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Journal of Sensors
Volume 2016, Article ID 9358358, 15 pages
http://dx.doi.org/10.1155/2016/9358358
Research Article

Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiobjective Discrete Binary Particle Swarm Optimization

1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
3Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

Received 16 December 2015; Revised 21 February 2016; Accepted 28 March 2016

Academic Editor: Liling Fu

Copyright © 2016 Zhen-Lun Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. R. Akhondi, A. Talevski, S. Carlsen, and S. Petersen, “Applications of wireless sensor networks in the oil, gas and resources industries,” in Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA '10), pp. 941–948, Perth, Australia, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. O. World, “Oil & Gas Wireless Sensor Networks,” 2014, http://www.onworld.com/oilandgas/index.html.
  3. X. Xu, H. Tian, J. Fu, Z. Sun, and X. Xu, “Status and design of digital oilfield,” in Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering, pp. 42–44, March 2013.
  4. Y. Pan, L. Xiao, and Y. Zhang, “Remote real-time monitoring system for oil and gas well based on wireless sensor networks,” in Proceedings of the International Conference on Mechanic Automation and Control Engineering (MACE '10), pp. 2427–2429, Wuhan, China, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Dalbro, E. Eikeland, A. J. In't Veld et al., “Wireless sensor networks for off-shore oil and gas installations,” in Proceedings of the 2nd International Conference on Sensor Technologies and Applications (SENSORCOMM '08), pp. 258–263, Cap Estérel, France, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Gogu, D. Nace, A. Dilo, and N. Meratnia, “Review of optimization problems in wireless sensor networks,” in Telecommunications Networks—Current Status and Future Trends, pp. 153–180, InTech, New York, NY, USA, 2012. View at Google Scholar
  7. H. Mahboubi, J. Habibi, A. G. Aghdam, and K. Sayrafian-Pour, “Distributed deployment strategies for improved coverage in a network of mobile sensors with prioritized sensing field,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 451–461, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. F. Barać, Performance Study of using Flooding in Industrial Wireless Sensor Networks, Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden, 2011.
  9. L. Wang, W. Ye, Y. Mao, P. G. Georgiev, H. Wang, and M. Fei, “The node placement of large-scale industrial wireless sensor networks based on binary differential evolution harmony search algorithm,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 3, pp. 955–970, 2013. View at Google Scholar · View at Scopus
  10. X. Cheng, D.-Z. Du, L. Wang, and B. Xu, “Relay sensor placement in wireless sensor networks,” Wireless Networks, vol. 14, no. 3, pp. 347–355, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Pan, Y. Hou, L. Cai, Y. Shi, and X. Shen, “Topology control for wireless sensor networks,” in Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, pp. 286–299, San Diego, Calif, USA, 2003.
  12. J. Tang, B. Hao, and A. Sen, “Relay node placement in large scale wireless sensor networks,” Computer Communications, vol. 29, no. 4, pp. 490–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. E. L. Lloyd and G. Xue, “Relay node placement in wireless sensor networks,” IEEE Transactions on Computers, vol. 56, no. 1, pp. 134–138, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  14. S. Misra, S. D. Hong, G. Xue, and J. Tang, “Constrained relay node placement in wireless sensor networks: formulation and approximations,” IEEE/ACM Transactions on Networking, vol. 18, no. 2, pp. 434–447, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. A. J. Perez, M. A. Labrador, and P. M. Wightman, “A multiobjective approach to the relay placement problem in WSNs,” in Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 475–480, IEEE, Cancun, Mexico, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J.-H. Zhong and J. Zhang, “A multi-objective memetic algorithm for relay node placement in wireless sensor network,” in Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO '11), pp. 159–160, Dublin, Ireland, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. T. M. Chan, K. F. Man, K. S. Tang, and S. Kwong, “A jumping-genes paradigm for optimizing factory WLAN network,” IEEE Transactions on Industrial Informatics, vol. 3, no. 1, pp. 33–43, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. C.-K. Ting, C.-N. Lee, H.-C. Chang, and J.-S. Wu, “Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm,” IEEE Transactions On Systems, Man, And Cybernetics. Part B, Cybernetics, vol. 39, no. 4, pp. 945–958, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Lakshminarasimman, S. Baskar, A. Alphones, and M. W. Iruthayarajan, “Base station placement for dynamic traffic load using evolutionary algorithms,” Wireless Personal Communications, vol. 72, no. 1, pp. 671–691, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Wechtaisong, T. Sutthitep, and C. Prommak, “Multi-objective planning and optimization for base station placement in WiMAX network,” in Proceedings of the 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON '14), pp. 1–4, IEEE, Nakhon Ratchasima, Thailand, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. R. V. Kulkarni, A. Förster, and G. K. Venayagamoorthy, “Computational intelligence in wireless sensor networks: a survey,” IEEE Communications Surveys and Tutorials, vol. 13, no. 1, pp. 68–96, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Yang, A. Wu, and H. Min, “A multi-objective PSO algorithm with transposon and elitist seeding approaches,” in Proceedings of the 6th International Conference on Advanced Computational Intelligence (ICACI '13), pp. 64–69, IEEE, Hangzhou, China, October 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Yang, A. Wu, and H. Min, “A multi-objective discrete PSO algorithm based on enhanced search,” in Proceedings of the 6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC '14), pp. 198–201, Hangzhou, China, August 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, 1995.
