Density: A Context Parameter of Ad Hoc Networks

  • Muhammad Hassan Raza
  • Larry Hughes
  • Imran Raza
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

A mobile ad hoc network or MANET is an autonomous collection of mobile nodes that communicate over wireless links. This mobility means that the network topology may change rapidly and unpredictably over time as the nodes move or adjust their transmission and reception parameters. The network is also decentralized, meaning that the nodes must execute message delivery independently of any centralized control [1].

This chapter describes two approaches to determining density in an ad hoc network: a census of nodes and traffic analysis. The remainder of this chapter is as follows. Section 37.2 describes two anthropogenic counting techniques (census and traffic analysis) and explains how these counting techniques may be applied to determine density. Section 37.3 describes the design of the proposed algorithms. Section 37.4 presents an application of the density-determining algorithm to CARP. Section 37.5 presents simulation results and Sect. 37.6 consists of conclusions.


Mobile Node Population Census Density Calculation Test Scenario Node Count 
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.


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  1. 1.
    S. Murthy and J. J. Garcia-Luna-Aceves (1996) An efficient routing protocol for wireless networks. In: ACM Mobile Networks and Applications Journal, Special Issue on Routing in Mobile Communication Networks. Volume 1. No. 2.Google Scholar
  2. 2.
    Yunjung Yi and Mario Gerla (2002) Efficient flooding in ad hoc networks using on-demand (passive) cluster formation. In: Proceedings of MOBIHOC 2002.Google Scholar
  3. 3.
    D. D. Perkins, H. D. Hughes, and C. B. Owen (2002) Factors affecting the performance of ad hoc networks. Communications, 2002. ICC 2002. IEEE International Conference.Google Scholar
  4. 4.
    Y. Tseng et al. (1999) The broadcast storm problem in a mobile ad-hoc network. In: Proceedings of ACM International Conference on Mobile Computing and Networking (MOBICOM).Google Scholar
  5. 5.
    C. K. Toh (2002) Ad hoc wireless networks, protocols and systems. In: Proceeings of IEEE Conference. Prentice Hall, Upper Saddle River, NJ.Google Scholar
  6. 6.
    L. Hughes and Y. Zhang (2004) Self-limiting adaptive protocols for controlled flooding in ad hoc networks. In: Proceedings of Ad-hoc, Mobile, and Wireless Networks. IEEE, Canada.Google Scholar
  7. 7.
    L. Hughes, Y. Zhang, and K. Shumon (2003) Cartesian ad hoc routing protocol. In: Proceedings of Second International Conference, ADHOC-NOW. Springer, Montreal, Canada.Google Scholar
  8. 8.
    A. Durresi, V. Paruchuri, L. Barolli, and Jain Raj (2005) QoS-energy aware broadcast for sensor networks. In: Proceedings of Parallel Architectures, Algorithms and Networks. ISPAN 2005. 8th International Symposium.Google Scholar
  9. 9.
    J. C. Boettcher and L. M. Gaines (2005) Industry Research Using the Economic Census: How to Find It, How to Use It, Greenwood Press, Westport, CT.Google Scholar
  10. 10.
    Ferguson Niels et al. (2003) Practical cryptography. In: Proceedings of IEEE Conference, IEE.Google Scholar
  11. 11.
    Dawn Song, David Wagner, and Xuqing Tian (2001) Timing analysis of keystrokes and timing attacks on SSH. In: Proceedings of 10th USENIX Security Symposium.Google Scholar
  12. 12.
    A. McIntosh et al. (1994) Statistical analysis of CCSN/SS7 traffic data from working CCS subnetworks. IEEE Journal on Selected Areas in Communications, 12(3).Google Scholar
  13. 13.
    George G. Morgan (2004) How to Do Everything with Your Genealogy. McGraw-Hill Professional, New York.Google Scholar
  14. 14.
    Jon D. Fricker and Robert K. Whitford (2004) Fundamentals of Transportation Engineering: A Multimodal Approach. Prentice Hall, Upper Saddle River, NJ.Google Scholar
  15. 15.
    Bob O’Hara and Al Petrick (1999) IEEE 802.11 Handbook, A Designer’s Companion. IEEE Press, Washington, DC.Google Scholar
  16. 16.
    Ranjith S. Jayaram and Injong Rhee (2003) A case for delay-based congestion control for CDMA 2.5G networks. In: Proceedings of International Conference on Ubiquitous Computing. Springer, Seattle.Google Scholar
  17. 17.
    E. Guadagnoli and W. F. Velicer (1988) Relation of sample size to the stability of component patterns. Psychological Bulletin, Volume 103. No. 2:265–275.CrossRefGoogle Scholar
  18. 18.
    C. H. Yu and J. Behrens (1994) Misconceptions in statistical power and dynamic graphics as a remediation. Poster session presented at the Annual Meeting of American Statistical Association. Toronto, Canada. Volume 18. No. 2.Google Scholar
  19. 19.
    I. G. Dambolena (1984) Teaching the central limit theorem through computer simulation. Mathematics and Computer Education: 128–132.Google Scholar
  20. 20.
    Olive J. Dunn and Virginia A. Clark (1987) Applied Statistics: Analysis of Variance and Regression, 2nd Edition. John Wiley and Sons, New York.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Muhammad Hassan Raza
    • 1
  • Larry Hughes
    • 2
  • Imran Raza
    • 3
  1. 1.Department of Engineering Mathematics and InternetworkingDalhousie UniversityHalifaxCanada
  2. 2.Department of Electrical and Computer EngineeringDalhousie UniversityHalifaxCanada
  3. 3.Department of Computer ScienceCOMSATS Institute of Information TechnologyLahorePakistan

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