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Distance Distribution in Convex n-Gons: Mathematical Framework and Wireless Networking Applications

Abstract

The performance of a wireless network is related to the Euclidean distance distribution between the communication nodes, which in turn depends on network geometry. In this paper, we provide an analytical framework for the description of distance statistics in convex polygons with arbitrary number and length of sides. Simulation results validate the formulation. Comparisons with models in the literature indicate that our approach is a generalization of previous works. Representative examples show the merits of the proposal and assess its applicability in wireless networking. The main contribution of this work is the consideration of polygonal-shaped networks. The obtained formulation reduces the complexity and computational cost of the modeling and simulation of wireless systems and it is more convenient than other approaches when network planning considers n-gonal coverage areas. Moreover, it provides adequate results for the calculation of distance-dependent parameters and performance metrics of wireless networks.

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References

  1. Megerian S., Koushanfar F., Qu G., Veltri G., Potkonjak M. (2002) Exposure in wireless sensor networks: Theory and practical solutions. Wireless Networks 8(5): 443–454

    Article  MATH  Google Scholar 

  2. Ghosh A., Das S. K. (2008) Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing 4(3): 303–334

    Article  MathSciNet  Google Scholar 

  3. Haenggi M., Andrews J. G., Baccelli F., Dousse O., Franceschetti M. (2009) Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE Journal on Selected Areas in Communications 27(7): 1029–1046

    Article  Google Scholar 

  4. Srinivasa S., Haenggi M. (2010) Distance distributions in finite uniformly random networks: Theory and applications. IEEE Transactions on Vehicular Technology 59(2): 940–949

    Article  Google Scholar 

  5. Zhuang, Y., Luo, Y., Cai, L., & Pan, J. (2011). A geometric probability model for capacity analysis and interference estimation in wireless mobile cellular systems. In 54th IEEE global telecommunications conference (GLOBECOM’11), Houston, USA. doi:10.1109/GLOCOM.2011.6134503.

  6. Sinanović, S., Serafimovski, N., Haas, H., & Auer, G. (2008). Maximizing the system spectral efficiency in a decentralised 2-link wireless network. EURASIP Journal on Wireless Communications and Networking. doi:10.1155/2008/867959.

  7. Heinzelman W. B., Chandrakasan A. P., Balakrishnan H. (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1(4): 660–670

    Article  Google Scholar 

  8. Zhuang, Y., Pan, J., & Cai, L. (2010). Minimizing energy consumption with probabilistic distance models in wireless sensor networks. In 29th IEEE international conference on computer communications (INFOCOM’10), San Diego, USA. doi:10.1109/INFCOM.2010.5462073.

  9. Gopakumar, A., & Jacob, L. (2011). Power-aware range-free wireless sensor network localization using neighbor distance distribution. Wireless Communications and Mobile Computing. doi:10.1002/wcm.1113.

  10. Adelantado F., Pérez-Romero J., Sallent O. (2007) Nonuniform traffic distribution model in reverse link of multirate/multiservice WCDMA-based systems. IEEE Transactions on Vehicular Technology 56(5): 2902–2914

    Article  Google Scholar 

  11. Chang S. Y., Wu H.-C. (2011) Statistical analysis for large-scale hierarchical networks using network coding. IEEE Transactions on Vehicular Technology 60(5): 2152–2163

    Article  MathSciNet  Google Scholar 

  12. Baltzis K. B. (2011) A geometric method for computing the nodal distance distribution in mobile networks. Progress in Electromagnetics Research 114: 159–175

    Google Scholar 

  13. Baltzis K. B. (2011) Analytical and closed-form expressions for the distribution of path loss in hexagonal cellular networks. Wireless Personal Communications 60(4): 599–610

    Article  Google Scholar 

  14. Ali A., Latiff L. A., Fisal N. (2010) Simulation-based real-time routing protocol with load distribution in wireless sensor networks. Wireless Communications and Mobile Computing 10(7): 1002–1016

    Google Scholar 

  15. Zhuang, Y., & Pan, J. (2012). A geometrical probability approach to location-critical network performance metrics. In 31st IEEE international conference on computer communications (INFOCOM’12), Orlando, USA. http://grp.pan.uvic.ca/~yyzhuang/hexagon.pdf. Accessed 7 May 2012.

