Understanding Communication via Diffusion: Simulation Design and Intricacies

  • Bilal Acar
  • Ali Akkaya
  • Gaye Genc
  • H. Birkan Yilmaz
  • M. Şükrü Kuran
  • Tuna TugcuEmail author
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 9)


Understanding Communication via Diffusion (CvD) is key to molecular communications research since it dominates the movement at the nano-scale. The researcher needs to properly understand the random diffusion of the molecules for the analysis of a molecular communication system. This chapter aims explaining the dynamics of diffusion from a communication engineer’s perspective as well as providing useful hints for an effective simulation design by discussing some key intricacies. The chapter starts with a brief survey of simulators for molecular communications, followed by the basics of the simulation of Brownian motion and CvD. Several intricacies are addressed to help the researcher in simulation design, such as the number of replications required in terms of movement and bit sequence. We utilize this information further by discussing the design of more complex CvD systems such as tunnel-based approach that utilizes destroyer molecules and distributed simulator design based on HLA. Introduction of more complex CvD systems provides significant improvements in data rate and communications in general, bridging the gap between human-scale and nano-scale systems and enabling nanonetworking as a viable technology.


Brownian Motion Symbol Duration Messenger Molecule Intersymbol Interference High Level Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by the State Planning Organization (DPT) of Republic of Turkey under the project TAM with the Project Number 2007K120610, Bogazici University Research Fund (BAP) under Grant Number 7436, and by the Scientific and Technical Research Council of Turkey (TUBITAK) under Grant Number 112E011. M. Şükrü Kuran partially carried out the work presented in this paper at LINCS (


  1. 1.
    Modeling and simulation (m & s) high level architecture (hla) (IEEE 1516-2010 series). Accessed 29 Jan 2014
  2. 2.
    N3sim is a simulation framework for diffusion-based molecular communication in nanonetworks. Accessed 29 Jan 2014
  3. 3.
    AbouRizk S, Mohamed Y, Taghaddos H, Saba F, Hague S (2010) Developing complex distributed simulation for industrial plant. In: Proceedings of the 2010 winter simulation conference, pp 3177–3188Google Scholar
  4. 4.
    Akkaya A, Genc G, Tugcu T (2014) HLA based architecture for molecular communication simulation. Simul Modell Pract Theory. doi: 10.1016/j.simpat.2013.12.012,
  5. 5.
    Ananthakrishnan R, Ehrlicher A (2007) The forces behind cell movement. Int J Biol Sci 3(5):303CrossRefGoogle Scholar
  6. 6.
    Berg HC (1993) Random walks in biology. Princeton University PressGoogle Scholar
  7. 7.
    Dahmann JS (1997) High level architecture for simulation. In: First International workshop on distributed interactive simulation and real time applications, 1997, p 32Google Scholar
  8. 8.
    Douglas C, Montgomery GCR (2006) Applied statistics and probability for engineers. WileyGoogle Scholar
  9. 9.
    Felicetti L, Femminella M, Reali G (2012) A simulation tool for nanoscale biological networks. Nano Commun Netw 3(1):2–18CrossRefGoogle Scholar
  10. 10.
    Fujimoto RM (2000) Parallel and distributed simulation systems. WileyGoogle Scholar
  11. 11.
    Genc G, Yilmaz HB, Tugcu T (2013) Reception enhancement with protrusions in communication via diffusion. In: 2013 First International Black Sea conference on communications and networking (BlackSeaCom), pp 89–93. IEEEGoogle Scholar
  12. 12.
    Gul E, Atakan B, Akan OB (2010) Nanons: a nanoscale network simulator framework for molecular communications. Nano Commun Netw 1(2):138–156CrossRefGoogle Scholar
  13. 13.
    Kuran MŞ, Yilmaz HB, Tugcu T (2013) A tunnel-based approach for signal shaping in molecular communication. In: 2013 IEEE international conference on communications workshops (ICC), pp 776–781Google Scholar
  14. 14.
    Kuran MŞ, Yilmaz HB, Tugcu T, Özerman B (2010) Energy model for communication via diffusion in nanonetworks. Nano Commun Netw 1(2):86–95CrossRefGoogle Scholar
  15. 15.
    Llatser I, Pascual I, Garralda N, Cabellos-Aparicio A, Alarcon E (2011) N3sim: a simulation framework for difusion-based molecular communication. In: IEEE Technical Committee on Simulation, vol 8Google Scholar
  16. 16.
    Mattila PK, Lappalainen P (2008) Filopodia: molecular architecture and cellular functions. Nat Rev Mol Cell Biol 9(6):446–454CrossRefGoogle Scholar
  17. 17.
    Moore M, Enomoto A, Nakano T, Suda T, Kayasuga A, Kojima H, Sakakibara H, Oiwa K (2006) Simulation of a molecular motor based communication network. In: Bio-inspired models of network, information and computing systemsGoogle Scholar
  18. 18.
    Redner S (2001) A guide to first-passage processes. Cambridge University PressGoogle Scholar
  19. 19.
    Saxton MJ (2007) Modeling 2d and 3d diffusion. In: Methods in membrane lipids. Springer, pp 295–321Google Scholar
  20. 20.
    Toth A, Banky D, Grolmusz V (2011) 3-d brownian motion simulator for high-sensitivity nanobiotechnological applications. IEEE Trans Nanobiosci 10(4):248–249CrossRefGoogle Scholar
  21. 21.
    Tyrrell HJV, Harris K (1984) Diffusion in liquids. A theoretical and experimental study. Butterworth Publishers, Stoneham, MAGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bilal Acar
    • 1
  • Ali Akkaya
    • 1
  • Gaye Genc
    • 1
  • H. Birkan Yilmaz
    • 1
    • 2
  • M. Şükrü Kuran
    • 3
  • Tuna Tugcu
    • 1
    Email author
  1. 1.NETLAB, Department of Computer EngineeringBogazici UniversityIstanbulTurkey
  2. 2.School of Integrated TechnologyYonsei UniversitySeoulSouth Korea
  3. 3.Abdullah Gul UniversityKayseriTurkey

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