Artículo

Throughput Optimization in Flow-Guided Nanocommunication Networks

Journal ar
IEEE Access
  • Volumen: 8
  • Fecha: 01 enero 2020
  • Páginas: 142875-142891
  • ISSN: 21693536
  • Tipo de fuente: Revista
  • DOI: 10.1109/ACCESS.2020.3013992
  • Tipo de documento: Artículo
  • Editorial: Institute of Electrical and Electronics Engineers Inc.
© 2013 IEEE.Flow-guided nanocommunication networks comprise a new paradigm that enables among other things, new medical applications for monitoring, information gathering, and data transmission inside the human body. Although promising, this type of network is conceived to operate in a very hostile environment (e.g. blood circulatory system) where nano-node mobility, along with reduced coverage hinder successful transmissions. In addition to these concerns, it is necessary to take into account the scarce amount of energy available in nano-nodes and the important limitations to harvesting energy. Thus, it is essential to carefully select network parameters to tailor network design and improve performance or satisfy specific application requirements and objectives. To this end, this article presents an optimization framework to maximize throughput by selecting the appropriate frame size as a function of the available energy, nano-router location, and transmission rate. This optimization is conducted for different scenarios where the study can reveal whether a general setup that can work in almost all situations is desirable or, depending on the application, a pre-setting customization of network parameters before deployment is more advantageous in terms of performance. Maximizing throughput ensures information transmission and establishes the maximum amount of data that a nano-router can receive. These two questions are critical for the development of reliable medical applications that can operate within the capabilities that flow-guided nano-networks can offer. Thus, the main technical contributions of this paper are: i) the extension of the flow-guided nano-network analytical model, and ii) the derivation of a throughput optimization framework for this type of networks, which provides a suitable frame size as a function of different network parameters such as flow velocity, nano-router location, and transmission power.

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