Underwater Self Deployment of Wireless Sensors for Maximum Coverage and Connectivity using K-Means Clustering

  • Authors

    • Gurjaspreet Kaur
    • Surinder Singh
    • Ramanpreet Kaur
    • Gaurav Garg
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.11.10997
  • Connectivity, K-Means Clustering, Maximum Area Coverage, Movement Restricted Deployment, Self Deployment, Underwater Communication.

  • Abstract

    By virtue of abundant research being executed in terrestrial wireless sensor networks, we get enlightened about the different advantages of using wireless sensors and implemented these wireless sensors in underwater to probe the area below the sea for its various applications. The most crucial task in underwater 3D space is the node deployment such that sensors can cover the maximum area while simultaneously maintaining the connectivity with the base station. Due to the advantages of self deployment where no pre calculations and no human interventions are needed, we proposed a distributed move restricted self deployment underwater wireless sensor network. The main emphasis is on maximizing the coverage area by the sensor nodes and simultaneously maintaining the connectivity with the base station. Firstly, the maximum coverage has achieved by removing the interference between the sensor nodes which minimize the coverage overlap and thus more area can be covered which improves the network coverage. Then by using K-Means clustering, all the sensors nodes were divided into clusters, having one centroid for each cluster. Further all these centroids have been interconnected and then by finding the centroid which is nearest to the sink by Euclidean distance formula, we connect it to the sink.

     

     

  • References

    1. [1] Climent, S, Sanchez, A, Capella, J.V, Meratnia, N., & Serrana, J.J. (2014), Underwater acoustic wireless sensor networks: Advances and future trends in physical, MAC and routing layers. Sensors 14, 795–833.

      [2] Guo, Z.W, Luo, H. J., Hong, F., Yang, M., & Ni, M. X. (2010), Current progress and research issues in underwater sensor networks. J. Comput. Res. Dev. 47, 377–389.

      [3] Ian. F. (2014), Wireless sensor networks in challenged environments such as underwater and underground. In Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, New York, NY, USA, pp. 1–2.

      [4] Heidemann, J, Stojanovic, M., & Zorzi., M. (2012), Underwater sensor networks: Applications, advances and challenges. Phil. Soc. R. Trans. A 370,158–175.

      [5] Liu, J., Han., X., Al-Bzoor, M., Zuba., M., Cui, J.H., Ammar R. A., & Rajasekaran, S. (2012) PADP: Prediction assisted dynamic surface gateway placement for mobile underwater networks. In Proceedings of IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, pp. 139–144.

      [6] Cui, J.H., Kong, J., Gerla, M., & Zhou, S. (2006), The challenges of building mobile underwater wireless networks for aquatic applications. IEEE Netw. 20, 12–18.

      [7] Akkaya, K., & Newell, A. (2009), Self-deployment of sensors for maximized coverage in underwater acoustic sensor networks. Comput. Commun. 32, 1233–1244.

      [8] Hua, C. B., Wei, Z., & Nan, C. Z. (2014). Underwater Acoustic Sensor Networks Deployment Using Improved Self-Organize Map Algorithm. Cybernetics and Information Technologies 14, 63-77.

      [9] Du, H., Xia, N., & Zheng, R. (2014). Particle swarm inspired underwater sensor self-deployment. Sensors 14, 15262-15281.

      [10] Jiang, P., Wang, X., & Jiang, L. (2015). Node deployment algorithm based on connected tree for underwater sensor networks. Sensors 15, 16763-16785.

      [11] Jiang, P., Xu, Y., & Wu, F. (2016). Node self-deployment algorithm based on an uneven cluster with radius adjusting for underwater sensor networks. Sensors 16, 98.

  • Downloads

  • How to Cite

    Kaur, G., Singh, S., Kaur, R., & Garg, G. (2018). Underwater Self Deployment of Wireless Sensors for Maximum Coverage and Connectivity using K-Means Clustering. International Journal of Engineering & Technology, 7(2.11), 5-8. https://doi.org/10.14419/ijet.v7i2.11.10997

    Received date: 2018-04-03

    Accepted date: 2018-04-03

    Published date: 2018-04-03