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AFSORP: Adaptive Fish Swarm Optimization-Based Routing Protocol for Mobility Enabled Wireless Sensor Network


Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Cuddalore, Tamil Nadu, India
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, India
4 Department of Computer and Information Science, Annamalai University, Cuddalore, Tamil Nadu, India
 

Advances in information and communication technology and electronics have led to a surge in interest in mobility-enabled wireless sensor networks (MEWSN). These minuscule sensor nodes collect data, process it, and then transmit it via a radio frequency channel to a central station or sink. Most of the time, MEWSNs are placed in hazardous or difficult-to-access locations. To increase the lifespan of a network, available resources must be utilized as efficiently as possible. The whole network connection collapses if even one node loses power, rendering the deployment's goals moot. Therefore, much MEWSN research has focused on energy efficiency, with energy-efficient routing protocols being a key component. This paper proposes an Adaptive Fish Swarm Optimization-based Routing Protocol (AFSORP) for identifying the best route in MEWSN. AFSORP functions based on the natural characteristics of fish. The two most important steps in AFSORP are chasing and blocking, which respectively seek the optimal route and choose the appropriate route to send data from the source node to the destination node. Standard network performance measurements are used to assess AFSORP with the help of the GNS3 simulator. The results show that AFSORP performs better than the existing routing methods.

Keywords

Routing, Mobility, WSN, MEWSN, Optimization, Fish, Energy.
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  • F. R. Mughal et al., “A new Asymmetric Link Quality Routing protocol (ALQR) for heterogeneous WSNs,” Microprocess. Microsyst., vol. 93, p. 104617, 2022, doi: https://doi.org/10.1016/j.micpro.2022.104617.

  • R. Kumar, S. Shekhar, H. Garg, M. Kumar, B. Sharma, and S. Kumar, “EESR: Energy efficient sector-based routing protocol for reliable data communication in UWSNs,” Comput. Commun., vol. 192, pp. 268–278, 2022, doi: https://doi.org/10.1016/j.comcom.2022.06.011.

  • H. Li, S. Wang, Q. Chen, M. Gong, and L. Chen, “IPSMT: Multi-objective optimization of multipath transmission strategy based on improved immune particle swarm algorithm in wireless sensor networks,” Appl. Soft Comput., vol. 121, p. 108705, 2022, doi: https://doi.org/10.1016/j.asoc.2022.108705.

  • Z. Guo and H. Chen, “A reinforcement learning-based sleep scheduling algorithm for cooperative computing in event-driven wireless sensor networks,” Ad Hoc Networks, vol. 130, p. 102837, 2022, doi: https://doi.org/10.1016/j.adhoc.2022.102837.

  • S. Mavinkattimath and R. Khanai, “A low power and high-speed hardware accelerator for Wireless Body Sensor Network (WBSN),” Mater. Today Proc., 2022, doi: https://doi.org/10.1016/j.matpr.2022.06.013.

  • 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, 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/s11277-021-08495-z.

  • R. Jaganathan 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, no. 1, pp. 1063–1074, 2021, doi: 10.12785/ijcds/100196.

  • A. Behura and M. R. Kabat, “Chapter 13 - Optimization-based energy-efficient routing scheme for wireless body area network,” in Cognitive Data Science in Sustainable Computing, S. Mishra, H. K. Tripathy, P. K. Mallick, A. K. Sangaiah, and G.-S. B. T.-C. B. D. I. with a M. A. Chae, Eds. Academic Press, 2022, pp. 279–303. doi: https://doi.org/10.1016/B978-0-323-85117-6.00016-9.

  • M. F. Carsancakli, M. A. Al Imran, H. U. Yildiz, A. Kara, and B. Tavli, “Reliability of linear WSNs: A complementary overview and analysis of impact of cascaded failures on network lifetime,” Ad Hoc Networks, vol. 131, p. 102839, 2022, doi: https://doi.org/10.1016/j.adhoc.2022.102839.

  • V. Kavitha and K. Ganapathy, “Galactic swarm optimized convolute network and cluster head elected energy-efficient routing protocol in WSN,” Sustain. Energy Technol. Assessments, vol. 52, p. 102154, 2022, doi: https://doi.org/10.1016/j.seta.2022.102154.

  • A. Sundar Raj and M. Chinnadurai, “Energy efficient routing algorithm in wireless body area networks for smart wearable patches,” Comput. Commun., vol. 153, pp. 85–94, 2020, doi: https://doi.org/10.1016/j.comcom.2020.01.069.

  • A. S. Toor and A. K. Jain, “Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks,” AEU - Int. J. Electron. Commun., vol. 102, pp. 41–53, 2019, doi: https://doi.org/10.1016/j.aeue.2019.02.006.

  • J. E. Z. Gbadouissa, A. A. A. Ari, C. Titouna, A. M. Gueroui, and O. Thiare, “HGC: HyperGraph based Clustering scheme for power aware wireless sensor networks,” Futur. Gener. Comput. Syst., vol. 105, pp. 175–183, Apr. 2020, doi: https://doi.org/10.1016/j.future.2019.11.043.

  • 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.

  • T. Nath and M. Azharuddin, “Application of wireless sensor networks for Rhino protection against poachers in Kaziranga National Park,” AEU - Int. J. Electron. Commun., vol. 111, p. 152882, Nov. 2019, doi: 10.1016/J.AEUE.2019.152882.

  • A. Bereketli, M. Tümçakır, and B. Yeni, “P-AUV: Position aware routing and medium access for ad hoc AUV networks,” J. Netw. Comput. Appl., vol. 125, pp. 146–154, Jan. 2019, doi: 10.1016/J.JNCA.2018.10.014.

  • D. Adhikari, D. Datta, and R. Datta, “Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks,” Comput. Networks, vol. 172, p. 107167, May 2020, doi: 10.1016/J.COMNET.2020.107167.

  • J. Liu et al., “QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks,” Comput. Commun., vol. 150, pp. 304–316, 2020, doi: https://doi.org/10.1016/j.comcom.2019.11.011.

  • H. Zemrane, Y. Baddi, and A. Hasbi, “Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols,” Procedia Comput. Sci., vol. 160, pp. 758–765, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.014.

  • J. Wang, H. Zhang, X. Tang, and Z. Li, “Delay-tolerant routing and message scheduling for CR-VANETs,” Futur. Gener. Comput. Syst., vol. 110, pp. 291–309, 2020, doi: https://doi.org/10.1016/j.future.2020.04.026.

  • P. Chithaluru, R. Tiwari, and K. Kumar, “AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network,” Comput. Networks, vol. 162, p. 106863, 2019, doi: https://doi.org/10.1016/j.comnet.2019.106863.

  • Lingaraj M and Prakash A, “Power Aware Routing Protocol (PARP) to Reduce Energy Consumption in Wireless Sensor Networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, Jan. 2019, Accessed: Apr. 07, 2021. [Online]. Available: https://www.ijrte.org/wp-content/uploads/papers/v7i5/E1969017519.pdf

  • F. Al-Salti, N. Alzeidi, K. Day, and A. Touzene, “An efficient and reliable grid-based routing protocol for UWSNs by exploiting minimum hop count,” Comput. Networks, vol. 162, p. 106869, Oct. 2019, doi: 10.1016/J.COMNET.2019.106869.

  • 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.

  • B. Chakraborty, S. Verma, and K. P. Singh, “Temporal Differential Privacy in Wireless Sensor Networks,” J. Netw. Comput. Appl., vol. 155, p. 102548, 2020, doi: https://doi.org/10.1016/j.jnca.2020.102548.


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  • AFSORP: Adaptive Fish Swarm Optimization-Based Routing Protocol for Mobility Enabled Wireless Sensor Network

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Authors

D. Jayaraj
Department of Computer Science and Engineering, Annamalai University, Cuddalore, Tamil Nadu, India
J. Ramkumar
Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India
M. Lingaraj
Department of Computer Science and Applications, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, India
B. Sureshkumar
Department of Computer and Information Science, Annamalai University, Cuddalore, Tamil Nadu, India

Abstract


Advances in information and communication technology and electronics have led to a surge in interest in mobility-enabled wireless sensor networks (MEWSN). These minuscule sensor nodes collect data, process it, and then transmit it via a radio frequency channel to a central station or sink. Most of the time, MEWSNs are placed in hazardous or difficult-to-access locations. To increase the lifespan of a network, available resources must be utilized as efficiently as possible. The whole network connection collapses if even one node loses power, rendering the deployment's goals moot. Therefore, much MEWSN research has focused on energy efficiency, with energy-efficient routing protocols being a key component. This paper proposes an Adaptive Fish Swarm Optimization-based Routing Protocol (AFSORP) for identifying the best route in MEWSN. AFSORP functions based on the natural characteristics of fish. The two most important steps in AFSORP are chasing and blocking, which respectively seek the optimal route and choose the appropriate route to send data from the source node to the destination node. Standard network performance measurements are used to assess AFSORP with the help of the GNS3 simulator. The results show that AFSORP performs better than the existing routing methods.

Keywords


Routing, Mobility, WSN, MEWSN, Optimization, Fish, Energy.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F218516