Drugscan

Tina Ha, Leonie Abbott, Nadia Mouchaileh, Melanie Morrow, Matthew Rawlins, Mark Naunton, Megumi Ng, Rachael Worthington, Rosemary Burke, Debbie Rigby

Research output: Contribution to journalReview article

Abstract

In this paper we present new randomized and deterministic algorithms for the classical problem of broadcasting in radio networks with unknown topology. We consider directed n-node radio networks with specified eccentricity D (maximum distance from the source node to any other node). Our first main result closes the gap between the lower and upper bound: we describe an optimal randomized broadcasting algorithm whose running time complexity is O(D log(n/D) + log2n), with high probability. In particular, we obtain a randomized algorithm that completes broadcasting in any n-node radio network in time O(n), with high probability. The main source of our improvement is a better "selecting sequence" used by the algorithm that brings some stronger property and improves the broadcasting time. Next, we demonstrate how to apply our approach to deterministic broadcasting, and describe a deterministic oblivious algorithm that completes broadcasting in almost optimal time O(n log2D). Finally, we show how our randomized broadcasting algorithm can be used to improve the randomized complexity of the gossiping problem.
Original languageEnglish
Pages (from-to)384-396
Number of pages14
JournalJournal of Pharmacy Practice and Research
Volume46
DOIs
Publication statusPublished - Jun 2016

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    Ha, T., Abbott, L., Mouchaileh, N., Morrow, M., Rawlins, M., Naunton, M., Ng, M., Worthington, R., Burke, R., & Rigby, D. (2016). Drugscan. Journal of Pharmacy Practice and Research, 46, 384-396. https://doi.org/10.1002/jppr.1301