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Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

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Evolutionary intelligence has become one of the most important directions that improve the performance and effectiveness of automated systems such as communication systems, robotics and engineering industries. Today, there are many applications of evolutionary intelligence in many engineering fields and the most important fields related to computation and informatics engineering as a part of electrical and communication engineering, as modern engineering applications are involved in these fields. The sensor network is the main data source in the world of smart systems nowadays. Additionally, it has become a field of science used in the development of the rest of scientific applications. The need to use evolutionary intelligence in sensor networks has emerged because of the problems encountered by different types of sensor networks. This paper represents a comprehensive scientific review of the role of evolutionary intelligence in sensor networks and its implications for this important part of engineering applications. This paper discusses the theoretical, mathematical and practical application of evolutionary computing with the use of evolutionary algorithms and the improvements resulting from the application of evolutionary intelligence in sensor networks. The content of this paper will review the most important of the evolutionary intelligence from principles, algorithms and applications. The problems facing the types of sensor network has been solved using evolutionary algorithms. After reviewing the evolutionary intelligence and its details in the sensor network, a performance evaluation is presented in the paper at the end of each of the targeted areas of the sensor network. This performance evaluation represents the measure of the quality of improvements provided by evolutionary intelligence in sensor network field with graphical analysis studies to demonstrate the effect of evolutionary algorithms on the sensor network.

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References

  1. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  3. Yu, X., Wu, P., Han, W., & Zhang, W. (2013). A survey on wireless sensor network infrastructure for agriculture. Computer Standards & Interfaces, 35(1), 59–64.

    Google Scholar 

  4. Wang, P., Hou, H., He, X., Wang, C., Xu, T., & Li, Y. (2015). Survey on application of wireless sensor network in smart grid. Procedia Computer Science, 52, 1212–1217.

    Google Scholar 

  5. Serpen, G., Li, J., & Liu, L. (2013). AI-WSN: Adaptive and intelligent wireless sensor network. Procedia Computer Science, 20, 406–413.

    Google Scholar 

  6. Marsh, D., Tynan, R., O’Kane, D., & O’Hare, G. M. (2004). Autonomic wireless sensor networks. Engineering Applications of Artificial Intelligence, 17(7), 741–748.

    MATH  Google Scholar 

  7. Federici, F., Alesii, R., Colarieti, A., Faccio, M., Graziosi, F., Gattulli, V., et al. (2014). Design of wireless sensor nodes for structural health monitoring applications. Procedia Engineering, 87, 1298–1301.

    Google Scholar 

  8. AL-Mousawi, A. J., & AL-Hassani, H. K. (2017). A survey in wireless sensor network for explosives detection. Computers & Electrical Engineering, 72, 682–701.

    Google Scholar 

  9. Sharma, D., Liscano, R., & Shah-Heydari, S. (2013). Enhancing collection tree protocol for mobile wireless sensor networks. Procedia Computer Science, 21, 416–423.

    Google Scholar 

  10. Tuna, G., Güngör, V. Ç., & Potirakis, S. M. (2015). Wireless sensor network-based communication for cooperative simultaneous localization and mapping. Computers & Electrical Engineering, 41, 407–425.

    Google Scholar 

  11. Benini, L., Farella, E., & Guiducci, C. (2006). Enabling technology for ambient intelligence. Microelectronics Journal, 37(12), 1639–1649.

    Google Scholar 

  12. Rademacher, S., Schmitt, K., & Wöllenstein, J. (2015). Wireless gas sensor network for the spatially resolved measurement of hazardous gases in case of a disaster. Procedia Engineering, 120, 310–314.

    Google Scholar 

  13. Kumar, S. P. L. (2017). State of The art-intense review on artificial intelligence systems application in process planning and manufacturing. Engineering Applications of Artificial Intelligence, 65, 294–329.

    Google Scholar 

  14. Ganesan, D., Cerpa, A., Ye, W., Yan, Y., Zhao, J., & Estrin, D. (2004). Networking issues in wireless sensor networks. Journal of Parallel and Distributed Computing, 64(7), 799–814.

    Google Scholar 

  15. Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303–334.

    Google Scholar 

  16. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Google Scholar 

  17. Serpen, G., Li, J., & Liu, L. (2013). AI-WSN: Adaptive and intelligent wireless sensor network. Procedia Computer Science, 20, 406–413.

