scholar 

  15. S. Cheng, J. Li, Sampling based (epsilon, delta)-approximate aggregation algorithm in sensor networks, in ICDCS (2009)

    Google Scholar 

  16. S. Cheng, J. Li, Z. Cai, O(\(\epsilon \))-approximation to physical world by sensor networks, in IEEE INFOCOM (2013)

    Google Scholar 

  17. C.-T. Cheng, C.K. Tse, F.C.M. Lau, A delay-aware data collection network structure for wireless sensor networks. IEEE Sens. J. 11(3), 699–710 (2011)

    Article  Google Scholar 

  18. J. Considine, F. Li, G. Kollios, J. Byers, Approximate aggregation techniques for sensor databases, in ICDE (2004)

    Google Scholar 

  19. M. Ding, X. Cheng, Robust event boundary detection in sensor networks—a mixture model based approach, in IEEE INFOCOM (2009)

    Google Scholar 

  20. M. Ding, X. Cheng, G. Xue, Aggregation tree construction in sensor networks, in IEEE VTC (2003)

    Google Scholar 

  21. K. Du, J. Wu, D. Zhou, Chain-based protocols for data broadcasting and gathering in the sensor networks, in IEEE IPDPS (2003)

    Google Scholar 

  22. E. Fasolo, M. Rossi, J. Widmer, M. Zorzi, In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wirel. Commun. 14(2), 70–87 (2007)

    Article  Google Scholar 

  23. F. Ferrari, M. zimmerling, L. Thiele, O. Saukh, Efficient network flooding and time synchronization with glossy, in IPSN (2011)

    Google Scholar 

  24. M. D. Francesco, S.K. Das, Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans. Sens. Netw. 8(1), 1–31, 2011

    Google Scholar 

  25. J. Guo, J. Fang, X. Chen, Survey on secure data aggregation for wirless sensor networks, in IEEE SOLI (2011)

    Google Scholar 

  26. J. He, S. Ji, Y. Pan, Z. Cai, Approximation algorithms for load-balanced virtual backbone construction in wireless sensor networks, Theoretical Computer Science

    Google Scholar 

  27. J. He, S. Ji, M. Yan, Y. Pan, Y. Li, Load-balanced CDS construction in wireless sensor networks via genetic algorithm. Int. J. Sens. Netw. (2011)

    Google Scholar 

  28. J. He, S. Ji, M. Yan, Y. Pan, Y. Li, Genetic-algorithm-based construction of load-balanced CDSs in wireless sensor networks, in MILCOM (2011)

    Google Scholar 

  29. J. He, Z. Cai, S. Ji, R. Beyah, Y. Pan, A genetic algorithm for constructing a reliable MCDS in probabilistic wireless networks, in WASA (2011)

    Google Scholar 

  30. J. He, S. Ji, P. Fan, Y. Pan, Y. Li, Constructing a load-balanced virtual backbone in wireless sensor networks, in ICNC (2012)

    Google Scholar 

  31. J. He, S. Ji, Y. Pan, Z. Cai, Load-balanced virtual backbone construction for wireless sensor networks, in COCOA (2012)

    Google Scholar 

  32. http://www.greenorbs.org/

  33. Q. Huang, Y. Zhang, Radial coordination for convergecast in wireless sensor networks, in IEEE LCN (2004)

    Google Scholar 

  34. S. Jain, R.C. Shah, S. Roy, Exploiting mobility for energy efficient data collection in wireless sensor networks. Mob. Netw. Appl. 11, 327–339 (2006)

    Article  Google Scholar 

  35. P. Jesus, C. Baquero, P.S. Almeida, A survey of distributed data aggregation algorithms, Technical report (2011)

    Google Scholar 

  36. S. Ji, Z. Cai, Distributed data collection and its capacity in asynchronous wireless sensor networks, in INFOCOM (2012)

    Google Scholar 

  37. S. Ji, Z. Cai, Distributed data collection in large-scale asynchronous wireless sensor networks under the generalized physical interference model. IEEE/ACM Trans. Netw. (in press)

    Google Scholar 

  38. S. Ji, R. Beyah, Z. Cai, Snapshot and continuous data collection in probabilistic wireless sensor networks. IEEE Trans. Mob. Comput. (in press)

    Google Scholar 

  39. S. Ji, Y. Li, X. Jia, Capacity of dual-radio multi-channel wireless sensor networks for continuous data collection, in IEEE INFOCOM (2011)

    Google Scholar 

  40. S. Ji, R. Beyah, Y. Li, Continuous data collection capacity of wireless sensor networks under physical interference model, in IEEE MASS (2011)

    Google Scholar 

  41. S. Ji, R. Beyah, Z. Cai, Snapshot/Continuous data collection capacity for large-scale probabilistic wireless sensor networks, in INFOCOM (2012)

    Google Scholar 

  42. H. Jiang, S. Jin, C. Wang, Prediction or not? an energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22(6), 1064–1071 (2011)

    Google Scholar 

  43. S. Ji, Z. Cai, Y. Li, X. Jia, Continuous data collection capacity of dual-radio multi-channel wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(10), 1844–1855 (October 2012)

    Google Scholar 

  44. A. Kesselman, D. Kowalski, Fast distributed algorithm for convergecast in ad hoc geometric radio networks, in IEEE WONS (2005)

    Google Scholar 

  45. A. Kinalis, S. Nikoletseas, Scalable data collection protocols for wireless sensor networks with multiple mobile sinks, in IEEE ANSS (2007)

    Google Scholar 

  46. H. Lee, A. Keshavarzian, Towards energy-optimal and reliable data collection via collision-free scheduling in wireless sensor networks, in IEEE INFOCOM (2008)

    Google Scholar 

  47. H. Lee, A. Keshavarzian, H. Aghajan, Near-lifetime-optimal data collection in wireless sensor networks via spatio-temporal load balancing. ACM Trans. Sens. Netw. 6(3) (2010)

    Google Scholar 

  48. Y. Li, L. Guo, S.K. Prasad, An energy-efficient distributed algorithm for minimum-latency aggregation scheduling in wireless sensor networks, in IEEE ICDCS (2011)

    Google Scholar 

  49. J. Li, S. Cheng, \((\epsilon,\delta )\)-approximate aggregation algorithms in dynamic sensor networks. IEEE Trans. Parallel Distrib. Syst. 23, 385–396 (2012)

    Article  Google Scholar 

  50. S. Lindsey, C. Raghavendra, K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics. IEEE Trans. Parallel Distrib. Syst. 13(9), 924–935 (2002)

    Article  Google Scholar 

  51. F. Liu, X. Cheng, D. Chen, Insider attacker detection in wireless sensor networks, in IEEE INFOCOM (2007)

    Google Scholar 

  52. C. Liu, K. Wu, J. Pei, An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Trans. Parallel Distrib. Syst. 18(7), 1010–1023 (2007)

    Article  Google Scholar 

  53. W. Lou, Y. Kwon, H-SPREAD: a hybrid multipath scheme for secure and reliable data collection in wireless sensor networks. IEEE Trans. Veh. Technol. 55(4), 1320–1330 (2006)

    Article  Google Scholar 

  54. C. Luo, F. Wu, J. Sun, C.W. Chen, Compressive data gathering for large-scale wireless sensor networks, in ACM MOBICOM (2009)

    Google Scholar 

  55. H. V. Luu, X. Tang, An efficient scheduling algorithm for data collection through multi-path routing structures in wireless sensor networks, in IEEE MSN (2010)

    Google Scholar 

  56. G. Manku, Routing networks for distributed hash tables, in PODC (2003)

    Google Scholar 

  57. H. Ö. Tan, ï. Körpeoǧlu, Power efficient data gathering and aggregation in wireless sensor networks, SIGMOD Record, vol. 32, No. 4, pp. 66–71 (2003)

    Google Scholar 

  58. A.S. Poornima, B.B. Amberker, Secure data collection using mobile data collector in clustered wireless sensor networks. IET Wireless Sens. Syst. 1(2), 85–95 (2011)

    Article  Google Scholar 

  59. R. Rajagopalan, P.K. Varshney, Data-aggregation techniques in sensor networks: a survey. IEEE Commun. Surv. Tutor. 8(4), 48–63 (2006)

    Article  Google Scholar 

  60. C. Ren, X. Mao, P. Xu, G. Dai, Z. Li, Delay and energy efficiency tradeoffs for data collections and aggregation in large scale wireless sensor networks, in IEEE MASS (2009)

