Experimental Evaluation of Distributed Node Coloring Algorithms for Wireless Networks

TL;DR

This paper evaluates distributed node coloring algorithms for wireless networks under realistic SINR interference, demonstrating that Rand4DColor is notably fast and robust, with practical improvements enhancing performance in static and dynamic scenarios.

Contribution

The paper provides a comprehensive experimental comparison of recent and classical coloring algorithms in the SINR model, introducing practical improvements and analyzing robustness in mobile networks.

Findings

Rand4DColor is very fast, completing coloring in less than one third of the time for local broadcasting.

Rand4DColor is 4 to 5 times faster than other O(Degree)-coloring algorithms.

ColorRed outperforms the Yu et al. algorithm in (Degree+1)-coloring scenarios.

Abstract

In this paper we evaluate distributed node coloring algorithms for wireless networks using the network simulator Sinalgo [by DCG@ETHZ]. All considered algorithms operate in the realistic signal-to-interference-and-noise-ratio (SINR) model of interference. We evaluate two recent coloring algorithms, Rand4DColor and ColorReduction (in the following ColorRed), proposed by Fuchs and Prutkin in [SIROCCO'15], the MW-Coloring algorithm introduced by Moscibroda and Wattenhofer [DC'08] and transferred to the SINR model by Derbel and Talbi [ICDCS'10], and a variant of the coloring algorithm of Yu et al. [TCS'14]. We additionally consider several practical improvements to the algorithms and evaluate their performance in both static and dynamic scenarios. Our experiments show that Rand4DColor is very fast, computing a valid (4Degree)-coloring in less than one third of the time slots required for local broadcasting, where Degree is the maximum node degree in the network. Regarding other O(Degree)-coloring algorithms Rand4DColor is at least 4 to 5 times faster. Additionally, the algorithm is robust even in networks with mobile nodes and an additional listening phase at the start of the algorithm makes Rand4DColor robust against the late wake-up of large parts of the network. Regarding (Degree+1)-coloring algorithms, we observe that ColorRed it is significantly faster than the considered variant of the Yu et al. coloring algorithm, which is the only other (Degree+1)-coloring algorithm for the SINR model. Further improvement can be made with an error-correcting variant that increases the runtime by allowing some uncertainty in the communication and afterwards correcting the introduced conflicts.