Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Ad Hoc Networks

Volumn 10, Issue 6, 2012, Pages 1134-1145

  Pradhan, Pyari Mohan ^a   Panda, Ganapati ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BHUBANESWAR   (India)

Author keywords

Multiobjective optimization; Multiobjective particle swarm optimization; Sensor deployment; Sensor network

Indexed keywords

COMMUNICATION NODES; COMPARATIVE STUDIES; COMPROMISED SOLUTION; DATA PROCESSING UNITS; ENERGY EFFICIENT; FULLY CONNECTED NETWORKS; FUZZY DECISION MAKING; HIGH ENERGY; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; MULTI-OBJECTIVE PROBLEM; NETWORK LAYOUT; NODE LOCATION; NON-DOMINATED SORTING GENETIC ALGORITHMS; OPTIMIZATION PROCESS; PARETO FRONT; PERFORMANCE METRICS; SENSOR DEPLOYMENT; WIRELESS SENSOR;

ALGORITHMS; ENERGY EFFICIENCY; FUZZY LOGIC; SENSOR NETWORKS; SENSOR NODES;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84859761228     PISSN: 15708705     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.adhoc.2012.03.001     Document Type: Article

References (26)

1. Y. Zou, K. Chakrabarty, Sensor deployment and target localization based on virtual forces, in: Proceedings of IEEE INFOCOM Conference, vol. 2, pp. 1293-1303.
2. N. Heo, and P.K. Varshney Energy-efficient deployment of intelligent mobile sensor network IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35 2005 78 92
3. S.S. Dhillon, K. Charabarty, Sensor placement for effective coverage and surveillance in distributed sensor networks, in: Proceedings of IEEE Wireless Communication Network Conference, pp. 1609-1614.
4. X. Wu, S. Lei, J. Yang, H. Xu, J. Cho, S. Lee, Swarm based sensor deployment optimization in ad hoc sensor networks, in: ICESS'05, pp. 533-541.
5. X. Wang, S. Wang, and J. Ma Dynamic deployment optimization in wireless sensor networks Intelligent Control and Automation Lecture Notes in Control and Information Sciences vol. 344 2006 Springer Berlin, Heidelberg 182 187
6. S. Slijepcevic, M. Potkonjak, Power efficient organization of wireless sensor networks, in: Proceedings of Fifth International Conference on Commun., vol. 2, pp. 472-476.
7. Z. Li, L. Lei, Sensor node deployment in wireless sensor networks based on improved particle swarm optimization, in: Applied Superconductivity and Electromagnetic Devices, 2009, ASEMD 2009. International Conference on, pp. 215-217.
8. X. Wang, S. Wang, and J.-J. Ma An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment Sensors 7 2007
9. W.-H. Liao, Y. Kao, and Y.-S. Li A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks Expert Systems with Applications 38 2011 12180 12188
10. C. Ozturk, D. Karaboga, and B. Gorkemli Artificial bee colony algorithm for dynamic deployment of wireless sensor network Turkish Journal of Electrical Engineering and Computer Sciences 20 2012
11. C.A. Coello, G.T. Pulido, and M.S. Lechuge Handling multiple objective with particle swarm optimization IEEE Transactions on Evolutionary Computation 8 2004 256 279
12. P.M. Pradhan, V. Baghel, M. Bernard, G. Panda, Energy efficient layout for a wireless sensor network using multiobjective particle swarm optimization, in: Proceedings of IEEE International Advance Computing Conference (IACC 2009), Patiala, India, pp. 65-70.
13. Y. Zou, K. Chakrabarty, Sensor deployment and target localization based on virtual forces, in: Proceedings of IEEE INFOCOM Conf., vol. 2, pp. 1293-1303.
14. A. Elfes Sonar-based real-world mapping and navigation IEEE Journal of Robotics and Automation RA-3 1987 2349 2365
15. T.H. Cormen, et al., Introduction to algorithms, MIT Press, Cambridge, MA, 2001.
16. F.Y. Edgeworth, Mathematical Physics, P. Keagan, London, England, 1881.
17. V. Pareto, Cours DEconomie Politique, P. Keagan, London, England, 1896.
18. D.E. Goldberg Genetic Algorithms in Search Optimization and Machine Learning 1989 Addison-Wesley Publishing Company Reading, Massachusetts
19. K. Deb, S. Agrawal, A. Pratab, T. Meyarivan, A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, Technical Report 200001, 2000.
20. J. Knowles, and D.C. Corne Approximating the nondominated front using the Pareto archived evolution strategy Evolutionary Computation 8 2000 149 172
21. K. Deb Multiobjective Optimization Using Evolutionary Algorithms 2001 Wiley
22. E. Zitzler, K. Deb, and L. Thiele Comparison of multiobjective evolutionary algorithms: empirical results Evolutionary Computation Journal 8 2000 125 148
23. E. Zitzler, L. Thiele, Multiobjective optimization using evolutionary algorithms - a comparative case study, in: Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), Berlin, Germany, pp. 292-301.
24. E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, Ph.D. thesis, 1999.
25. J.R. Schott, Fault Tolerant Design using Single and Multi-criteria Genetic Algorithms, Master's thesis, 1995.
26. B. Panigrahi, V.R. Pandi, R. Sharma, S. Das, and S. Das Multiobjective bacteria foraging algorithm for electrical load dispatch problem Energy Conversion and Management 52 2011 1334 1342