    Google Scholar 

  61. S. Rothery, W. Hu, P. Corke, An empirical study of data collection protocols for wireless sensor networks, in ACM RealWSN (2008)

    Google Scholar 

  62. Y. Sang, H. Shen, Y. Inoguchi, Y. Tan, N. Xiong, Secure data aggregation in wireless sensor networks: a survey, in IEEE PDCAT (2006)

    Google Scholar 

  63. N. Shrivastava, C. Buragohain, D. Agrawal, S. Suri, Medians and beyond: new aggregation techniques for sensor networks, in ACM Sensys (2004)

    Google Scholar 

  64. J.L.V.M. Stanislaus, M. Younis, Delay-conscious federation of multiple wireless sensor network segments using mobile relays, in IEEE VTC 2012-Fall (2012)

    Google Scholar 

  65. I. Stoica, R. Morris, D. Karger, M. Kaashoek, H. Balakrishnan, Chord: a scalable peer-to-peer lookup service for internet applications, in SIGCOMM (2001)

    Google Scholar 

  66. X. Tang, J. Xu, Adaptive data collection strategies for lifetime-constrained wireless sensor networks. IEEE Trans. Parrellel Distrib. Syst. 19(6), 721–734 (2008)

    Article  Google Scholar 

  67. M. Thangaraj, P.P. Ponmalar, A survey on data aggregation techniques in wireless sensor networks. Int. J. Res. Rev. Wireless Sens. Netw. 1(3), 36–42 (2011)

    Google Scholar 

  68. P.-J. Wan, S.C.-H. Huang, L. Wang, Z. Wan, X. Jia, Minimum-latency aggregation scheduling in multihop wireless networks, in ACM MOBIHOC (2009)

    Google Scholar 

  69. F. Wang, J. Liu, Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun. Surv. Tutor. 13(4) (2011)

    Google Scholar 

  70. C. Wang, H. Ma, Y. He, S. Xiong, Approximate data collection for wireless sensor networks, in IEEE ICPADS (2010)

    Google Scholar 

  71. F. Wang, D. Wang, J. Liu, Traffic-aware relay node deployment: maximizing lifetime for data collection wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22(8), 1415–1423 (2011)

    Article  Google Scholar 

  72. W. Wu, X. Cheng, M. Ding, K. Xing, F. Liu, P. Deng, Localized outlying and boundary data detection in sensor networks. TKDE 19(8), 1145–1157 (2007)

    Google Scholar 

  73. K. Xing, X. Cheng, Location-centric storage for on-demand warning in sensor networks, in IEEE INFOCOM (2005)

    Google Scholar 

  74. X. Xu, S. Wang, X. Mao, S. Tang, X. Li, An improved approximation algorithm for data aggregation in multi-hop wireless sensor networks, in FOWANC (2009)

    Google Scholar 

  75. Y. Yu, B. Krishnamachari, V.K. Prasanna, Energy-latency tradeoffs for data gathering in wireless sensor networks, in IEEE INFOCOM (2004)

    Google Scholar 

  76. B. Yu, J. Li, Y. Li, Distributed data aggregation scheduling in wireless sensor networks, in IEEE INFOCOM (2009)

    Google Scholar 

  77. H. Zhang, A. Arora, Y. Choi, M.G. Gouda, Reliable bursty convergecast in wireless sensor networks, in ACM MOBIHOC (2005)

    Google Scholar 

  78. Y. Zhang, S. Gandham, Q. Huang, Distributed minimal time convergecast scheduling for small or sparse data sources, in RTSS (2007)

    Google Scholar 

  79. J. Zhu, X. Hu, Improved algorithm for minimum data aggregation time problem in wireless sensor networks. J. Syst. Sci. Complexity 21, 626–636 (2008)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

    Shouling Ji & Zhipeng Cai

  2. Department of Computer Science, Kennesaw State University, Kennesaw, GA, 30144-5591, USA

    Jing (Selena) He

Authors
  1. Shouling Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing (Selena) He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhipeng Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhipeng Cai .

Editor information

Editors and Affiliations

  1. College of Engineering and Computer Science, Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    Habib M. Ammari

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ji, S., He, J., Cai, Z. (2014). Data Gathering in Wireless Sensor Networks. In: Ammari, H. (eds) The Art of Wireless Sensor Networks. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40009-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40009-4_16

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40008-7

  • Online ISBN: 978-3-642-40009-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation