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Reseach Article

Efficient Cooperative Control System for pH Sensitive Nanorobots in Drug Delivery

by S Y Ahmed, S E Amin, T Alarif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 1
Year of Publication: 2014
Authors: S Y Ahmed, S E Amin, T Alarif
10.5120/18042-8947

S Y Ahmed, S E Amin, T Alarif . Efficient Cooperative Control System for pH Sensitive Nanorobots in Drug Delivery. International Journal of Computer Applications. 103, 1 ( October 2014), 39-43. DOI=10.5120/18042-8947

@article{ 10.5120/18042-8947,
author = { S Y Ahmed, S E Amin, T Alarif },
title = { Efficient Cooperative Control System for pH Sensitive Nanorobots in Drug Delivery },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 1 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number1/18042-8947/ },
doi = { 10.5120/18042-8947 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:28.380059+05:30
%A S Y Ahmed
%A S E Amin
%A T Alarif
%T Efficient Cooperative Control System for pH Sensitive Nanorobots in Drug Delivery
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 1
%P 39-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a control algorithm is proposed for pH sensitive nanorobots to deliver drugs in the tumor area while navigating in the blood stream environment. The nanorobots are able to communicate with their neighbors using the Particle Swarm Optimization algorithm. Furthermore, the obstacle avoidance algorithm allows the nanorobots to avoid collision with the blood cells. Each nanorobot can measure the pH value at its current position using sensors. Through cooperation, the nanorobots can drive the swarm to the tumor, which is defined by a certain pH value (less than 7. 4). When the nanorobots locate the tumor cells, they release the drug which will raise the pH value of the cell until it is destroyed. The graphical interface simulations have shown the effectiveness of the proposed algorithm.

References
  1. Adriano Cavalcanti, Robert A. Freitas, Jr. and Luiz C. Kretly, 2004, Nanorobotics Control Design: A Practical Approach Tutorial, ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
  2. Bonabeau, E. , Dorigo, M. , Theraulaz, G. ,1999. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York.
  3. Khin Haymar Saw Hla, YoungSik Choi, Jong Sou Park, 2008. Obstacle Avoidance Algorithm for Collective Movement in Nanorobots, IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 11.
  4. A. Cavalcanti and R. Freitas. Autonomous multi-robot sensor-based cooperation for nanomedicine. International Journal of Nonlinear Science and Numerical Simulation, August 2002.
  5. A. Cavalcanti and R. Freitas. Nanorobotics control design: A collective behavior approach for medicine. IEEE Transactions on Nanobioscience, June 2005.
  6. A. Cavalcanti, B. Shirinzadeh, R. Freitas, and T. Hogg. Nanorobot architecture for medical target identification. Nanotechnology, January 2008.
  7. A. Cavalcanti, B. Shirinzadeh, T. Fukuda, and S. Ikeda. Nanorobot for brainaneurysm. The International Journal of Robotics Research, April 2009.
  8. M. Lewis and G. Bekey. The behavioral self-organization of nanorobots using local rules. In Proceedings of IEEE International Conference on Intelligent Robots and Systems, 1992.
  9. S. Chandrasekaran and D. Hougen. Swarm intelligence for cooperation of bionano robots using quorum sensing. In Bio Micro and Nanosystems Conference, San Francisco, CA, June 2006.
  10. K. Engin, D. Leeper, J. Cater, A. Thistlethwaite, L. Tupchong, and J. Mcfarlane. Extracellular ph distribution in human tumors. International Journal of Hyperthermia, 1995.
  11. Dhariwal, A. , Sukhatme, G. S. , Requicha, A. A. G. : Bacterium-inspired Robots for Environmental Monitoring. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, 2004.
  12. A. Ummat, G. Sharma, C. Mavroidis, and A. Dubey. Biomedical Engineering Handbook, chapter Bio-nanorobotics: state of the art and future challenges. CRC Press, 2005.
  13. R. Freitas. Nanomedicine, Volume I: Basic Capabilities. Landes Bioscience, Georgetown, TX, 1999.
  14. Y. Astier, H. Bayley, and S. Howorka. Protein components for nanodevices. Current Opinion in Chemical Biology, 2005.
  15. N. Seeman. From genes to machines: Dna nanomechanical devices. Trends in Biochemical Sciences, 2005.
  16. Y. Cui, Q. Wei, H. Park, and C. Lieber. Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science, 2001.
  17. Eberhart, R. , Kennedy, J. , 1995. A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro machine Human Science, pp. 39–43. IEEE Press, New York.
  18. Siew Chin Neoh1, Norhashimah Morad2, Arjuna Marzuki1, Chee Peng Lim1, Zalina Abdul Aziz, 2009. A Multi-resolution GA-PSO Layered Encoding Cascade Optimization Model, Innovations in Swarm Intelligence.
Index Terms

Computer Science
Information Sciences

Keywords

Biomedical Nanorobots Nanotechnology Particle Swarm Optimization pH Therapy Reynold Number Tumor