Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Genetic algorithm-based meta-heuristic for target coverage problem

In wireless sensor networks (WSNs), network lifetime and energy consumption are two important parameters which directly impacts each other. In order to enhance the global network lifetime, one should need to utilise the available sensors’ energy in an optimise way. There are several approaches discussed in the literature to maximise the network lifetime for well-known target coverage problem in WSN. The target coverage problem is presented as a maximum network lifetime problem (MLP) and solved heuristically using various approaches. In this study, the authors propose a genetic algorithm (GA)-based meta-heuristic to solve the above said MLP. The GA is a non-linear optimisation solution method which is proven to be better as compared to the column generation or approximation schemes.

References

    1. 1)
      • 8. Pujari, A.K., Mini, S., Padhi, T., et al: ‘Polyhedral approach for lifetime maximization of target coverage problem’. Proc. of the Int. Conf. on Distributed Computing and Networking, Turku, Finland, 2015, pp. 14.114.8.
    2. 2)
      • 15. Gupta, S.K., Kuilab, P., Jana, P.K.: ‘Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks’, Comput. Electr. Eng., 2016, 56, pp. 544556.
    3. 3)
      • 14. Gentili, M., Raiconi, A.: ‘Alpha-coverage to extend network lifetime on wireless sensor networks’, Optimum Lett., 2013, 7, (1), pp. 157172.
    4. 4)
      • 18. Zhang, H., Hou, J.C.: ‘Maximizing α-lifetime for wireless sensor networks’, Int. J. Sens. Netw., 2006, 1, (1), pp. 6471.
    5. 5)
      • 3. Cardei, M., Thai, M.T., Li, Y., et al: ‘Energy-efficient target coverage in wireless sensor networks’. Proc. of the 24th Conf. of the IEEE Communications Society, Miami, USA, 2005, vol. 3, pp. 19761984.
    6. 6)
      • 12. Castaño, F., Rossi, A., Sevaux, M., et al: ‘A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints’, Comput. Oper. Res., 2014, 52, (B), pp. 220230.
    7. 7)
      • 10. Singh, S., Chand, S., Kumar, R., et al: ‘NEECP: a novel energy efficient clustering protocol for prolonging lifetime of WSNs’, IET Wirel. Sens. Syst., 2016, 6, (5), pp. 151157.
    8. 8)
      • 1. Chong, C.-Y., Kumar, S.: ‘Sensor networks: evolution, opportunities, and challenges’, Proc. IEEE, 2003, 91, (8), pp. 12471256.
    9. 9)
      • 21. Konak, A., Coit, D.W., Smith, A.E.: ‘Multi-objective optimization using genetic algorithms: a tutorial’, Reliab. Eng. Syst. Saf., 2006, 91, (9), pp. 9921007.
    10. 10)
      • 17. Raiconi, A., Gentili, M.: ‘Exact and metaheuristic approaches to extend lifetime and maintain connectivity in wireless sensors networks’, ‘Lecture notes in computer science’, vol. 6701 (Springer, Berlin/Heidelberg, 2011), pp. 607619.
    11. 11)
      • 6. Mostafaei, H., Meybodi, M.R.: ‘Maximizing lifetime of target coverage in wireless sensor networks using learning automata’, Wirel. Pers. Commun., 2013, 71, (2), pp. 14611477.
    12. 12)
      • 16. Carrabs, F., Cerulli, R., D'Ambrosio, C., et al: ‘A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints’, J. Netw. Comput. Appl., 2015, 58, pp. 1222.
    13. 13)
      • 7. Mostafaei, H., Esnaashari, M., Meybodi, M.R.: ‘A coverage monitoring algorithm based on learning automata for wireless sensor networks’, Appl. Math. Inf. Sci., 2015, 9, (3), pp. 13171325.
    14. 14)
      • 13. Singh, S., Chand, S., Kumar, B.: ‘Heterogeneous HEED protocol for wireless sensor networks’, Wirel. Pers. Commun., 2014, 77, (3), pp. 21172139.
    15. 15)
      • 4. Mini, S., Udgata, S.K., Sabat, S.L.: ‘A heuristic to maximize network lifetime for target coverage problem in wireless sensor networks’, Ad Hoc Sens. Wirel. Netw., 2011, 13, pp. 251269.
    16. 16)
      • 19. Yoon, Y., Kim, Y.-H.: ‘An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks’, IEEE Trans. Cybern., 2013, 43, (5), pp. 14731483.
    17. 17)
      • 5. Mostafaei, H., Montieri, A., Persico, V., et al: ‘A sleep scheduling approach based on learning automata for WSN partial coverage’, J. Netw. Comput. Appl., 2017, 80, pp. 6778.
    18. 18)
      • 20. Rebai, M., Leberre, M., Snoussi, H., et al: ‘Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks’, Comput. Oper. Res., 2015, 59, pp. 1121.
    19. 19)
      • 11. Chaudhary, M., Pujari, A.K.: ‘Q-coverage problem in wireless sensor networks’. Proc. Int. Conf. Distributed Computing Networking, Hydearbad, india, 2009, pp. 325330.
    20. 20)
      • 9. Manju Chand, S., Kumar, B.: ‘Maximizing network lifetime for target coverage problem in wireless sensor networks’, IET Wirel. Sens. Syst., 2016, 77, (3), pp. 21172139.
    21. 21)
      • 2. Slijepcevic, S., Potkonjak, M.: ‘Power efficient organization of wireless sensor networks’. Proc. of the IEEE Int. Conf. on Communications, Helsinki, Finland, 2001, pp. 472476.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2017.0067
Loading

Related content

content/journals/10.1049/iet-wss.2017.0067
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address