Performance metrics for self-positioning autonomous MANET nodes

Authors

  • Janusz Kusyk The United States Patent and Trademark Office, Alexandria, VA, USA
  • Jianmin Zou The Department of Electrical Engineering, The City College of New York, NY,USA
  • Stephen Gundry The Department of Electrical Engineering, The City College of New York, NY,USA
  • Cem Safak Sahin BAE Systems – AIT, Burlington, MA, USA
  • M. ̈Umit Uyar The Department of Electrical Engineering, The City College of New York, NY,USA

DOI:

https://doi.org/10.13052/jcsm2245-1439.223

Keywords:

Topology control, MANETs, node-spreading, uniformity measures, Voronoi tessellation, area coverage, game theory, bio-inspired algorithms

Abstract

We present quantitative techniques to assess the performance of mobile ad hoc network (MANET) nodes with respect to uniform distribution, the total terrain covered by the communication areas of all nodes, and distance traveled by each node before a desired network topology is reached.Our uniformity metrics exploit information from a Voronoi tessellation generated by nodes in a deployment territory.Since movement is one of the most power consuming tasks that mobile nodes execute, the average distance traveled by each node (ADT) before the network reaches its final distribution provides an important performance assessment tool for power-aware MANETs.Another performance metric, network area coverage (NAC) achieved by all nodes, can demonstrate how efficient the MANET nodes are in maximizing the area of operation.Using these metrics, we evaluate our node-spreading bio-inspired game (BioGame), that combines our force-based geneticalgorithm (FGA) and game theory to guide autonomous mobile nodes in making movement decisions.Our simulation experiments demonstrate that these performance evaluation metrics are good indicators for assessing the efficiency of node distribution methods.

Downloads

Download data is not yet available.

Author Biographies

Janusz Kusyk, The United States Patent and Trademark Office, Alexandria, VA, USA

Janusz Kusyk, Ph.D., received B.S. and M.A. degrees in Computer Science from Brooklyn College, Brooklyn, New York in 2002 and 2006, respectively, and he received Ph.D. degree in Computer Science in the Graduate Center, The City University of New York in 2012. Currently, he is a Patent Examinerat USPTO, Alexandria, VA. His research interests are in the areas of network modeling and analysis and applications of game theory and genetically inspired algorithms to wireless networks and distributed robotics.

Jianmin Zou, The Department of Electrical Engineering, The City College of New York, NY,USA

Jianmin Zou received his B.S. degrees in both Computer Science and Chemical Engineering from Huazhong University of Science and Technology, P. R. of China in 2009. He is currently a Ph.D. candidate at the City College of New York (CCNY) of the City University of New York(CUNY). His interests include wireless mobile ad hoc networks, underwater sensor networks, biologically inspired algorithms and game theory.

Stephen Gundry, The Department of Electrical Engineering, The City College of New York, NY,USA

Stephen Gundry received two Bachelor of Science degrees in both Engineering Science and Physics from the City University of New York at the College of Staten Island (CSI), in 2003, and a Master of Engineering degree in Electrical Engineering from the City University of New York at the City College of New York (CCNY), in 2009 and is currently a Ph.D.candidate at this institution. His interests include biologically inspired algorithms, artificial intelligence, game theory and mobile ad hoc networks.

Cem Safak Sahin, BAE Systems – AIT, Burlington, MA, USA

Cem Safak Sahin, Ph.D., received his B.S. degree from Gazi University,Turkey in 1996, M.S. degree from Middle East Technical University, Turkey in 2000, and M. Phil. and Ph.D. degrees from the City University of New York in 2010, all in Electrical Engineering. Until 2004 he was an engineer at Roketsan Inc., a leading defense company of Turkey’s rocket and missile research and production programs. From 2004 to 2008, he was Principal Engineer, Systems Design at Mikes Inc., a defense company specializing in Electronic Warfare Systems, working as part of a multi-national defense project in the United States. He was Senior Software Engineer from 2008 to 2010 in Elanti System. Currently, he is Senior Research Engineer at BAE Systems-AIT in Burlington, MA. His interests include wireless ad-hoc networks, bio-inspired algorithms, communication theory, multi-sensor fusion, algorithm development, artificial intelligence, machine learning, and electronic warfare systems.

M. ̈Umit Uyar, The Department of Electrical Engineering, The City College of New York, NY,USA

M. ̈Umit Uyar, Ph.D., is a Professor with the Electrical Engineering Department of the City College and the Computer Science Department of the Graduate Center of the City University of New York. His interests include bio-inspired computation with applications to the mobile ad hoc networks,distributed robotics tasks and cancer chemotherapy treatment decision support systems. Dr. Uyar was the lead principle investigator for several large grants from U.S. Army and NSF to conduct research on knowledge sharing mobile agents using bio-inspired algorithms for topology control in MANETs and for a smart robot brain on FPGA which has reliable communication capabilities. Prior to joining academia, he was a Distinguished Member of Technical Staff at AT&T Bell Labs until 1993. He is an IEEE Fellow and holds six U.S. patents. Dr. M. Umit Uyar has a B.S. degree from Istanbul Technical University, and M.S. and Ph.D. degrees from Cornell University, Ithaca, NY, all in electrical engineering.

References

C. Abbas, M. Dzulkifli, and A. Azizah. Exploiting Voronoi diagram properties in face segmentation and feature extraction. Pattern Recognition, 41(12):3842–3859, 2008.

B. A. Bash and P. J. Desnoyers. Exact distributed Voronoi cell computation in sensor networks. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN’07), pages 236–243. ACM, New York, 2007.

J. Bernauer, R. P. Bahadur, F. Rodier, J. Janin, and A. Poupon. DiMoVo: a Voronoitessellation-based method for discriminating crystallographic and biological protein-protein interactions. Bioinformatics, 24(5):652–658, March 2008.

J. B. D. Cabrera, R. Ramanathan, C. Gutierrez, and R. K. Mehra. Stable topology control for mobile ad-hoc networks. Communications Letters, IEEE, 11(7):574 –576, july 2007.

U. K. Chakraborty, S. K. Das, and U. T. E. Abbott. Clustering in mobile ad hoc net-works with differential evolution. In Proceedings 2011 IEEE Congress on Evolutionary Computation (CEC), pages 2223–2228, June 2011.

J. Cort ́es, S. Martinez, T. Karatas, and F. Bullo. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20(2):243–255, April 2004.

Q. Du, V. Faber, and M. Gunzburger. Centroidal voronoi tessellations: Applications and algorithms. Society for Industrial and Applied Mathematics Review, 41(4):637–676,1999.

D. Fudenberg and J. Tirole. Game Theory. The MIT Press, August 1991.

B. A. Garro, H. Sossa, and R. A. Vazquez. Path planning optimization using bio-inspirited algorithms. In MICAI’06: Proceedings of the Fifth Mexican International Conference on Artificial Intelligence, pages319–330. IEEE Computer Society, Washington,DC, USA, 2006.

W. George and Barlow. Hexagonal territories. Animal Behaviour, 22, Part 4:876–IN1,1974.

S. Gundry, J. Kusyk, J. Zou, C. S. Sahin, and M. U. Uyar. Performance evaluation of differential evolution based topology control method for autonomous MANET nodes.In Proceedings IEEE Symposium on Computers and Communications (ISCC), pages228–233, May 2012.

S. Gundry, J. Zou, J. Kusyk, J. Zou, C. S. Sahin, and M. U. Uyar. Markov chain model for differential evolution based topology control in MANETs. In Proceedings IEEE Sarnoff Symposium, pages 1–5, May 2012.

J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge,MA, April 1992.

A. Howard, M. J. Mataric, and G. S. Sukhatme. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Proceedings Distributed Autonomous Robot Systems 5, pages 299–308, 2002.

J. Kusyk, C. S. Sahin, M. U. Uyar, E. Urrea, and S. Gundry. Self organization of nodes in mobile ad hoc networks using evolutionary games and genetic algorithms. Journal of Advanced Research. Elsevier, 2:253–264, July 2011.

J. Kusyk, C. S. Sahin, J. Zou, S. Gundry, E. Urrea, and M. U. Uyar. Game theoretic and bio-inspired optimization approach for autonomous movement of MANET nodes.In Handbook of Optimization, from Classical to Modern Approach, Intelligent Systems Reference Library, Vol. 38, Springer, 2011.

J. Kusyk, E. Urrea, C. S. Sahin, and M. U. Uyar. Game theory and genetic algorithm based approach for self positioning of autonomous nodes. International Journal of AdHoc & Sensor Wireless Networks. Old City Publishing, 16(1-3):93–118, 2012.

J. Kusyk, J. Zou, S. Gundry, C. S. Sahin, and M. U. Umit. Techniques for performance evaluation of self-positioning autonomous MANET nodes. In Proceedings IEEE Sarnoff Symposium, pages 1–5, May 2012.

L. Lu, B. L ́evy, and W. Wang. Centroidal Voronoi tessellations for line segments andgraphs. In Computer Graphics Forum (Eurographics Conf. Proc.), 2012.

S. Luke, C. Cioffi-Revilla, L. Panait, K. Sullivan, and G. Balan. MASON: A multi agent simulation environment. Simulation, 81(7):517–527, 2005.

M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, 1998.

H. Nguyen, J. Burkardt, M. Gunzburger, L. Ju, and Y. Saka. Constrained CVT meshesand a comparison of triangular mesh generators. Computetional Geometry, 42(1):1–19,January 2009.

A. Okabe, B. Boots, and Ks Sugihara. Spatial Tesselations: Concepts and Applications of Voronoi Diagrams, Wiley Series in Probability and Mathematica lStatistics. Wiley,1992.

C. S. Sahin. Genetic Algorithms for Topology Control Problems. LAP LAMBERT Academic Publishing, February 2011.

C. S. Sahin, E. Urrea, M. U. Uyar, M. Conner, I. Hokelek, G. Bertoli, and C. Pizzo.Genetic algorithms for self-spreading nodes in MANETs. In Proceedings of the 10thAnnual Conference on Genetic and Evolutionary Computation (GECCO), pages 1141–1142, 2008.

J. Toutouh and E. Alba. Optimizing OLSR in VANETS with differential evolution: a comprehensive study. In Proceedings of the First ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet’11),pages 1–8. ACM, New York, 2011.

E. Urrea, C. S. Sahin, I. Hokelek, M. U. Uyar, M. Conner, G. Bertoli, and C. Pizzo.Bio-inspired topology control for knowledge sharing mobile agents. Ad Hoc Networks,7(4):677–689, 2009.

A. R. Vahdat, A. N. Nour Ashrafoddin, and S. S. Ghidary. Mobile robot global localization using differential evolution and particle swarm optimization. In Proceedings IEEECongress on Evolutionary Computation (CEC2007), pages 1527–1534, Sept. 2007.

Y. Wang, L. Cao, and T. A. Dahlberg. Efficient fault tolerant topology control for three-dimensional wireless networks. In Proceedings of 17th International Conference on Computer Communications and Networks (ICCCN ’08), pages 1–6, Aug. 2008.

J. Zou, S. Gundry, J. Kusyk, M. U. Uyar, and C. S. Sahin. 3D genetic algorithms for underwater sensor networks. International Journal of Ad Hoc and Ubiquitous Computing,13(1):10–22, 2013.

Downloads

Published

2013-07-25

How to Cite

1.
Kusyk J, Zou J, Gundry S, Sahin CS, Uyar M̈. Performance metrics for self-positioning autonomous MANET nodes. JCSANDM [Internet]. 2013 Jul. 25 [cited 2025 Feb. 12];2(2):151-73. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/6139

Issue

2013: Vol 2 Iss 2

Section

Articles