Tree-Based Topologies for Multi-Sink Networks

  • Chiara BurattiEmail author
  • Marco Martalò
  • Roberto Verdone
  • Gianluigi Ferrari
Part of the Signals and Communication Technology book series (SCT)


The connectivity theory studies networks formed by large numbers of nodes distributed according to some statistics over a limited or unlimited region of ℝd, with d=1,2,3, and aims at describing the potential set of links that can connect nodes to each other, subject to some constraints from the physical viewpoint (power budget or radio resource limitations). Connectivity depends on the number of nodes per unit area (nodes’ density) and on the transmit power. The choice of an appropriate transmit power level is an important aspect of network design as it affects network connectivity. In fact, with a high transmit power a large number of nodes are expected to be reached via direct links. On the other hand, a low transmit power would increase the possibility that a given node cannot reach any other node, that is, it is isolated.


Sensor Node Medium Access Control Medium Access Control Protocol Level Sensor Poisson Point Process 
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.


  1. 1.
    R. Verdone, D. Dardari, G. Mazzini, A. Conti, Wireless Sensor and Actuator Networks: Technologies, Analysis and Design (Elsevier, London, 2008)Google Scholar
  2. 2.
    P. Santi, Topology Control in Wireless Ad Hoc and Sensor Networks (Wiley, Chichester, 2005)CrossRefGoogle Scholar
  3. 3.
    C.F. Chiasserini, M.A. Marsan, A distributed self-healing approach to Bluetooth scatternet formation. IEEE Trans. Wirel. Commun. 4(6), 2649–2654 (2005)CrossRefGoogle Scholar
  4. 4.
    Zigbee Alliance,
  5. 5.
    A. Koubaa, M. Alves, E. Tovar, Modeling and worst-case dimensioning of cluster-tree wireless sensor networks, in Proceedings of IEEE International Real-Time Systems Symposium (RTSS), Rio de Janeiro, Brazil (2006), pp. 412–421Google Scholar
  6. 6.
    R. Verdone, C. Buratti, J. Orriss, On the design of tree-based topologies for wireless sensor networks, in Proceedings of IFIP Annual Mediterranean Ad Hoc Networking Workshop (MedHocNet), Lipari Island, Italy (2006)Google Scholar
  7. 7.
    J. Orriss, S. K. Barton, Probability distributions for the number of radio transceivers which can communicate with one another. IEEE Trans. Commun. 51(4), 676–681 (2003)CrossRefGoogle Scholar
  8. 8.
    M. Haenggi, On distances in uniformly random networks. IEEE Trans. Inf. Theory 51(10), 3584–3586 (2005)CrossRefMathSciNetGoogle Scholar
  9. 9.
    P. Santi, D.M. Blough, The critical transmitting range for connectivity in sparse wireless ad hoc networks. IEEE Trans. Mob. Comput. 2(1), 25–39 (2003)CrossRefGoogle Scholar
  10. 10.
    C. Bettstetter, J. Zangl, How to achieve a connected ad hoc network with homogeneous range assignment: an analytical study with consideration of border effects, in Proceedings of International Workshop on Mobile and Wireless Communications Network, Stockholm, Sweden (2002), pp. 125–129Google Scholar
  11. 11.
    C. Bettstetter, On the minimum node degree and connectivity of a wireless multihop network, in Proceedings of ACM Symposium on Mobile Ad Hoc Networks and Comp. (Mobihoc), Lausanne, Switzerland (2002), pp. 80–91Google Scholar
  12. 12.
    A. Fanimokun, J. Frolik, Effects of natural propagation environments on wireless sensor network coverage area, in Proceedings of Southeastern Symposium on System Theory (SSST), Morgantown, WV, USA (2003), pp. 16–18Google Scholar
  13. 13.
    J. Orriss, A. Phillips, S. Barton, A statistical model for the spatial distribution of mobiles and base stations, in Proceedings of IEEE Vehicular Technical Conference (VTC), vol. 1, Los Angeles, CA, USA (1999), pp. 127–130Google Scholar
  14. 14.
    D. Miorandi, E. Altman, Coverage and connectivity of ad hoc networks in presence of channel randomness, in Proceedings of IEEE Conference on Computer Communication (INFOCOM), vol. 1, Miami, FL, USA (2005), pp. 491–502Google Scholar
  15. 15.
    E. Salbaroli, A. Zanella, A connectivity model for the analysis of a wireless ad-hoc network of finite area, in Proceedings of IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON), vol. 3, New York, NY, USA (2006), pp. 756–760Google Scholar
  16. 16.
    IEEE 802.15.4 Std, Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Computer Society Press, pp. 1–679, October 2003, ISBN: 0-7381-3677-5Google Scholar
  17. 17.
    Freescale, Freescale Semiconductor’s MC13192 Developer’s Kit Google Scholar
  18. 18.
    S. Vural, E. Ekici, Probability distribution of multi-hop-distance in one-dimensional sensor networks. ACM Comput. Netw. Int. J. Comput. Telecommun. Netw. 51(13), 3727–3749 (2007)zbMATHGoogle Scholar
  19. 19.
    Bluetooth™, Specificaton of the Bluetooth System, vol. 0–3. (IEEE, 2004),
  20. 20.
    J. Orriss, S.K. Barton, R. Verdone, A hierarchical model for a sensor network, in Proceedings of International Workshop on Wireless, Ad hoc and Sensor Networks (IWWAN), London, UK (2005)Google Scholar
  21. 21.
    C. Buratti, J. Orriss, R. Verdone, On the design of tree-based topologies for multi-sink wireless sensor networks, in Proceedings of IEEE NEWCOM/ACORN Workshop, Vienna, Austria (2006)Google Scholar
  22. 22.
    A. Marcucci, M. Nati, C. Petrioli, A. Vitaletti, Directed diffusion light: low overhead data dissemination in wireless sensor networks, in Proceedings of IEEE Vehicular Technical Conference (VTC), vol. 4, Stockholm, Sweden (2005), pp. 2538–2545Google Scholar
  23. 23.
    F. Fabbri, R. Verdone, A statistical model for the connectivity of nodes in a multi-sink wireless sensor network over a bounded region, in Proceedings of IEEE European Wireless (EW), Prague, Czech Republic (2008), pp. 1–6Google Scholar
  24. 24.
    D. Stoyan, W.S. Kendall, J. Mecke, Stochastic Geometry and Its Applications (Wiley, Chichester, 1995)zbMATHGoogle Scholar
  25. 25.
    B. Bollobas, Random Graphs (Cambridge University Press, Cambridge, 2001)zbMATHGoogle Scholar
  26. 26.
    R. Meester, R. Roy, Continuum Percolation (Cambridge University Press, Cambridge, 1996)zbMATHGoogle Scholar
  27. 27.
    M.D. Penrose, A. Pistztora, Large deviations for discrete and continious percolation. Adv. Appl. Probab. 28(1), 29–52 (1996)CrossRefzbMATHGoogle Scholar
  28. 28.
    M.D. Penrose, On the spread-out limit for bond and continuum percolation. Ann Appl Probab 3(1), 253–276 (1993)CrossRefzbMATHMathSciNetGoogle Scholar
  29. 29.
    M.D. Penrose, On k-connectivity for a geometric random graph. Rand Struct Algorithms 15(2), 145–164 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  30. 30.
    Z. Vincze, R. Vid, A. Vidacs, Deploying multiple sinks in multi-hop wireless sensor networks, in Proceedings of IEEE International Conference on Pervasive Services, Istanbul, Turkey, 55–63 (2007)Google Scholar
  31. 31.
    H. Pishro-Nik, K. Chan, F. Fekri, On connectivity properties of large-scale sensor networks, in Proceedings of IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON). Santa Clara, CA, USA (2004), pp. 498–507Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chiara Buratti
    • 1
    Email author
  • Marco Martalò
    • 2
  • Roberto Verdone
    • 1
  • Gianluigi Ferrari
    • 2
  1. 1.Dipto. Elettronica, Informatica e Sistemistica (DEIS) Università di BolognaBolognaItaly
  2. 2.Dipto. Ingegneria dell’InformazioneUniversità di ParmaParmaItaly

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