  16. Xiao L., Greenstein L. J., Mandayam N. B., Periyalwar S. (2008) Distributed measurements for estimating and updating cellular system performance. IEEE Transactions on Communications 56(6): 991–998

    Article  Google Scholar 

  17. Choi S.-O., You K.-H. (2008) Channel adaptive power control in the uplink of CDMA systems. Wireless Personal Communications 47(3): 441–448

    Article  Google Scholar 

  18. Mullen J. P. (2003) Robust approximations to the distribution of link distances in a wireless network occupying a rectangular region. Mobile Computing and Communications Review 7(2): 80–91

    Article  Google Scholar 

  19. Pirinen, P. (2006). Outage analysis of ultra-wideband system in lognormal multipath fading and square-shaped cellular configurations. EURASIP Journal on Wireless Communications and Networking. doi:10.1155/WCN/2006/19460.

  20. Bai, X., Kumar, S., Xuan, D., Yun, Z., & Lai, T. H. (2006). Deploying wireless sensors to achieve both coverage and connectivity. In 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’06), Florence, Italy, pp. 131–142.

  21. Zhuang, Y., & Pan, J. (2011). Random distances associated with rhombuses. http://arxiv.org/pdf/1106.2200.pdf. Accessed 7 May 2012.

  22. Melodia T., Pompili D., Akyildiz I. F. (2010) Handling mobility in wireless sensor and actor networks. IEEE Transactions on Mobile Computing 9(2): 160–173

    Article  Google Scholar 

  23. Noori M., Ardakani M. (2011) Lifetime analysis of random event-driven clustered wireless sensor networks. IEEE Transactions on Mobile Computing 10(10): 1448–1458

    Article  Google Scholar 

  24. Li W., Martins P., Shen L. (2012) Determination method of optimal number of clusters for clustered wireless sensor networks. Wireless Communications and Mobile Computing 12(2): 158–168

    Article  Google Scholar 

  25. Ganti R. K., Haenggi M. (2009) Interference and outage in clustered wireless ad hoc networks. IEEE Transactions on Information Theory 55(9): 4067–4086

    Article  MathSciNet  Google Scholar 

  26. Stamatiou K., Proakis J. G., Zeidler J. R. (2010) Channel diversity in random wireless networks. IEEE Transactions on Wireless Communications 9(7): 2280–2289

    Article  Google Scholar 

  27. Alouini M.-S., Goldsmith A. J. (1999) Area spectral efficiency of cellular mobile radio systems. IEEE Transactions on Vehicular Technology 48(4): 1047–1066

    Article  Google Scholar 

  28. Moltchanov D. (2012) Distance distributions in random networks. Ad Hoc Networks 10(6): 1146–1166

    Article  Google Scholar 

  29. Voronoi diagrams. http://www-sop.inria.fr/prisme/fiches/Voronoi/index.html.en. Accessed 7 May 2012.

  30. MathWorld–A Wolfram web resource. Circle-line intersection. http://mathworld.wolfram.com/Circle-LineIntersection.html. Accessed 7 May 2012.

  31. Vizireanu D. N., Halunga S. V. (2011) Single sine wave parameters estimation method based on four equally spaced samples. International Journal of Electronics 98(7): 941–948

    Article  Google Scholar 

  32. Haenggi M., Puccinelli D. (2005) Routing in ad hoc networks: A case for long hops. IEEE Communications Magazine 43(10): 93–101

    Article  Google Scholar 

  33. Bush, S. F. (2005). Low-energy sensor-network time synchronization as an emergent property. In 14th International conference on computer communications and networks (ICCN’05), San Diego, USA, pp. 93–98.

  34. Luo, D., Zuo, D., & Yang, X. An energy-saving routing protocol for wireless sensor networks. In 4th International conference on wireless communications, networking and mobile computing (WiCOM’08), Dalian, China. doi:10.1109/WiCOM.2008.964.

  35. Baltzis K. B. (2010) Closed from description of microwave signal attenuation in cellular systems. Radio Engineering 19(1): 11–16

    Google Scholar 

  36. Rappaport T. S. (2002) Wireless communications: Principles and practice (2nd ed.). Prentice Hall, Upper Saddle River

    Google Scholar 

  37. Agbinya J. I. (2007) Design considerations of MoHotS and wireless chain networks. Wireless Personal Communications 40(1): 91–106

    Article  Google Scholar 

  38. Shankar P. M. (2011) Statistical models for fading and shadowed fading channels in wireless systems: A pedagogical perspective. Wireless Personal Communications 60(2): 191–213

    Article  MathSciNet  Google Scholar 

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Correspondence to Konstantinos B. Baltzis.

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Baltzis, K.B. Distance Distribution in Convex n-Gons: Mathematical Framework and Wireless Networking Applications. Wireless Pers Commun 71, 1487–1503 (2013). https://doi.org/10.1007/s11277-012-0887-9

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  • DOI: https://doi.org/10.1007/s11277-012-0887-9

Keywords

  • Euclidean distance
  • Polygon
  • Distance-related metrics
  • Routing protocol
  • Voronoi diagram
  • Wireless network