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Integrated Load Balancing and Void Healing Routing with Cuckoo Search Optimization Scheme for Underwater Wireless Sensor Networks

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Abstract

In underwater wireless sensor networks, routing play a vital role in selecting an optimal path for packet forwarding. In routing scheme, most of the existing work is suffering from both load balancing and void node issue. This is due to the environmental interference, overloaded data, energy depletion, random deployment and mobility of the nodes. However, it causes loss of packet, high energy depletion and bad network quality. We have resolved this issue by implementing load balancing and void healing routing using cuckoo search optimization (CSO) scheme. In this scheme, first we placed the parent node and identify their child node within the transmission range in each level of the network. Then, we applied load balancing with priority based packet forwarding to maintain the uneven distribution of the load and reduces the end-to-end delay. Next, void healing routing with CSO scheme is addressed to recover the convex and concave void issue in the network. A novel multi-objective fitness function is also formulated for selecting the optimal number of nodes. In packet routing, each child node is responsible for receiving the packets from their neighbor nodes and transferred to the parent node. After receiving the packets at parent node, autonomous underwater vehicle is used for collecting the relevant packets from each parent node through minimum travelling time and send towards the base station. The performance evaluation of proposed scheme shows better network quality, packet delivery ratio, less energy consumption and delay over the existing solutions.

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References

  1. Han, G., Jiang, J., Bao, N., Wan, L., & Guizani, M. (2015). Routing protocols for underwater wireless sensor networks. IEEE Communications Magazine,53(11), 72–78.

    Article  Google Scholar 

  2. Darehshoorzadeh, A., & Boukerche, A. (2015). Underwater sensor networks: A new challenge for opportunistic routing protocols. IEEE Communications Magazine,53(11), 98–107.

    Article  Google Scholar 

  3. Heidemann, J., Stojanovic, M., & Zorzi, M. (2012). Underwater sensor networks: Applications, advances and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,370(1958), 158–175.

    Article  Google Scholar 

  4. Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., & Qaisar, S. B. (2015). Underwater sensor network applications: A comprehensive survey. International Journal of Distributed Sensor Networks,11(11), 896832.

    Article  Google Scholar 

  5. Khasawneh, A., Latiff, M. S. B. A., Chizari, H., Tariq, M., & Bamatraf, A. (2015). Pressure based routing protocol for underwater wireless sensor networks: A survey. KSII Transactions on Internet & Information Systems,9(2), 504.

    Article  Google Scholar 

  6. Rahim, S. S., Ahmed, S., Javaid, N., Khan, A., Siddiqui, N., Hadi, F., et al. (2019). Scalability analysis of depth-based routing and energy-efficient depth-based routing protocols in terms of delay, throughput, and path loss in underwater acoustic sensor networks. In M. A. Jan, F. Khan, & M. Alam (Eds.), Recent trends and advances in wireless and IoT-enabled networks (pp. 171–185). Cham: Springer.

    Chapter  Google Scholar 

  7. Azam, I., Javaid, N., Ahmad, A., Abdul, W., Almogren, A., & Alamri, A. (2017). Balanced load distribution with energy hole avoidance in underwater WSNs. IEEE Access,5, 15206–15221.

    Article  Google Scholar 

  8. Noh, Y., Lee, U., Wang, P., Choi, B. S. C., & Gerla, M. (2012). VAPR: Void-aware pressure routing for underwater sensor networks. IEEE Transactions on Mobile Computing,12(5), 895–908.

    Article  Google Scholar 

  9. Coutinho, R. W., Boukerche, A., Vieira, L. F., & Loureiro, A. A. (2015). A novel void node recovery paradigm for long-term underwater sensor networks. Ad Hoc Networks,34, 144–156.

    Article  Google Scholar 

  10. Nowsheen, N., Karmakar, G., & Kamruzzaman, J. (2016). PRADD: A path reliability-aware data delivery protocol for underwater acoustic sensor networks. Journal of Network and Computer Applications,75, 385–397.

    Article  Google Scholar 

  11. Yu, H., Yao, N., Wang, T., Li, G., Gao, Z., & Tan, G. (2016). WDFAD-DBR: Weighting depth and forwarding area division DBR routing protocol for UASNs. Ad Hoc Networks,37, 256–282.

    Article  Google Scholar 

  12. Coutinho, R. W., Boukerche, A., Vieira, L. F., & Loureiro, A. A. (2015). Geographic and opportunistic routing for underwater sensor networks. IEEE Transactions on Computers,65(2), 548–561.

    Article  MathSciNet  Google Scholar 

  13. Goyal, N., Dave, M., & Verma, A. K. (2016). Energy efficient architecture for intra and inter cluster communication for underwater wireless sensor networks. Wireless Personal Communications,89(2), 687–707.

