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Hybrid Optimization-Based Efficient Routing Protocol for Energy Consumption Minimization in Mobile Wireless Sensor Network


Affiliations
1 Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Tamil Nadu, India
 

Mobile Wireless Sensor Network (MWSN) is a dispersed network having autonomous sensor nodes which monitors physical occurrences or environmental variables in real-time. Most MWSNs have limited energy, so energy efficiency is critical. A node’s data will be routed by one of two standard methods: single-long-hop or short-multi-hop routing paths. The quantity of energy required to deliver a packet grows directly proportional to the packet’s travel distance in MWSN. Single-hop communication in MWSN, on the other hand, is typically relatively energy-intensive. The nodes located nearer to the sink are considerably perform well than the rest of the nodes in MWSN because of the multi-hop connection, resulting in a shorter lifespan for the MWSN. In this paper, Hybrid Optimization-based Efficient Routing Protocol (HOERP) is proposed to minimize the energy consumption in MWSN. HOERP involves grey wolf optimization and particle swarm optimization, where local search is done by grey wolf optimization and the global search optimization is done by particle swarm optimization. Utilizing the nonlinear parameters in HOERP assist in identifying the optimized cum successful route leading to consume less energy. HOERP is evaluated in NS3 using the metrics standardly used in network-oriented researches. Result highlights that HOERP consumes less energy to deliver data packets than the current routing protocols.

Keywords

Routing, MWSN, Energy, Delay, Hybrid, Optimization, Simulator, Network
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  • J. Kim, “Three dimensional distributed rendezvous in spherical underwater robots considering power consumption,” Ocean Eng., vol.

  • , Mar. 2020, doi: 10.1016/j.oceaneng.2020.107050.

  • A. Pathak and M. G. Bhatt, “Synergetic manufacturing systems anchored by cloud computing: A classified review of trends and perspective,” Mater. Today Proc., 2020, doi: https://doi.org/10.1016/j.matpr.2020.07.435.

  • C. Wang, Y. Zhang, X. Wang, and Z. Zhang, “Hybrid Multihop Partition-Based Clustering Routing Protocol for WSNs,” IEEE Sensors Lett., vol. 2, no. 1, pp. 1–4, 2018, doi:

  • 1109/LSENS.2018.2803086.

  • X. Li, J. Bao, J. Sun, and J. Wang, “Development of circular economy in smart cities based on FPGA and wireless sensors,” Microprocess.

  • Microsyst., vol. 80, Feb. 2021, doi: 10.1016/j.micpro.2020.103600.

  • Z. A. Khan et al., “Region Aware Proactive Routing Approaches Exploiting Energy Efficient Paths for Void Hole Avoidance in Underwater WSNs,” IEEE Access, vol. 7, pp. 140703–140722, 2019, doi: 10.1109/ACCESS.2019.2939155.

  • C. Wang, L. Zhang, Z. Li, and C. Jiang, “SDCoR: Software Defined Cognitive Routing for Internet of Vehicles,” IEEE Internet Things J., vol. 5, no. 5, pp. 3513–3520, 2018, doi: 10.1109/JIOT.2018.2812210.

  • L. Hong-tan, K. Cui-hua, B. A. Muthu, and C. B. Sivaparthipan, “Big data and ambient intelligence in IoT-based wireless student health monitoring system,” Aggression and Violent Behavior. Elsevier Ltd, 2021. doi: 10.1016/j.avb.2021.101601.

  • X. Zheng, P. Li, Z. Chu, and X. Hu, “A Survey on Multi-Label Data Stream Classification,” IEEE Access, vol. 8, pp. 1249–1275, 2020, doi: 10.1109/ACCESS.2019.2962059.

  • Y. Xu, Z. Yue, and L. Lv, “Clustering Routing Algorithm and Simulation of Internet of Things Perception Layer Based on Energy Balance,” IEEE Access, vol. 7, pp. 145667–145676, 2019, doi: 10.1109/ACCESS.2019.2944669.

  • M. T. Vu et al., “Docking assessment algorithm for autonomous underwater vehicles,” Appl. Ocean Res., vol. 100, Jul. 2020, doi: 10.1016/j.apor.2020.102180.

  • 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, Mar. 2021, doi: 10.1016/j.suscom.2020.100471.

  • R. Jaganathan and V. Ramasamy, “Performance modeling of bioinspired routing protocols in Cognitive Radio Ad Hoc Network to reduce end-to-end delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp.

  • –231, 2019, doi: 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., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-02108495-z.

  • 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.

  • , pp. 4548–4554, 2020, doi: 10.30534/ijeter/2020/82882020.

  • K. Patil, M. Jafri, D. Fiems, and A. Marin, “Stochastic modeling of depth based routing in underwater sensor networks,” Ad Hoc Networks, vol. 89, pp. 132–141, 2019, doi:

  • https://doi.org/10.1016/j.adhoc.2019.03.009.

  • J. Aranda, D. Mendez, H. Carrillo, and M. Schölzel, “A framework for multimodal wireless sensor networks,” Ad Hoc Networks, vol. 106, p.102201, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102201.

  • P. Maheshwari, A. K. Sharma, and K. Verma, “Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization,” Ad Hoc Networks, vol. 110, p. 102317, 2021, doi: https://doi.org/10.1016/j.adhoc.2020.102317.

  • A. Chowdhury and D. De, “MSLG-RGSO: Movement score based limited grid-mobility approach using reverse Glowworm Swarm Optimization algorithm for mobile wireless sensor networks,” Ad Hoc Networks, vol. 106, p. 102191, 2020, doi:

  • https://doi.org/10.1016/j.adhoc.2020.102191.

  • M. Boushaba, A. Hafid, and M. Gendreau, “Node stability-based routing in Wireless Mesh Networks,” J. Netw. Comput. Appl., vol. 93, pp. 1–12, 2017, doi: https://doi.org/10.1016/j.jnca.2017.02.010.

  • X. Liu, J. Yu, W. Zhang, and H. Tian, “Low-energy dynamic clustering scheme for multi-layer wireless sensor networks,” Comput.

  • Electr. Eng., vol. 91, p. 107093, 2021, doi:

  • https://doi.org/10.1016/j.compeleceng.2021.107093.

  • A. Rajini, N. Nithya “Hybrid Intrusion Detection System in IOT Network Environments” Compliance Engineering Journal, vol.10, no.11, pp.541-548, 2019.

  • Z. H. Mir and Y.-B. Ko, “Self-Adaptive Neighbor Discovery in Wireless Sensor Networks with Sectored-Antennas,” Comput. Stand.

  • Interfaces, vol. 70, p. 103427, 2020, doi:

  • https://doi.org/10.1016/j.csi.2020.103427.

  • X. Fu, H. Yao, and Y. Yang, “Exploring the invulnerability of wireless sensor networks against cascading failures,” Inf. Sci. (Ny)., vol. 491, pp. 289–305, 2019, doi: https://doi.org/10.1016/j.ins.2019.04.004.

  • 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, doi: https://doi.org/10.1016/j.comnet.2019.106994.

  • 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, Mar. 2019, doi:

  • 1016/j.comnet.2019.01.024.

  • S. Doostali and S. M. Babamir, “An energy efficient cluster head selection approach for performance improvement in network-codingbased wireless sensor networks with multiple sinks,” Comput.

  • Commun., vol. 164, pp. 188–200, 2020, doi:

  • https://doi.org/10.1016/j.comcom.2020.10.014.

  • D. Wang, J. Liu, and D. Yao, “An energy-efficient distributed adaptive cooperative routing based on reinforcement learning in wireless multimedia sensor networks,” Comput. Networks, vol. 178, p. 107313, 2020, doi: https://doi.org/10.1016/j.comnet.2020.107313.

  • A. Rajini, N. Nithya, ”Intrusion Detection System in IOT Network Environments in DDOS Attack” Infokara Research, vol.9, no.2, pp.719-725, 2020.

  • P. M., D. S.S., and B. J. Rabi, “A novel approach of hierarchical compressive sensing in wireless sensor network using block tridiagonal matrix clustering,” Comput. Commun., vol. 168, pp. 54–64, 2021, doi: https://doi.org/10.1016/j.comcom.2020.12.017.


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  • Hybrid Optimization-Based Efficient Routing Protocol for Energy Consumption Minimization in Mobile Wireless Sensor Network

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Authors

V. Veerakumaran
Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Tamil Nadu, India
Aruchamy Rajini
Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Tamil Nadu, India

Abstract


Mobile Wireless Sensor Network (MWSN) is a dispersed network having autonomous sensor nodes which monitors physical occurrences or environmental variables in real-time. Most MWSNs have limited energy, so energy efficiency is critical. A node’s data will be routed by one of two standard methods: single-long-hop or short-multi-hop routing paths. The quantity of energy required to deliver a packet grows directly proportional to the packet’s travel distance in MWSN. Single-hop communication in MWSN, on the other hand, is typically relatively energy-intensive. The nodes located nearer to the sink are considerably perform well than the rest of the nodes in MWSN because of the multi-hop connection, resulting in a shorter lifespan for the MWSN. In this paper, Hybrid Optimization-based Efficient Routing Protocol (HOERP) is proposed to minimize the energy consumption in MWSN. HOERP involves grey wolf optimization and particle swarm optimization, where local search is done by grey wolf optimization and the global search optimization is done by particle swarm optimization. Utilizing the nonlinear parameters in HOERP assist in identifying the optimized cum successful route leading to consume less energy. HOERP is evaluated in NS3 using the metrics standardly used in network-oriented researches. Result highlights that HOERP consumes less energy to deliver data packets than the current routing protocols.

Keywords


Routing, MWSN, Energy, Delay, Hybrid, Optimization, Simulator, Network

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F215919