Advertisement

Optimal Sensor Deployment Using Ant Lion Optimization

  • Mudassar Ali Syed
  • Misbahuddin Md
  • Raziuddin SyedEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Wireless Sensor Networks (WSN’s) consists of small and tiny devices called sensor nodes. These sensor nodes are deployed in the required landscape to gather information. Improving coverage rate of sensor nodes imposes a bigger challenge in the sensor network deployment task. Our work proposes a solution based on the Ant Lion Optimization (ALO) algorithm to augment the coverage rate of the sensor network. The extensive simulations corroborate the approach usability in WSN. The results signify the improved performance and better convergence rate of the proposed algorithm approach achieving the objective of better coverage rate.

Keywords:

Wireless sensor network Coverage Ant lion optimization 

References

  1. 1.
    Zhao F, Guibas LJ, Guibas L (2004) Wireless sensor networks: an information processing approach. Morgan Kaufmann, San FranciscoGoogle Scholar
  2. 2.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput networks 38:393–422CrossRefGoogle Scholar
  3. 3.
    Hoebeke J, Moerman I, Dhoedt B, Demeester P (2004) An overview of mobile ad hoc networks: applications and challenges. Journal-Communications Netw 3:60–66Google Scholar
  4. 4.
    Chakrabarty K, Iyengar SS, Qi H, Cho E (2002) Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans Comput 51:1448–1453MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang Y-C, Hu C-C, Tseng, Y-C (2005) Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks. In: First International Conference on Wireless Internet (WICON’05), pp. 114–121Google Scholar
  6. 6.
    Heo N, Varshney PK (2005) Energy-efficient deployment of intelligent mobile sensor networks. IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Humans 35:78–92Google Scholar
  7. 7.
    Yen L-H, Yu CW, Cheng Y-M (2006) Expected k-coverage in wireless sensor networks. Ad Hoc Networks. 4:636–650CrossRefGoogle Scholar
  8. 8.
    Wu CH, Lee KC, Chung YC (2007) A Delaunay Triangulation based method for wireless sensor network deployment. Comput Commun 30:2744–2752CrossRefGoogle Scholar
  9. 9.
    Zou Y, Chakrabarty K (2003) Energy-aware target localization in wireless sensor networks. In: Pervasive Computing and Communications, 2003.(PerCom 2003). In: Proceedings of the First IEEE International Conference on, pp 60–67Google Scholar
  10. 10.
    Lei S, Cho J, Jin W, Lee S, Wu X (2005) Energy-efficient deployment of mobile sensor networks by PSO, pp 373–382Google Scholar
  11. 11.
    Xiaoling W, Lei S, Jie Y, Hui X, Cho J, Lee S (2005) Swarm based sensor deployment optimization in ad hoc sensor networks. In: International Conference on Embedded Software and Systems, pp 533–541Google Scholar
  12. 12.
    Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  13. 13.
    Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1:660–670CrossRefGoogle Scholar
  14. 14.
    Megerian S, Koushanfar F, Qu G, Veltri G, Potkonjak M (2002) Exposure in wireless sensor networks: theory and practical solutions. Wirel Networks 8:443–454CrossRefGoogle Scholar
  15. 15.
    Chaudhary DK, Dua RL (2012) Application of multi objective particle swarm optimization to maximize coverage and lifetime of wireless sensor network. Int J Comput Eng Res 2:1628–1633Google Scholar
  16. 16.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  17. 17.
    Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRefGoogle Scholar
  18. 18.
    Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Deccan College of Engineering and TechnologyHyderabadIndia
  2. 2.B. S. Abdur Rahman UniversityChennaiIndia

Personalised recommendations