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A simple and scalable receiver model in molecular communication systems

Published: 28 September 2016 Publication History

Abstract

This paper shows a simple although reliable receiver model for diffusion-based molecular communication systems. Indeed, the complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A sample scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in a diseased area. The proposed model tackles the time needed for analyzing such a system by the introduction of an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. Our results demonstrate that the proposed approach substantially matches simulation results achieved through detailed simulations of a large number of receivers by means of BiNS2 simulator, although the time taken for obtaining the results is order of magnitudes lower than the simulation time. We believe that this model is the precursor of novel models based on similar principles that allow realizing reliable simulations of body-wide systems.

References

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A. Akkaya et al. Effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver. IEEE Comm. Letters, 19(2):155--158, Feb 2015.
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U. Chude-Okonkwo et al. Molecular communication model for targeted drug delivery in multiple disease sites with diversely expressed enzymes. IEEE Trans. on NanoBioscience, 15(3):230--245, April 2016.
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L. Felicetti, M. Femminella, and G. Reali. A simulation tool for nanoscale biological networks. Nano Communication Networks, 3(1):2--18, 2012.
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M. Femminella, G. Reali, and A. V. Vasilakos. A molecular communications model for drug delivery. IEEE Trans. on NanoBioscience, 14(8), Dec 2015.
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T. M. Martin, B. J. Wysocki, T. A. Wysocki, and A. K. Pannier. Identifying intracellular pDNA losses from a model of nonviral gene delivery. IEEE Transactions on NanoBioscience, 14(4):455--464, June 2015.
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T. Nakano, Y. Okaie, and A. V. Vasilakos. Transmission rate control for molecular communication among biological nanomachines. IEEE Journal on Selected Areas in Communications, 31(12, suppl.), 2013.
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M. Pierobon and I. Akyildiz. Noise analysis in ligand-binding reception for molecular communication in nanonetworks. IEEE Transactions on Signal Processing, 59(9):4168--4182, September 2011.

Cited By

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  • (2024)On the Reception Process of Molecular Communication-Based Drug DeliveryIEEE Access10.1109/ACCESS.2024.336281212(24217-24231)Online publication date: 2024
  • (2023)Case Studies of Applications of Digital Networks Theories to Molecular Network StacksMolecular Communications10.1007/978-3-031-36882-0_4(167-195)Online publication date: 17-Aug-2023
  • (2022)A Learning Automaton-Based Algorithm for Maximizing the Transfer Data Rate in a Biological NanonetworkApplied Sciences10.3390/app1219949912:19(9499)Online publication date: 22-Sep-2022
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    cover image ACM Other conferences
    NANOCOM'16: Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication
    September 2016
    178 pages
    ISBN:9781450340618
    DOI:10.1145/2967446
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 28 September 2016

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    Author Tags

    1. Diffusion
    2. markovian model
    3. scalable simulation

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    Overall Acceptance Rate 97 of 135 submissions, 72%

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    Cited By

    View all
    • (2024)On the Reception Process of Molecular Communication-Based Drug DeliveryIEEE Access10.1109/ACCESS.2024.336281212(24217-24231)Online publication date: 2024
    • (2023)Case Studies of Applications of Digital Networks Theories to Molecular Network StacksMolecular Communications10.1007/978-3-031-36882-0_4(167-195)Online publication date: 17-Aug-2023
    • (2022)A Learning Automaton-Based Algorithm for Maximizing the Transfer Data Rate in a Biological NanonetworkApplied Sciences10.3390/app1219949912:19(9499)Online publication date: 22-Sep-2022
    • (2022)Message exchange dynamics in wireless biological nanonetworksInternational Journal of Communication Systems10.1002/dac.533035:17Online publication date: 26-Aug-2022
    • (2021)A Simple Queuing Model for Molecular Communications ReceiversSensors10.3390/s2122766421:22(7664)Online publication date: 18-Nov-2021
    • (2021)Saturating Receiver and Receptor Competition in Synaptic DMC: Deterministic and Statistical Signal ModelsIEEE Transactions on NanoBioscience10.1109/TNB.2021.309227920:4(464-479)Online publication date: Oct-2021
    • (2021)Receptor Saturation Modeling for Synaptic DMCICC 2021 - IEEE International Conference on Communications10.1109/ICC42927.2021.9500809(1-6)Online publication date: Jun-2021
    • (2018)Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano ThingsIEEE Transactions on NanoBioscience10.1109/TNB.2018.279243417:1(44-54)Online publication date: Jan-2018
    • (2017)Congestion Control in Molecular Cyber-Physical SystemsIEEE Access10.1109/ACCESS.2017.27075975(10000-10011)Online publication date: 2017

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