    Article  Google Scholar 

  14. Coutinho, R. W., Boukerche, A., Vieira, L. F., & Loureiro, A. A. (2017). Performance modeling and analysis of void-handling methodologies in underwater wireless sensor networks. Computer Networks,126, 1–14.

    Article  Google Scholar 

  15. Kanthimathi, N. (2017). Void handling using Geo-Opportunistic Routing in underwater wireless sensor networks. Computers & Electrical Engineering,64, 365–379.

    Article  Google Scholar 

  16. Ghoreyshi, S. M., Shahrabi, A., & Boutaleb, T. (2017). Void-handling techniques for routing protocols in underwater sensor networks: Survey and challenges. IEEE Communications Surveys & Tutorials,19(2), 800–827.

    Article  Google Scholar 

  17. Bouk, S., Ahmed, S., Park, K. J., & Eun, Y. (2017). Edove: Energy and depth variance-based opportunistic void avoidance scheme for underwater acoustic sensor networks. Sensors,17(10), 2212.

    Article  Google Scholar 

  18. Wang, Z., Han, G., Qin, H., Zhang, S., & Sui, Y. (2018). An energy-aware and void-avoidable routing protocol for underwater sensor networks. IEEE Access,6, 7792–7801.

    Article  Google Scholar 

  19. Qiuli, C., Wei, X., Fei, D., & Ming, H. (2018). A reliable routing protocol against hotspots and burst for UASN-based fog systems. Journal of Ambient Intelligence and Humanized Computing,10, 1–13.

    Google Scholar 

  20. Javaid, N., Ahmad, Z., Sher, A., Wadud, Z., Khan, Z. A., & Ahmed, S. H. (2018). Fair energy management with void hole avoidance in intelligent heterogeneous underwater WSNs. Journal of Ambient Intelligence and Humanized Computing,10, 1–17.

    Google Scholar 

  21. Guan, Q., Ji, F., Liu, Y., Yu, H., & Chen, W. (2019). Distance-vector-based opportunistic routing for underwater acoustic sensor networks. IEEE Internet of Things Journal,6(2), 3831–3839.

    Article  Google Scholar 

  22. Albukhary, R. A., & Bouabdallah, F. (2019). Time-variant balanced routing strategy for underwater wireless sensor networks. Wireless Networks,25(6), 3481–3495.

    Article  Google Scholar 

  23. Chen, J. F., Hsieh, H. N., & Do, Q. (2014). Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. Algorithms,7(4), 538–553.

    Article  Google Scholar 

  24. Mahmoudi, S., & Lotfi, S. (2015). Modified cuckoo optimization algorithm (MCOA) to solve graph coloring problem. Applied Soft Computing,33, 48–64.

    Article  Google Scholar 

  25. Barber, C. B., Dobkin, D. P., Dobkin, D. P., & Huhdanpaa, H. (1996). The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software (TOMS),22(4), 469–483.

    Article  MathSciNet  Google Scholar 

  26. Khan, J., & Cho, H. S. (2015). A distributed data-gathering protocol using AUV in underwater sensor networks. Sensors,15(8), 19331–19350.

    Article  Google Scholar 

  27. Ilyas, N., Alghamdi, T. A., Farooq, M. N., Mehboob, B., Sadiq, A. H., Qasim, U., et al. (2015). AEDG: AUV-aided efficient data gathering routing protocol for underwater wireless sensor networks. Procedia Computer Science,52, 568–575.

    Article  Google Scholar 

  28. Luo, H., Guo, Z., Wu, K., Hong, F., & Feng, Y. (2009). Energy balanced strategies for maximizing the lifetime of sparsely deployed underwater acoustic sensor networks. Sensors,9(9), 6626–6651.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology, Raipur, Chhattisgarh, 492010, India

    Sangeeta Kumari & Pavan Kumar Mishra

  2. Department of Computer Science and Engineering, National Institute of Technology, Raipur, Chhattisgarh, 492010, India

    Veena Anand

Authors
  1. Sangeeta Kumari
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  2. Pavan Kumar Mishra
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  3. Veena Anand
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Correspondence to Sangeeta Kumari.

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Kumari, S., Mishra, P.K. & Anand, V. Integrated Load Balancing and Void Healing Routing with Cuckoo Search Optimization Scheme for Underwater Wireless Sensor Networks. Wireless Pers Commun 111, 1787–1803 (2020). https://doi.org/10.1007/s11277-019-06957-z

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  • DOI: https://doi.org/10.1007/s11277-019-06957-z

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