Definition
Brownian motion is the random motion of particles, e.g., molecules, suspended in the fluid medium, e.g., liquid and gas, that results from a large number of collisions those particles experience with the fast-moving particles of the fluid medium.
Acronyms
- BM:
-
Brownian motion
- MC:
-
Molecular communication
- MRBP:
-
Molecule-receptor binding process
- RN:
-
Receiving nanomachine
- TN:
-
Transmitting nanomachine
- VRV:
-
Virtual reception volume
Historical Background
Brownian motion (BM) is an important phenomenon that is the basis of diffusion-based propagation of molecules in molecular communication (MC) and, therefore, is the fundamental principle behind diffusion-based MC in the field of nanoscale communication networks, also known as nanonetworks. In the field of natural and applied sciences, BM is also popularly known as random walk motion of particles. The history of BM is quite old....
References
Ahmadzadeh A, Jamali V, Noel A, Schober R (2017) Diffusive mobile molecular communications over time-variant channels. IEEE Commun Lett 21(6):1265–1268
Ahmadzadeh A, Jamali V, Schober R (2018) Stochastic channel modeling for diffusive mobile molecular communication systems. IEEE Trans Commun 66(12):6205–6220
Akyildiz IF, Brunetti F, Blazquez C (2008) Nanonetworks: a new communication paradigm. Comput Netw J (Elsevier) 52:2260–2279
Atakan B, Akan OB (2010) Deterministic capacity of information flow in molecular nanonetworks. Nano Commun Netw 1(1):31–42
Berg HC (1993) Random walks in biology. Princeton University Press, Princeton
Berg HC, Purcell EM (1977) Physics of chemoreception. Biophys J 20(2):193–219
Brown R (1828) A brief account of microscopical observations. Philos Mag 4:161–173
Bush SF (2010) Nanoscale communication networks. Artech House, Boston
Cao TN, Trinh DP, Jeong Y, Shin H (2015) Anomalous diffusion in molecular communication. IEEE Commun Lett 19(10):1674–1677
Crank J (1975) The mathematics of diffusion. Clarendon Press, Oxford, UK
Culbertson CT, Jacobson SC, Michael Ramsey J (2002) Diffusion coefficient measurements in microfluidic devices. Talanta 56(2):365–373
Delgado J, Alves M, Guedes de Carvalho J (2005) A simple and inexpensive technique to measure molecular diffusion coefficients. J Phase Equilib Diffus 26(5):447–451
Eckford AW (2007) Nanoscale communication with Brownian motion. In: 2007 41st annual conference on information sciences and systems, 14–16 Mar 2007
Einstein A (1905) On the movement of small particles suspended in stationary liquids required by the molecular-kinetic theory of heat. Ann Phys 17:549–560
Felicetti L, Femminella M, Reali G (2013) Simulation of molecular signaling in blood vessels: software design and application to atherogenesis. Nano Commun Netw 4(3):98–119
Freitas RA (1999) Nanomedicine, Vol. 1: Basic capabilities, 1st edn. Landes Bioscience, Austin
Gohari A, Mirmohseni M, Nasiri-Kenari M (2016) Information theory of molecular communication: directions and challenges. IEEE Trans Mol Biol Multi-Scale Commun 2(2):120–142
Gul E, Atakan B, Akan OB (2010) NanoNS: a nanoscale network simulator framework for molecular communications. Nano Commun Netw 1(2):138–156
Haselmayr W, Aejaz SMH, Asyhari AT, Springer A, Guo W (2017) Transposition errors in diffusion-based mobile molecular communication. IEEE Commun Lett 21(9):1973–1976
Höfling F, Franosch T (2013) Anomalous transport in the crowded world of biological cells. Rep Prog Phys 76(4):046602
IEEE (2016) IEEE recommended practice for nanoscale and molecular communication framework. IEEE Std 1906.1-2015, pp 1–64
Jamali V, Ahmadzadeh A, Wicke W, Noel A, Schober R (2018) Channel modeling for diffusive molecular communication – a tutorial review. In: The proceedings of IEEE (submitted to)
Kadloor S, Adve RS, Eekford AW (2012) Molecular communication using Brownian motion with drift. IEEE Trans Nanobioscience 11(2):89–99
Koo B, Lee C, Yilmaz HB, Farsad N, Eckford A, Chae C (2016) Molecular MIMO: from theory to prototype. IEEE J Sel Areas Commun 34(3):600–614
Llatser I, Demiray D, Cabellos-Aparicio A, Altilar DT, Alarcón E (2014) N3Sim: simulation framework for diffusion-based molecular communication nanonetworks. Simul Model Pract Theory 42:210–222
Mahfuz MU, Makrakis D, Mouftah HT (2014) A comprehensive study of sampling-based optimum signal detection in concentration-encoded molecular communication. IEEE Trans Nanobioscience 13(3):208–222
Mahfuz MU, Makrakis D, Mouftah HT (2016) Concentration-encoded subdiffusive molecular communication: theory, channel characteristics, and optimum signal detection. IEEE Trans Nanobioscience 15(6):533–548
Mai TC, Egan M, Duong TQ, Renzo MD (2017) Event detection in molecular communication networks with anomalous diffusion. IEEE Commun Lett 21(6):1249–1252
Metzler R, Klafter J (2000) The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys Rep 339(1):1–77
Nakano T, Okaie Y, Jian-Qin L (2012) Channel model and capacity analysis of molecular communication with Brownian motion. IEEE Commun Lett 16(6):797–800
Nakano T, Eckford AW, Haraguchi T (2013) Molecular communication. Cambridge University Press, Cambridge, UK
Nakano T, Suda T, Okaie Y, Moore M, Vasilakos A (2014) Molecular communication among biological nanomachines: a layered architecture and research issues. IEEE Trans Nanobioscience 13:169
Noel A, Cheung KC, Schober R, Makrakis D, Hafid A (2017) Simulating with AcCoRD: actor-based communication via reaction–diffusion. Nano Commun Netw 11:44–75
Pierobon M, Akyildiz IF (2010) A physical end-to-end model for molecular communication in nanonetworks. IEEE J Sel Areas Commun 28(4):602–611
Pierobon M, Akyildiz IF (2011) Noise analysis in ligand-binding reception for molecular communication in nanonetworks. IEEE Trans Signal Process 59(9):4168–4182
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Mahfuz, M.U. (2019). Brownian Motion. In: Shen, X., Lin, X., Zhang, K. (eds) Encyclopedia of Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-32903-1_231-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-32903-1_231-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32903-1
Online ISBN: 978-3-319-32903-1
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering