International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Ambient Intelligence-Based Fish Swarm Optimization Routing Protocol for Congestion Avoidance in Mobile Ad-Hoc Network


Affiliations
1 Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, India
2 Department of Computer Science, Nehru Arts and Science College, Coimbatore, India
 

In mobile ad hoc networks, path stability estimation is a major difficulty because of connection failures that affect network nodes' mobility. In MANETs, path stability estimates must be based on a unified model that accounts for network node mobility and topology-triggered reactive path distribution statistics between surrounding nodes. It is possible to increase the collaboration between nodes in MANET by implementing an effective, trustworthy cum optimization-based routing protocol. This paper proposes the Ambient Intelligence-based Fish Swarm Optimization Routing Protocol (AIFSORP) to find the most efficient route to a destination and decrease the time and energy required. AIFSORP is designed to mimic the ant's innate instincts to forage its food. In AIFSORP, nodes quickly notify their neighbors when they discover a possible route to their target. Only when the route meets the threshold criterion is it picked for data transmission and shared with neighboring nodes. Optimization plays a significant part in AIFSORP towards determining the best route to the destination. AIFSORP's performance is evaluated using NS3s with standard network metrics. Compared to current routing systems, AIFSORP decreases delays and energy usage more effectively.

Keywords

Routing, Congestion, Delay, MANET, Optimization, Fish-Swarm.
User
Notifications
Font Size

  • N. B. Long, H. Tran-Dang, and D. Kim, “Energy-Aware Real-Time Routing for Large-Scale Industrial Internet of Things,” IEEE Internet Things J., vol. 5, no. 3, pp. 2190–2199, 2018, doi: 10.1109/JIOT.2018.2827050.

  • P. Shi, C. Gu, C. Ge, and Z. Jing, “QoS Aware Routing Protocol Through Cross-layer Approach in Asynchronous Duty-Cycled WSNs,” IEEE Access, vol. 7, pp. 57574–57591, 2019, doi: 10.1109/ACCESS.2019.2913679.

  • E. A. A. Alaoui, S. C. K. Tekouabou, Y. Maleh, and A. Nayyar, “Towards to intelligent routing for DTN protocols using machine learning techniques,” Simul. Model. Pract. Theory, vol. 117, p. 102475, 2022, doi: https://doi.org/10.1016/j.simpat.2021.102475.

  • G. Dhand and K. Sheoran, “Protocols SMEER (Secure Multitier Energy Efficient Routing Protocol) and SCOR (Secure Elliptic curve based Chaotic key Galois Cryptography on Opportunistic Routing),” Mater. Today Proc., vol. 37, pp. 1324–1327, 2021, doi: https://doi.org/10.1016/j.matpr.2020.06.503.

  • 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.

  • J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.

  • P. Menakadevi and J. Ramkumar, “Robust Optimization Based Extreme Learning Machine for Sentiment Analysis in Big Data,” 2022 Int. Conf. Adv. Comput. Technol. Appl., pp. 1–5, Mar. 2022, doi: 10.1109/ICACTA54488.2022.9753203.

  • J. Ramkumar, C. Kumuthini, B. Narasimhan, and S. Boopalan, “Energy Consumption Minimization in Cognitive Radio Mobile Ad-Hoc Networks using Enriched Ad-hoc On-demand Distance Vector Protocol,” 2022 Int. Conf. Adv. Comput. Technol. Appl., pp. 1–6, Mar. 2022, doi: 10.1109/ICACTA54488.2022.9752899.

  • Vadivel, R., and J. Ramkumar. "QoS-enabled improved cuckoo search-inspired protocol (ICSIP) for IoT-based healthcare applications." In Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, pp. 109-121. IGI Global, 2020.

  • 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, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.

  • J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.

  • 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, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.

  • 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, doi: 10.30534/ijeter/2020/82882020.

  • 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, “FLIP: Frog Leap Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks,” in International Conference on Recent Trends in Engineering and Material Sciences (ICEMS - 2016), 2016, p. 248.

  • C. Singhal, S. De, and U. P. Moravapalle, “Optimized mesh routing with intermediate recovery for error resilient delivery of MD coded image/video content,” Comput. Commun., vol. 118, pp. 148–162, 2018, doi: https://doi.org/10.1016/j.comcom.2017.10.016.

  • J. Ramkumar and R. Vadivel, “Improved Wolf prey inspired protocol for routing in cognitive radio Ad Hoc networks,” Int. J. Comput. Networks Appl., vol. 7, no. 5, pp. 126–136, 2020, doi: 10.22247/ijcna/2020/202977.

  • S. Zhao and G. Yu, “Channel allocation optimization algorithm for hybrid wireless mesh networks for information physical fusion system,” Comput. Commun., 2021, doi: https://doi.org/10.1016/j.comcom.2021.07.031.