    Google Scholar 

  18. Chang, F.-C., & Huang, H.-C. (2016). A survey on intelligent sensor network and its applications. Journal of Network Intelligence, 1(1), 1–5.

    MathSciNet  Google Scholar 

  19. Kulkarni, R. V., Forster, A., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 13(1), 68–96.

    Google Scholar 

  20. Jabbar, S., Iram, R., Minhas, A. A., Shafi, I., Khalid, S., & Ahmad, M. (2013). Intelligent optimization of wireless sensor networks through bio-inspired computing: Survey and future directions. International Journal of Distributed Sensor Networks, 2013, 13, 421084.

    Google Scholar 

  21. Jerison, H. (1973). Evolution of the brain and intelligence (pp. iv–ii). London: Academic Press.

    Google Scholar 

  22. Nguyen, T. T., Yang, S., & Branke, J. (2012). Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6, 1–24.

    Google Scholar 

  23. Ahmed, Y. E. E., Adjallah, K. H., Stock, R., & Babikier, S. F. (2016). Wireless sensor network lifespan optimization with simple, rotated, order and modified partially matched crossover genetic algorithms. IFAC-PapersOnLine, 49(25), 182–187.

    Google Scholar 

  24. Aguilar-Rivera, R., Valenzuela-Rendón, M., & Rodríguez-Ortiz, J. J. (2015). Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications, 42(21), 7684–7697.

    Google Scholar 

  25. Yi, L., & Wanli, K. (2011). A new genetic programming algorithm for building decision tree. Procedia Engineering, 15(2011), 3658–3662.

    Google Scholar 

  26. Cai, J., & Thierauf, G. (1996). Evolution strategies for solving discrete optimization problems. Advances in Engineering Software, 25(2–3), 177–183.

    Google Scholar 

  27. Balkaya, Ç. (2013). An implementation of differential evolution algorithm for inversion of geoelectrical data. Journal of Applied Geophysics, 98, 160–175.

    Google Scholar 

  28. Holmes, J. H., Durbin, D. R., & Winston, F. K. (2000). The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence in Medicine, 19(1), 53–74.

    Google Scholar 

  29. Bensmaine, A., Dahane, M., & Benyoucef, L. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.

    Google Scholar 

  30. Saranya, S., & Princy, M. (2012). Routing techniques in sensor network—A survey. Procedia Engineering, 38, 2739–2747.

    Google Scholar 

  31. Liang, Z., Jianmin, X. U., & Lingxiang, Z. (2007). Application of genetic algorithm in dynamic route guidance system. Journal of Transportation Systems Engineering and Information Technology, 7(3), 45–48.

    Google Scholar 

  32. Gupta, S. K., Kuila, P., & Jana, P. K. (2016). Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Computers & Electrical Engineering, 56, 544–556.

    Google Scholar 

  33. Bhatia, T., Kansal, S., Goel, S., & Verma, A. K. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers & Electrical Engineering, 56, 441–455.

    Google Scholar 

  34. Bayraklı, S., & Erdogan, S. Z. (2012). Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Computer Science, 10, 247–254.

    Google Scholar 

  35. Yan, W., Xin-xin, S., & Yan-ming, S. U. (2011). Study on the application of genetic algorithms in the optimization of wireless network. Procedia Engineering, 16, 348–355.

    Google Scholar 

  36. Gong, G., Liu, Y., & Qian, M. (2001). An adaptive simulated annealing algorithm. Stochastic Processes and their Applications, 94(1), 95–103.

    MathSciNet  MATH  Google Scholar 

  37. Shahi, B., Dahal, S., Mishra, A., Kumar, S. V., & Kumar, C. P. (2016). A review over genetic algorithm and application of wireless network systems. Procedia Computer Science, 78, 431–438.

    Google Scholar 

  38. Bari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7, 665–676.

    Google Scholar 

  39. Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46(2014), 198–226.

    Google Scholar 

  40. Nayebi, A., & Sarbazi-Azad, H. (2011). Performance modelling of the LEACH protocol for mobile wireless sensor networks. Journal of Parallel and Distributed Computing, 71, 812–821.

    MATH  Google Scholar 

  41. Geetha, V., Kallapur, P. V., & Tellajeera, S. (2012). Clustering in wireless sensor networks: Performance comparison of leach & leach-C protocols using ns2. Procedia Technology, 4, 163–170.

    Google Scholar 

  42. Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

    Google Scholar 

  43. Zhou, Y., Li, X., & Gao, L. (2013). A differential evolution algorithm with intersecting mutation operator. Applied Soft Computing, 13(1), 390–401.

    Google Scholar 

  44. Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    MathSciNet  MATH  Google Scholar 

  45. Potthuri, S., Shankar, T., & Rajesh, A. (2016). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing (DESA). Ain Shams Engineering Journal, 9(4), 655–663.

    Google Scholar 

  46. Sumithra, S., & Victoire, T. A. A. (2015). Differential evolution algorithm with diversified vicinity operator for optimal routing and clustering of energy efficient wireless sensor networks. The Scientific World Journal, 2015, 3, 729634.

    Google Scholar 

  47. Raguraman, P., Ramasundaram, M., & Balakrishnan, V. (2018). Localization in wireless sensor networks: A dimension based pruning approach in 3D environments. Applied Soft Computing, 68, 219–232.

    Google Scholar 

  48. Sun, W., & Su, X. (2011). Wireless sensor network node localization based on genetic algorithm. In 2011 IEEE 3rd international conference on communication software and networks (pp. 316–319).

  49. Schmitt, L. M. (2001). Theory of genetic algorithms. Theoretical Computer Science, 259(1–2), 1–61.

    MathSciNet  MATH  Google Scholar 

  50. Carter, J. N. (2003). Chapter 3, Introduction to using genetic algorithms. In M. Nikravesh, F. Aminzadeh, & L. A. Zadeh (Eds.), Developments in petroleum science (Vol. 51, pp. 51–76). Elsevier.

  51. Banzhaf, W. (2001). Artificial intelligence: Genetic programming. In International encyclopedia of the social & behavioral sciences (pp. 789–792). Pergamon.

  52. Tam, V., Cheng, K.-Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. In IEEE consumer communications and networking conference (CCNC) 2006 (pp. 137–141). Las Vegas, NV, USA.

  53. Li, Z., Zhou, X., & Li, S. (2005). Issues of wireless sensor network management. Lecture notes in computer science (pp. 355–36 l).

  54. Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66. https://doi.org/10.1109/2.940014.

    Article  Google Scholar 

  55. Flathagen, J., & Korsnes, R. (2010). Localization in wireless sensor networks based on Ad hoc routing and evolutionary computation. In 2010, Milcom military communications conference, CA (pp. 1062–1067).

  56. Tam, V., Cheng, K. -Y., & Lui, K.-S. (2006). Improving localization in wireless sensor networks with an evolutionary algorithm. CCNC. In 2006 3rd IEEE consumer communications and networking conference, 2006 (pp. 137–141). Las Vegas, NV, USA, 2006.

  57. Mohamed, S. M., Hamza, H. S., & Saroit, I. A. (2017). Coverage in mobile wireless sensor networks (M-WSN): A survey. Computer Communications, 110, 133–150.

    Google Scholar 

  58. Vecchio, M., & López-Valcarce, R. (2015). Improving area coverage of wireless sensor networks via controllable mobile nodes: A greedy approach. Journal of Network and Computer Applications, 48, 1–13.

    Google Scholar 

  59. Li, X. -Y., Wan, P. -J., & Frieder, O. (2002). Coverage in wireless ad-hoc sensor networks. In 2002 IEEE international conference on communications. Conference proceedings. ICC 2002 (Cat. No.02CH37333), New York, NY, USA (Vol. 5, pp. 3174–3178).

  60. Li, M., Liu, S., Zhang, L., Wang, H., Meng, F., & Bai, L. (2012). Non-dominated sorting genetic algorithms-based on multi-objective optimization model in the water distribution system. Procedia Engineering, 37, 309–313.

    Google Scholar 

  61. Jie, J., Jian, C., Chang, G. R., & Ying-You, W. E. N. (2008). Efficient cover set selection in wireless sensor networks. Acta Automatica Sinica, 34(9), 1157–1162.

    Google Scholar 

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  1. Department of Programming and Intelligent Applications, Information Technology Regulation Directory, Communication and Media Commission of Iraq (CMC), Baghdad, Iraq

    Ali Jameel Al-Mousawi

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  1. Ali Jameel Al-Mousawi
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Correspondence to Ali Jameel Al-Mousawi.

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Al-Mousawi, A.J. Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage. Wireless Netw 26, 5595–5621 (2020). https://doi.org/10.1007/s11276-019-02008-4

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