Using novel distributed heuristics on hexagonal connected dominating sets to model routing dissemination

  • Authors:
  • Maria Striki;Anthony McAuley

  • Affiliations:
  • Telcordia Technologies Inc., One Telcordia Drive, Piscataway, NJ;Telcordia Technologies Inc., One Telcordia Drive, Piscataway, NJ

  • Venue:
  • Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
  • Year:
  • 2009

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Abstract

As future wireless networks become more diverse, there are important performance limits when network conditions become more extreme. An important performance limit in dense networks is the scalability of routing and of topology updates dissemination in particular. In previous work [16], the expected performance of diverse approaches to topology update schemes using flat flooding, Multi-Point relays (MPRs), and Connected Dominating Sets (CDSs), has been modeled and analyzed. Analysis showed that one representative of the analyzed CDS models, the CDS-based Hexagon one, offered order of magnitude less overhead in dense networks with a small increase in routing stretch. To our knowledge, other existing distributed CDS heuristics cannot match the performance of this model. In this work, we are advancing the CDS-HEX topology dissemination approach from the limited-scope centralized scenarios with strict symmetrical relays placement to any general dynamic scenario with totally random placement of nodes. We propose novel heuristics to approximate the theoretically optimal CDS-HEX for dynamic environments. We are showing that: a) although the distributed scheme operates sub-optimally compared to its centralized ancestor, it is still superior to the MPR scheme in key metrics, b) the set-up overhead of the more sophisticated distributed CDS-HEX is not significantly higher than this of MPRs, while the steady state overhead of CDS-HEX turns to be lower, and c) simulations verify all the closed analytical formulae and asymptotic results of previous analysis and provide additional insight on metrics that cannot be analytically measured.