  25. J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104–4108, Orlando, Fla, USA, October 1997.
  26. B. Al-Kazemi and C. Mohan, “Discrete multi-phase particle swarm optimization,” in Information Processing with Evolutionary Algorithms, pp. 305–327, Springer, Berlin, Germany, 2005. View at Google Scholar
  27. G. Pampara, N. Franken, and A. P. Engelbrecht, “Combining particle swarm optimisation with angle modulation to solve binary problems,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), pp. 89–96, Edinburgh, Scotland, September 2005. View at Scopus
  28. M. Clerc, “Discrete particle swarm optimization, illustrated by the traveling salesman problem,” in New Optimization Techniques in Engineering, pp. 219–239, Springer, 2004. View at Google Scholar
  29. A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” Microprocessors and Microsystems, vol. 26, no. 8, pp. 363–371, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. W. Pang, K.-P. Wang, C.-G. Zhou, and L.-J. Dong, “Fuzzy discrete particle swarm optimization for solving traveling salesman problem,” in Proceedings of the 4th International Conference on Computer and Information Technology (CIT '04), pp. 796–800, IEEE, September 2004. View at Scopus
  31. W.-N. Chen, J. Zhang, H. S. H. Chung, W.-L. Zhong, W.-G. Wu, and Y.-H. Shi, “A novel set-based particle swarm optimization method for discrete optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 2, pp. 278–300, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. G. Palermo, C. Silvano, and V. Zacearia, “Discrete particle swarm optimization for multi-objective design space exploration,” in Proceedings of the 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools (DSD '08), pp. 641–644, IEEE, Parma, Ohio, USA, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. F. R. Cordeiro, A. G. Silva-Filho, and G. R. Carvalho, “MOPSO applied to architecture tuning with unified second-level cache for energy and performance optimization,” in Proceedings of the 22nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD '10), pp. 103–110, Petropolis, Brazil, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Gong, Q. Cai, X. Chen, and L. Ma, “Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 82–97, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Le Riche, C. Knopf-Lenoir, and R. T. Haftka, “A segregated genetic algorithm for constrained structural optimization,” in Proceedings of the 6th International Conference on Genetic Algorithms, pp. 558–565, July 1995.
  36. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  37. T. M. Chan, K. F. Man, K.-S. Tang, and S. Kwong, “A jumping gene paradigm for evolutionary multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 2, pp. 143–159, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. M. R. Sierra and C. A. C. Coello, “Improving PSO-Based multi-objective optimization using crowding, mutation and ε-dominance,” in Evolutionary Multi-Criterion Optimization: Third International Conference, EMO 2005, Guanajuato, Mexico, March 9–11, 2005. Proceedings, vol. 3410 of Lecture Notes in Computer Science, pp. 505–519, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  39. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Sun, W. Xu, W. Fang, and Z. Chai, “Quantum-behaved particle swarm optimization with binary encoding,” in Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11–14, 2007, Proceedings, Part I, vol. 4431 of Lecture Notes in Computer Science, pp. 376–385, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  41. E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength pareto evolutionary algorithm,” in Proceedings of the Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (EUROGEN '01), pp. 95–100, Athens, Greece, 2001.
  42. W.-S. Geng, Study on Development Effect Evaluation and Well Pattern Thickening in South Songfangtun Oilfield, Northeast Petroleum University, Daqing, China, 2013.
  43. G.-P. Jiang, Optimization Researeh on Adjusting Injection-Produetion System of Xingbei Development Area in Daqing, Zhejiang University, Hangzhou, China, 2008.
  44. J. J. Durillo and A. J. Nebro, “JMetal: a Java framework for multi-objective optimization,” Advances in Engineering Software, vol. 42, no. 10, pp. 760–771, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, “Performance assessment of multiobjective optimizers: an analysis and review,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117–132, 2003. View at Publisher · View at Google Scholar · View at Scopus
  46. G. G. Yen and Z. He, “Performance metric ensemble for multiobjective evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 131–144, 2014. View at Publisher · View at Google Scholar · View at Scopus
  47. E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 1999.
  48. E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999. View at Publisher · View at Google Scholar · View at Scopus
  49. E. Zitzler and S. Künzli, “Indicator-based selection in multiobjective search,” in Proceedings of the 8th International Conference on Parallel Problem Solving Nature (PPSN '04), Lecture Notes in Computer Science, pp. 832–842, Springer, Berlin, Germany, 2004. View at Google Scholar
  50. A. J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J. J. Durillo, and A. Beham, “AbYSS: adapting scatter search to multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 4, pp. 439–457, 2008. View at Publisher · View at Google Scholar · View at Scopus
  51. K. Li, S. Kwong, R. Wang, K.-S. Tang, and K.-F. Man, “Learning paradigm based on jumping genes: a general framework for enhancing exploration in evolutionary multiobjective optimization,” Information Sciences, vol. 226, pp. 1–22, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus