Event Detection in Molecular Communication Networks with Anomalous Diffusion

Trang C. Mai, Malcolm Egan, Quang Duong, Marco Di Renzo

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
365 Downloads (Pure)

Abstract

A key problem in nanomachine networks is how information from sensors is to be transmitted to a fusion center. In this letter, we propose a molecular communication-based event detection network. In particular, we develop a detection framework that can cope with scenarios where the molecules propagate according to anomalous diffusion instead of the conventional Brownian motion. We propose an algorithm for optimizing the network throughput by exploiting tools from reinforcement learning. Our algorithms are evaluated with the aid of numerical simulations, which demonstrate the trade-offs between the performance and complexity.
Original languageEnglish
Pages (from-to)1249-1252
JournalIEEE Communications Letters
Volume21
Issue number6
Early online date15 Feb 2017
DOIs
Publication statusPublished - Jun 2017

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