Comparison of the Mobile and Fixed Nano/Micro-Scale Systems by Using Monte Carlo Simulation for Different Viscosity Values

Öz

Nano networks that are defined as a communication of nano-sized devices (Nano Machines) are a new nano/micro-scale system subject. In this study, on the contrary to the literature, a mobile nano network model has been used to analyze the proposed system in a different viscosity environment by using some Physics law. Because it is known that besides the molecules, which transport information between transmitter and receiver, the transmitter and receiver parts of the biological cells can be mobile in the blood or any other fluid media. In addition, the effect of viscosity which is an important part of the environment of the nano-device systems and distance between transmitter and receiver are analyzed detailed in Matlab with analytical and simulation results by comparing the fixed and mobile nano scale systems. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. As is expected, the fraction of received molecules is obtained the highest when the viscosity of the environment and distance are the lowest for both fixed and mobile system models. Also positions of receiver and transmitter show that when the distance of transmitter and receiver increases from the origin, fraction of received molecules decreases.

Anahtar Kelimeler

• Nano networks
• viscosity
• Fick's law
• fraction of received molecules

Kaynakça

1. Arne NA. Niitsoo, T. Edelhäußer, E. Eberlein, N. Hadaschik, C. Mutschler. 2018. “A Deep Learning Approach to Position Estimation from Channel Impulse Responses.” Sensors 1(D):1–23. doi: 10.3390/s19051064.
2. Akkaya, Ali, H. Birkan Yilmaz, Chan Byoung Chae, and Tuna Tugcu. 2015. “Effect of Receptor Density and Size on Signal Reception in Molecular Communication via Diffusion with an Absorbing Receiver.” IEEE Communications Letters 19(2):155–58. doi: 10.1109/LCOMM.2014.2375214.
3. Balevi, Eren, Student Member, Ozgur B. Akan, and Senior Member. 2013. “A Physical Channel Model for Nanoscale Neuro-Spike Communications.” 61(3):1178–87.
4. Barros, Michael Taynnan, Walisson Silva, and Carlos Danilo Miranda Regis. 2018. “The Multi-Scale Impact of the Alzheimer’s Disease in the Topology Diversity of Astrocytes Molecular Communications Nanonetworks.” (October):1–16.
5. Bi, Dadi, Apostolos Almpanis, Adam Noel, Yansha Deng, and Robert Schober. 2021. “A Survey of Molecular Communication in Cell Biology: Establishing a New Hierarchy for Interdisciplinary Applications.” IEEE Communications Surveys and Tutorials 1–51. doi: 10.1109/COMST.2021.3066117.
6. Breki, Alexander, and Michael Nosonovsky. 2018. “Einstein’s Viscosity Equation for Nanolubricated Friction.” Langmuir 34(43):12968–73. doi: 10.1021/acs.langmuir.8b02861.
7. Chang, Ge, Lin Lin, and Hao Yan. 2018. “Adaptive Detection and ISI Mitigation for Mobile Molecular Communication.” IEEE Transactions on Nanobioscience 17(1):21–35. doi: 10.1109/TNB.2017.2786229.
8. Charbonneau, Benoit, Patrick Charbonneau, and Grzegorz Szamel. 2018. “A Microscopic Model of the Stokes-Einstein Relation in Arbitrary Dimension.” Journal of Chemical Physics 148(22). doi: 10.1063/1.5029464.

9. Chouhan, Lokendra, and Prabhat K. Sharma. 2020. “Molecular Communication in Three-Dimensional Diffusive Channel with Mobile Nanomachines.” Nano Communication Networks 24:100296. doi: 10.1016/j.nancom.2020.100296.
10. Er, M. B. 2020. “A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features.” IEEE Access 8.
11. Farsad, Nariman, Andrew W. Eckford, Satoshi Hiyama, and Yuki Moritani. 2012. “On-Chip Molecular Communication: Analysis and Design.” IEEE Transactions on Nanobioscience 11(3):304–14. doi: 10.1109/TNB.2012.2186460.
12. Farsad, Nariman, and Andrea Goldsmith 2018. “Neural Network Detectors for Sequence Detection in Communication Systems.” Electrical Engineering and Systems Science, 1–15.
13. Felicetti, L., M. Femminella, and G. Reali. 2018. “Directional Receivers for Diffusion-Based Molecular Communications.” IEEE Access PP(c):1. doi: 10.1109/ACCESS.2018.2889031.
14. Guidoni, S. E., and C. M. Aldao. 2002. “On Diffusion, Drift and the Einstein Relation.” European Journal of Physics 23(4):395–402. doi: 10.1088/0143-0807/23/4/302.
15. Huang, Shuai, Lin Lin, Weisi Guo, Hao Yan, Juan Xu, and Fuqiang Liu. 2020. “Initial Distance Estimation and Signal Detection for Diffusive Mobile Molecular Communication.” IEEE Transactions on Nanobioscience 19(3):422–33. doi: 10.1109/TNB.2020.2986314.
16. Isik, I., Yilmaz, H. B., Demirkol, I., & Tagluk, M. E. 2020. “Effect of Receiver Shape and Volume on the Alzheimer Disease for Molecular Communication via Diffusion.” IET Nanobiotechnology 14(7):602–8.
17. Isik, Ibrahim, Mehmet Bilal Er, and Mehmet Emin Tagluk. 2020. “Analysis of Half Sphere Receiver Model in Molecular Communication Through Diffusion.” Journal of Physical Chemistry and Functional Materials 3(2):63–67.
18. Iwasaki, Satoru, Jian Yang, and Tadashi Nakano. 2017. “A Mathematical Model of Non-Diffusion-Based Mobile Molecular Communication Networks.” IEEE Communications Letters 21(9):1969–72. doi: 10.1109/LCOMM.2017.2681061.
19. Koca, Caglar, Meltem Civas, Selin M. Sahin, Onder Ergonul, and Ozgur B. Akan. 2021. “Molecular Communication Theoretical Modeling and Analysis of SARS-CoV2 Transmission in Human Respiratory System.” IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 1–11. doi: 10.1109/TMBMC.2021.3071748.
20. Kumar, Sudhir. 2020. “Nanomachine Localization in a Diffusive Molecular Communication System.” IEEE Systems Journal 14(2):3011–14. doi: 10.1109/JSYST.2019.2963790.
21. Lin, Lin, Qian Wu, Maode Ma, and Hao Yan. 2019. “Concentration-Based Demodulation Scheme for Mobile Receiver in Molecular Communication.” Nano Communication Networks 20:11–19. doi: 10.1016/j.nancom.2019.01.003.
22. Moore, Michael John, Tatsuya Suda, and Kazuhiro Oiwa. 2009. “Molecular Communication : Modeling Noise Effects on Information Rate.” 8(2):169–80.
23. Okaie, Yutaka, Shinya Ishiyama, and Takahiro Hara. 2018. “Leader-Follower-Amplifier Based Mobile Molecular Communication Systems for Cooperative Drug Delivery.” 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings 1–6. doi: 10.1109/GLOCOM.2018.8647185.
24. Peskir, Goran. 2003. “On the Diffusion Coefficient: The Einstein Relation and Beyond.” Stochastic Models 19(3):383–405. doi: 10.1081/STM-120023566.
25. Rusinque, Hector, and Gunther Brenner. 2019. “Mass Transport in Porous Media at the Micro- and Nanoscale: A Novel Method to Model Hindered Diffusion.” Microporous and Mesoporous Materials 280(November 2018):157–65. doi: 10.1016/j.micromeso.2019.01.037.
26. Schurwanz, Max, Peter Adam Hoeher, Sunasheer Bhattacharjee, Martin Damrath, Lukas Stratmann, and Falko Dressler. 2021. “Duality between Coronavirus Transmission and Air-Based Macroscopic Molecular Communication.” IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 1–9. doi: 10.1109/TMBMC.2021.3071747.
27. Singh, Satbir, and Hemant Rajveer Singh. 2016. “Molecular Receptor Antennas for Nano Communication : An Overview.” 9028:13–16.
28. Walsh, Frank. 2013. “Protocols for Molecular Communication.” Waterford Institute of Technology.
29. Walter, H., and J. Vreeburg. 1989. Fluid Sciences and Materials Science in Space - a European Perspective. 50.
30. Wu, Guan-sian, and Po-hsuan Tseng. 2021. “A Deep Neural Network-Based Indoor Positioning Method Using Channel State Information.” 290–94.
31. Wu, Qian, Lin Lin, Zhan Luo, and Hao Yan. 2017. “Bit Alignment Scheme for Mobile Receiver in Molecular Communication.” International Conference on Ubiquitous and Future Networks, ICUFN 750–52. doi: 10.1109/ICUFN.2017.7993892.
32. Yilmaz, H. Birkan, and Chan-byoung Chae. 2014. “Simulation Modelling Practice and Theory Simulation Study of Molecular Communication Systems with an Absorbing Receiver.” Simulation Modelling Practice and Theory 49:136–50. doi: 10.1016/j.simpat.2014.09.002.
33. Yilmaz, H. Birkan, Akif Cem Heren, and Tuna Tugcu. 2014. “3-D Channel Characteristics for Molecular Communications with an Absorbing Receiver.” IEEE COMMUNICATIONS LETTERS 3-D 1–4.
34. Yutaka O, Shouhei K, Tadashi N,, Yasushi H, Tokuko H, Takahiro H, 2019. “Methods and Applications of Mobile Molecular Communication.” Proceedings of the IEEE 107(7).