Improving the performance guarantee for approximate graph coloring
Journal of the ACM (JACM)
On the complexity of radio communication
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Improved distributed algorithms for coloring and network decomposition problems
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Scheduling algorithms for multihop radio networks
IEEE/ACM Transactions on Networking (TON)
On the Complexity of Distance-2 Coloring
ICCI '92 Proceedings of the Fourth International Conference on Computing and Information: Computing and Information
On the chromatic number of random geometric graphs
Combinatorica
The capacity of wireless networks
IEEE Transactions on Information Theory
Distributed Channel Assignment and Routing in Multiradio Multichannel Multihop Wireless Networks
IEEE Journal on Selected Areas in Communications
Building a reference combinatorial model for MANETs
IEEE Network: The Magazine of Global Internetworking
Proceedings of the 18th annual international conference on Mobile computing and networking
Evaluating Temporal Robustness of Mobile Networks
IEEE Transactions on Mobile Computing
Bridging the Gap between Protocol and Physical Models for Wireless Networks
IEEE Transactions on Mobile Computing
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We model the problem of channel assignment in mobile networks as one of temporal coloring (T-coloring), that is, coloring a time-varying graph. In order to capture the impact of channel re-assignments due to mobility, we model the cost of coloring as C + αA, where C is the total number of colors used and A is the total number of color changes, and α is a user-selectable parameter reflecting the relative penalty of channel usage and re-assignments. Using these models, we present several novel algorithms for temporal coloring. We begin by analyzing two simple algorithms called SNAP and SMASH that take diametrically opposite positions on colors vs re-assignments, and provide theoretical results on the ranges of α in which one outperforms the other, both for arbitrary and random time-varying graphs. We then present six more algorithms that build upon each of SNAP and SMASH in different ways. Simulations on random geometric graphs with random waypoint mobility show that the relative cost of the algorithms depends upon the value of α and the transmission range, and we identify precise values at which the crossovers happen.