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Whale Optimization Routing Protocol for Minimizing Energy Consumption in Cognitive Radio Wireless Sensor Network
Cognitive Radio Wireless Sensor Networks (CR-WSN) works on nodes that are dependent on batteries. A critical problem with CR-WSN is a lack of energy, especially in situations such as warfare where rapid and aggressive action is needed. The battery level of nodes degrades CR-WSN performance. Researchers face significant difficulty developing a routing protocol for CR-WSN, and that obstacle is posed by energy consumption to deliver a packet. A substantial number of nodes reside in CR-WSN. Every node in CR-WSN is constrained by battery. To minimize network cost, it should be feasible to have the routing protocol used for CR-WSN to be energy efficient. This paper proposes an optimization-based routing protocol, namely Whale Optimization Routing Protocol (WORP), for identifying the best route in CR-WSN to minimize the delay and lead to network efficiency. WORP draws inspiration from the behaviors of whales as they forage, similar to their hunting activity. By prioritizing residual energy and the total energy of the nodes in the route, WORP encourages energy-aware route selection. WORP is examined via simulation with NS2 against current routing protocols. Benchmark performance metrics are used to assess the effectiveness of WORP. Results make an indication that WORP has superior performance than current routing protocols in CR-WSN.
WSN, CR-WSN, Routing, Optimization, Delay, Energy Consumption.
- K. Vijayan, G. Ramprabu, S. SelvakumaraSamy, and M. Rajeswari, “Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions,” Microprocess. Microsyst., vol. 79, Article-id: 103298, 2020. https://doi.org/10.1016/j.micpro.2020.103298.
- P. Sethu Lakshmi and M. G. Jibukumar, “Performance Analysis of SWIPT in Multi-hop Wireless Sensor Networks,” in Procedia Computer Science, 2020, vol. 171, pp. 2157–2166, https://doi.org/10.1016/j.procs.2020.04.233.
- R. N. Yadav, R. Misra, and D. Saini, “Energy aware cluster based routing protocol over distributed cognitive radio sensor network,” Comput. Commun., vol. 129, pp. 54–66, 2018. https://doi.org/10.1016/j.comcom.2018.07.020.
- G. Jaber and R. Kacimi, “A collaborative caching strategy for content-centric enabled wireless sensor networks,” Comput. Commun., vol. 159, pp. 60–70, 2020. https://doi.org/10.1016/j.comcom.2020.05.018.
- B. Kabakulak, “Sensor and sink placement, scheduling and routing algorithms for connected coverage of wireless sensor networks,” Ad Hoc Networks, vol. 86, pp. 83–102, 2019. https://doi.org/10.1016/j.adhoc.2018.11.005.
- F. Niaz, M. Khalid, Z. Ullah, N. Aslam, M. Raza, and M. K. Priyan, “A bonded channel in cognitive wireless body area network based on IEEE 802.15.6 and internet of things,” Comput. Commun., vol. 150, pp. 131–143, 2020. https://doi.org/10.1016/j.comcom.2019.11.016.
- Z. Liu, M. Zhao, Y. Yuan, and X. Guan, “Sub channel and resource allocation in cognitive radio sensor network with wireless energy harvesting,” Comput. Networks, vol. 167, 2020. https://doi.org/10.1016/j.comnet.2019.107028.
- M. M. Hassani and R. Berangi, “A new congestion control mechanism for transport protocol of cognitive radio sensor networks,” AEU–Int. J. Electron. Commun., vol. 85, pp. 134–143, 2018. https://doi.org/10.1016/j.aeue.2017.12.026.
- C. Singhal and V. Patil, “HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network,” Comput. Commun., vol. 169, pp. 11–25, 2021. https://doi.org/10.1016/j.comcom.2020.12.025.
- F. Al-Turjman, “Cognitive routing protocol for disaster-inspired Internet of Things,” Futur. Gener. Comput. Syst., vol. 92, pp. 1103–1115, 2019. https://doi.org/10.1016/j.future.2017.03.014.
- B. S. Awoyemi and B. T. Maharaj, “Quality of service provisioning through resource optimisation in heterogeneous cognitive radio sensor networks,” Comput. Commun., vol. 165, pp. 122–130, 2021. https://doi.org/10.1016/j.comcom.2020.11.006.
- V. Van Huynh, H. S. Nguyen, L. T. T. Hoc, T. S. Nguyen, and M. Voznak, “Optimization issues for data rate in energy harvesting relay-enabled cognitive sensor networks,” Comput. Networks, vol. 157, pp. 29–40, Jul. 2019. https://doi.org/10.1016/j.comnet.2019.04.012.
- A. S. Cacciapuoti, M. Caleffi, and L. Paura, “Reactive routing for mobile cognitive radio ad hoc networks,” Ad Hoc Networks, vol. 10, no. 5, pp. 803–815, 2012. Accessed: Feb. 28, 2021. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1570870511000813.
- J. Lee and J. Lim, “Cognitive routing for multi-hop mobile cognitive radio ad hoc networks,” J. Commun. Networks, vol. 16, no. 2, pp. 155–161, 2014, doi: 10.1109/JCN.2014.000026.
- L. Cheng, L. Zhong, X. Zhang, and J. Xing, “A staged adaptive firefly algorithm for UAV charging planning in wireless sensor networks,” Comput. Commun., vol. 161, pp. 132–141, 2020. https://doi.org/10.1016/j.comcom.2020.07.019.
- P. Feng, Y. Bai, J. Huang, W. Wang, Y. Gu, and S. Liu, “CogMOR-MAC: A cognitive multi-channel opportunistic reservation MAC for multi-UAVs ad hoc networks,” Comput. Commun., vol. 136, pp. 30–42, 2019. https://doi.org/10.1016/j.comcom.2019.01.010.
- S. Gopikrishnan, P. Priakanth, and G. Srivastava, “DEDC: Sustainable data communication for cognitive radio sensors in the Internet of Things,” Sustain. Comput. Informatics Syst., vol. 29, 2021. https://doi.org/10.1016/j.suscom.2020.100471.
- S. Aswale and V. R. Ghorpade, “Geographic Multipath Routing based on Triangle Link Quality Metric with Minimum Inter-path Interference for Wireless Multimedia Sensor Networks,” J. King Saud Univ.–Comput. Inf. Sci., vol. 33, no. 1, pp. 33–44, 2021. https://doi.org/10.1016/j.jksuci.2018.02.001.
- Y. U. Xiu-wu, Y. U. Hao, L. Yong, and X. Ren-rong, “A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks,” Comput. Networks, vol. 167, p. 106994, 2020. https://doi.org/10.1016/j.comnet.2019.106994.
- C. Gu, M. Bradbury, J. Kirton, and A. Jhumka, “A decision theoretic framework for selecting source location privacy aware routing protocols in wireless sensor networks,” Futur. Gener. Comput. Syst., vol. 87, pp. 514–526, 2018. https://doi.org/10.1016/j.future.2018.01.046.
- I. Jemili, D. Ghrab, A. Belghith, and M. Mosbah, “Cross-layer adaptive multipath routing for multimedia Wireless Sensor Networks under duty cycle mode,” Ad Hoc Networks, vol. 109, p. 102292, 2020. https://doi.org/10.1016/j.adhoc.2020.102292.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019. https://doi.org/10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23,2021. https://doi.org/ 10.1007/s11277-021-08495-z.
- B. Abbache et al., “Dissimulation-based and load-balance-aware routing protocol for request and event oriented mobile wireless sensor networks,” AEU–Int. J. Electron. Commun., vol. 99, pp. 264–283, 2019. https://doi.org/10.1016/j.aeue.2018.12.003.
- M. K, C. K, and S. C, “An energy efficient clustering scheme using multilevel routing for wireless sensor network,” Comput. Electr. Eng., vol. 69, pp. 642–652, 2018. https://doi.org/10.1016/j.compeleceng.2017.10.007.
- F. H. Awad, “Optimization of relay node deployment for multisource multipath routing in Wireless Multimedia Sensor Networks using Gaussian distribution,” Comput. Networks, vol. 145, pp. 96–106, 2018. https://doi.org/10.1016/j.comnet.2018.08.021.
- R. Almesaeed and A. Jedidi, “Dynamic directional routing for mobile wireless sensor networks,” Ad Hoc Networks, vol. 110, p. 102301, 2021.https://doi.org/10.1016/j.adhoc.2020.102301.
- L. Rui, X. Wang, Y. Zhang, X. Wang, and X. Qiu, “A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids,” Futur. Gener. Comput. Syst., vol. 100, pp. 35–45, 2019. https://doi.org/10.1016/j.future.2019.04.024.
- A. Agrawal, V. Singh, S. Jain, and R. K. Gupta, “GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink,” AEU–Int. J. Electron. Commun., vol. 94, pp. 1–11, 2018. https://doi.org/10.1016/j.aeue.2018.06.036.
- K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy, and A. Kannan, “Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Comput. Networks, vol. 151, pp. 211–223, 2019. https://doi.org/10.1016/j.comnet.2019.01.024.
- R. Yarinezhad, “Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure,” Ad Hoc Networks, vol. 84, pp. 42–55, 2019. doi: https://doi.org/10.1016/j.adhoc.2018.09.016.
- J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, vol. 556, pp. 145–153, 2017. https://doi.org/10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” IIndianJpurnal of Science and Technology, vol. 13, no. 30, pp. 3059–3069, 2020. https://doi.org/10.17485/IJST/v13i30.1152.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020. https://doi.org/10.30534/ijeter/2020/82882020.
- Dr.R.Vadivel and J.Ramkumar. “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications”, Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, Chapter 6, Pages 109-121, 2020. https://doi.org/10.4018/978-1-7998-1090-2.ch006.
- J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, pp. 2–11, 2020, Accessed: Dec. 02, 2020. [Online]. Available: http://journals.uob.edu.bh.
- M. Tubishat, N. Idris, and M. Abushariah, “Explicit aspects extraction in sentiment analysis using optimal rules combination,” Futur. Gener. Comput. Syst., vol. 114, pp. 448–480, 2021. https://doi.org/10.1016/j.future.2020.08.019.
- M. Lingaraj, T. N. Sugumar, C. Stanly Felix and J. Ramkumar, "Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network", International Journal of Computer Networks and Applications (IJCNA), vol. 8, no.3, pp. 258-267, 2021. https://doi.org/10.22247/ijcna/2021/209192.
- S. Boopalan and S. Jayasankari, "Dolphin Swarm Inspired Protocol (DSIP) for Routing in Underwater Wireless Sensor Networks", International Journal of Computer Networks and Applications (IJCNA), vol. 8, no.1, pp. 44–53, 2021. https://doi.org/10.22247/ijcna/2021/207981.
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