Challenging benchmark for location discovery in ad hoc networks: foundations and applications

  • Authors:

  • Davood Shamsi;Farinaz Koushanfar;Miodrag Potkonjak

  • Affiliations:

  • Rice University, Houston, TX, USA;Rice University, Houston, TX, USA;University of California, Los Angeles, Los Angeles, CA, USA

  • Venue:
  • Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We have created the first comprehensive and challenging benchmark data set for the ad-hoc location discovery (LD). The benchmark is a collection of representative real-life distance measurement data that establishes a common basis for understanding, characterization, evaluation and comparison of the LD algorithms and solvers. It is constructed using a novel analysis methodology that systematically establishes the difficulty of discovering the locations. Presence of measurement noise renders the problem difficult even in dense networks. The noise impacts the continuous optimization underlying the LD calculations. We focus on the difficulty of node localization in dense networks. In such networks, the location calculation is viewed as a continuous optimization problem instance with an objective function and a set of constraints. We devise a number of new metrics that evaluate the difficulty of the continuous optimization based on the data set properties. For the LD optimization, a fast simulation methodology is devised for rapid analysis of the sensitivity of the goodness with respect to the data set properties. We present a number of applications for the benchmark data and use it for evaluation and comparison of six popular LD algorithms. The LD benchmarks are publicly available at: http://www.ece.rice.edu/~mm7/benchLD/.