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Dolphin Swarm Inspired Protocol (DSIP) for Routing in Underwater Wireless Sensor Networks


Affiliations
1 Department of Computer Science, PKR Arts College for Women, Gobichettipalayam, Erode, Tamil Nadu, India
 

Underwater communication is still carried out using communication cables because of the minimum development that is established in underwater wireless communications. The utilization of wires to make sure the connectivity of sensor nodes that are located at the bottom of the sea is highly expensive. Finding the best route to send the sensed data to the destination in minimum duration has become a primary challenge in underwater wireless sensor networks (UWSN). Feasible routing protocols available for general sensor networks are not feasible for UWSN because of the difficult communication medium. Existing routing protocol face the problem of consuming more energy to deliver the data packet and also due to selecting the unfit route it faces more delay. To overcome the routing challenges present in UWSN, Dolphin Swarm Inspired Protocol (DSIP) is proposed in this paper. DSIP is inspired by the swarming nature of dolphins towards finding their food. Four significant phases involved in DSIP to find the best route in UWSN are searching, calling, reception, and predation. NS3 is used to evaluate the performance of DSIP against previous routing protocols with benchmark metrics namely packet delivery ratio, end-to-end delay, node death rate, and energy consumption. Results indicate that DSIP has consumed 1.43 times less energy than other previous protocols.

Keywords

Energy, Delay, Swarm, Dolphin, Routing, Packet Delivery Ratio.
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  • Liu, L., Wang, R., Xiao, G., & Guo, D. (2020). On the throughput optimization for message dissemination in opportunistic underwater sensor networks. Computer Networks, 169, 107097. https://doi.org/10.1016/j.comnet.2020.107097.
  • Mishachandar, B., & Vairamuthu, S. (2021). An underwater cognitive acoustic network strategy for efficient spectrum utilization. Applied Acoustics, 175, 107861. https://doi.org/10.1016/j.apacoust.2020.107861.
  • Camara Junior, E. P. M., Vieira, L. F. M., & Vieira, M. A. M. (2020). CAPTAIN: A data collection algorithm for underwater optical-acoustic sensor networks. Computer Networks, 171, 107145. https://doi.org/10.1016/j.comnet.2020.107145.
  • Shakila, R., & Paramasivan, B. (2020). Performance Analysis of Submarine Detection in Underwater Wireless Sensor Networks for Naval Application. Microprocessors and Microsystems, 103293. https://doi.org/10.1016/j.micpro.2020.103293.
  • Dong, M., Li, H., Yin, R., Qin, Y., & Hu, Y. (2021). Scalable asynchronous localization algorithm with mobility prediction for underwater wireless sensor networks. Chaos, Solitons & Fractals, 143, 110588. https://doi.org/10.1016/j.chaos.2020.110588.
  • Zhang, W., Wang, X., Han, G., Peng, Y., Guizani, M., & Sun, J. (2021). A load-adaptive fair access protocol for MAC in underwater acoustic sensor networks. Journal of Network and Computer Applications, 173, 102867. https://doi.org/10.1016/j.jnca.2020.102867.
  • Roy, A., & Sarma, N. (2020). A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2020.09.002.
  • Vijayan, K., Ramprabu, G., Selvakumara Samy, S., & Rajeswari, M. (2020). Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions. Microprocessors and Microsystems, 79, 103298. https://doi.org/10.1016/j.micpro.2020.103298.
  • Ojha, T., Misra, S., & Obaidat, M. S. (2020). SEAL: Self-adaptive AUV-based localization for sparsely deployed Underwater Sensor Networks. Computer Communications, 154, 204–215. https://doi.org/10.1016/j.comcom.2020.02.050.
  • Toky, A., Singh, R. P., & Das, S. (2020). Localization schemes for Underwater Acoustic Sensor Networks - A Review. Computer Science Review, 37, 100241. https://doi.org/10.1016/j.cosrev.2020.100241.
  • Zhang, M., & Cai, W. (2020). Energy-Efficient Depth Based Probabilistic Routing Within 2-Hop Neighborhood for Underwater Sensor Networks. IEEE Sensors Letters, 4(6), 1–4. https://doi.org/10.1109/LSENS.2020.2995236.
  • Rani, S., Ahmed, S. H., Malhotra, J., & Talwar, R. (2017). Energy efficient chain based routing protocol for underwater wireless sensor networks. Journal of Network and Computer Applications, 92, 42–50. https://doi.org/10.1016/j.jnca.2017.01.011.
  • Han, G., Liu, L., Bao, N., Jiang, J., Zhang, W., & Rodrigues, J. J. P. C. (2017). AREP: An asymmetric link-based reverse routing protocol for underwater acoustic sensor networks. Journal of Network and Computer Applications, 92, 51–58. https://doi.org/10.1016/j.jnca.2017.01.009.
  • Bharamagoudra, M. R., Manvi, S. S., & Gonen, B. (2017). Event driven energy depth and channel aware routing for underwater acoustic sensor networks: Agent oriented clustering based approach. Computers & Electrical Engineering, 58, 1–19. https://doi.org/10.1016/j.compeleceng.2017.01.004.
  • Ullah, I., Chen, J., Su, X., Esposito, C., & Choi, C. (2019). Localization and Detection of Targets in Underwater Wireless Sensor Using Distance and Angle Based Algorithms. IEEE Access, 7, 45693–45704. https://doi.org/10.1109/ACCESS.2019.2909133
  • Du, X., Huang, K., Lan, S., Feng, Z., & Liu, F. (2014). LB-AGR: level-based adaptive geo-routing for underwater sensor network. The Journal of China Universities of Posts and Telecommunications, 21(1), 54–59. https://doi.org/10.1016/S1005-8885(14)60268-5.
  • Faheem, M., Ngadi, M. A., & Gungor, V. C. (2019). Energy efficient multi-objective evolutionary routing scheme for reliable data gathering in Internet of underwater acoustic sensor networks. Ad Hoc Networks, 93, 101912. https://doi.org/10.1016/j.adhoc.2019.101912.
  • Ali, T., Jung, L. T., & Faye, I. (2014). Diagonal and Vertical Routing Protocol for Underwater Wireless Sensor Network. Procedia - Social and Behavioral Sciences, 129, 372–379. https://doi.org/10.1016/j.sbspro.2014.03.690.
  • Su, Y., Fan, R., & Jin, Z. (2019). ORIT: A Transport Layer Protocol Design for Underwater DTN Sensor Networks. IEEE Access, 7, 69592–69603. https://doi.org/10.1109/ACCESS.2019.2918561
  • Jiang, J., Han, G., Guo, H., Shu, L., & Rodrigues, J. J. P. C. (2016). Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks. Journal of Network and Computer Applications, 59, 4–13. https://doi.org/10.1016/j.jnca.2015.01.005.
  • Patil, K., Jafri, M., Fiems, D., & Marin, A. (2019). Stochastic modeling of depth based routing in underwater sensor networks. Ad Hoc Networks, 89, 132–141. https://doi.org/10.1016/j.adhoc.2019.03.009.
  • Ilyas, N., Alghamdi, T. A., Farooq, M. N., Mehboob, B., Sadiq, A. H., Qasim, U., Khan, Z. A., & Javaid, N. (2015). AEDG: AUV-aided Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks. Procedia Computer Science, 52, 568–575. https://doi.org/10.1016/j.procs.2015.05.038.
  • Kanthimathi, N., & Dejey. (2017). Void handling using Geo-Opportunistic Routing in underwater wireless sensor networks. Computers & Electrical Engineering, 64, 365–379. https://doi.org/10.1016/j.compeleceng.2017.07.016.
  • Javaid, N., Hussain, S., Ahmad, A., Imran, M., Khan, A., & Guizani, M. (2017). Region based cooperative routing in underwater wireless sensor networks. Journal of Network and Computer Applications, 92, 31–41. https://doi.org/10.1016/j.jnca.2017.01.013.
  • Jafri, M. R., Sandhu, M. M., Latif, K., Khan, Z. A., Yasar, A. U. H., & Javaid, N. (2014). Towards Delay-sensitive Routing in Underwater Wireless Sensor Networks. Procedia Computer Science, 37, 228–235. https://doi.org/10.1016/j.procs.2014.08.034.
  • Ramkumar, J., & Vadivel, R. (2020). Improved Wolf prey inspired protocol for routing in cognitive radio Ad Hoc networks. International Journal of Computer Networks and Applications, 7(5), 126–136. https://doi.org/10.22247/ijcna/2020/202977.
  • Ramkumar, J., & Vadivel, R. (2020b). Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks. International Journal of Emerging Trends in Engineering Research, 8(8). https://doi.org/10.30534/ijeter/2020/82882020.
  • Wu, Tq., Yao, M. & Yang, Jh. (2016). Dolphin swarm algorithm. Frontiers Inf Technol Electronic Eng 17, 717–729. https://doi.org/10.1631/FITEE.1500287
  • Z. Wang, G. Han, H. Qin, S. Zhang & Y. Sui. (2018). An Energy-Aware and Void-Avoidable Routing Protocol for Underwater Sensor Networks," IEEE Access, 6, 7792-7801. doi: 10.1109/ACCESS.2018.2805804.
  • Gopi, S., Govindan, K., Chander, D., Desai, U. B., & Merchant, S. N. (2010). E-PULRP: Energy optimized path unaware layered routing protocol for underwater sensor networks. IEEE Transactions on Wireless Communications, 9(11), 3391–3401. https://doi.org/10.1109/TWC.2010.091510.090452.

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  • Dolphin Swarm Inspired Protocol (DSIP) for Routing in Underwater Wireless Sensor Networks

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Authors

S. Boopalan
Department of Computer Science, PKR Arts College for Women, Gobichettipalayam, Erode, Tamil Nadu, India
S. Jayasankari
Department of Computer Science, PKR Arts College for Women, Gobichettipalayam, Erode, Tamil Nadu, India

Abstract


Underwater communication is still carried out using communication cables because of the minimum development that is established in underwater wireless communications. The utilization of wires to make sure the connectivity of sensor nodes that are located at the bottom of the sea is highly expensive. Finding the best route to send the sensed data to the destination in minimum duration has become a primary challenge in underwater wireless sensor networks (UWSN). Feasible routing protocols available for general sensor networks are not feasible for UWSN because of the difficult communication medium. Existing routing protocol face the problem of consuming more energy to deliver the data packet and also due to selecting the unfit route it faces more delay. To overcome the routing challenges present in UWSN, Dolphin Swarm Inspired Protocol (DSIP) is proposed in this paper. DSIP is inspired by the swarming nature of dolphins towards finding their food. Four significant phases involved in DSIP to find the best route in UWSN are searching, calling, reception, and predation. NS3 is used to evaluate the performance of DSIP against previous routing protocols with benchmark metrics namely packet delivery ratio, end-to-end delay, node death rate, and energy consumption. Results indicate that DSIP has consumed 1.43 times less energy than other previous protocols.

Keywords


Energy, Delay, Swarm, Dolphin, Routing, Packet Delivery Ratio.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F207981