  • L. Aliouat, H. Mabed, and J. Bourgeois, “Efficient routing protocol for concave unstable terahertz nanonetworks,” Comput. Networks, vol. 179, p. 107375, 2020, doi: https://doi.org/10.1016/j.comnet.2020.107375.

  • S. M. Mostafa, I. M. Darwish, and M. R. Saadi, “Improved lightweight security approach routing protocol in internet of things,” Internet of Things, vol. 11, p. 100208, 2020, doi: https://doi.org/10.1016/j.iot.2020.100208.

  • M. Chen, N. Wang, H. Zhou, and Y. Chen, “FCM technique for efficient intrusion detection system for wireless networks in cloud environment,” Comput. Electr. Eng., vol. 71, pp. 978–987, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.10.011.

  • A. Gaurav and A. K. Singh, “Light weight approach for secure backbone construction for MANETs,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 7, pp. 908–919, 2021, doi: https://doi.org/10.1016/j.jksuci.2018.05.013.

  • X. Pang, M. Liu, Z. Li, B. Gao, and X. Guo, “Geographic Position based Hopless Opportunistic Routing for UAV networks,” Ad Hoc Networks, vol. 120, p. 102560, 2021, doi: https://doi.org/10.1016/j.adhoc.2021.102560.

  • J. Kim, P. K. Biswas, S. Bohacek, S. J. Mackey, S. Samoohi, and M. P. Patel, “Advanced protocols for the mitigation of friendly jamming in mobile ad-hoc networks,” J. Netw. Comput. Appl., vol. 181, p. 103037, 2021, doi: https://doi.org/10.1016/j.jnca.2021.103037.

  • M. Vigenesh and R. Santhosh, “An efficient stream region sink position analysis model for routing attack detection in mobile ad hoc networks,” Comput. Electr. Eng., vol. 74, pp. 273–280, 2019, doi: https://doi.org/10.1016/j.compeleceng.2019.02.005.

  • S. Rashidibajgan and R. Doss, “Privacy-preserving history-based routing in Opportunistic Networks,” Comput. Secur., vol. 84, pp. 244– 255, 2019, doi: https://doi.org/10.1016/j.cose.2019.03.020.

  • S. A. Sharifi and S. M. Babamir, “The clustering algorithm for efficient energy management in mobile ad-hoc networks,” Comput. Networks, vol. 166, p. 106983, 2020, doi: https://doi.org/10.1016/j.comnet.2019.106983.

  • R. S. Krishnan, E. G. Julie, Y. H. Robinson, R. Kumar, P. H. Thong, and L. H. Son, “Enhanced certificate revocation scheme with justification facility in mobile ad-hoc networks,” Comput. Secur., vol. 97, p. 101962, 2020, doi: https://doi.org/10.1016/j.cose.2020.101962.

  • R. Karimi and S. Shokrollahi, “PGRP: Predictive geographic routing protocol for VANETs,” Comput. Networks, vol. 141, pp. 67–81, 2018, doi: https://doi.org/10.1016/j.comnet.2018.05.017.

  • N. S. Saba Farheen and A. Jain, “Improved routing in MANET with optimized multi path routing fine tuned with hybrid modeling,” J. King Saud Univ. - Comput. Inf. Sci., 2020, doi: https://doi.org/10.1016/j.jksuci.2020.01.001.

  • M. Bouhaddi, M. S. Radjef, and K. Adi, “An efficient intrusion detection in resource-constrained mobile ad-hoc networks,” Comput. Secur., vol. 76, pp. 156–177, 2018, doi: https://doi.org/10.1016/j.cose.2018.02.018.

  • S. Naz et al., “A dynamic caching strategy for CCN-based MANETs,” Comput. Networks, vol. 142, pp. 93–107, 2018, doi: https://doi.org/10.1016/j.comnet.2018.05.027.

  • B. P. Santos, O. Goussevskaia, L. F. M. Vieira, M. A. M. Vieira, and A. A. F. Loureiro, “Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT,” Ad Hoc Networks, vol. 78, pp. 84–98, 2018, doi: https://doi.org/10.1016/j.adhoc.2018.05.012.

  • Y. He, F. R. Yu, Z. Wei, and V. Leung, “Trust management for secure cognitive radio vehicular ad hoc networks,” Ad Hoc Networks, vol. 86, pp. 154–165, 2019, doi: https://doi.org/10.1016/j.adhoc.2018.11.006.How to cite this article:

  • M. Zhang, M. Yang, Q. Wu, R. Zheng, and J. Zhu, “Smart perception and autonomic optimization: A novel bio-inspired hybrid routing protocol for MANETs,” Futur. Gener. Comput. Syst., vol. 81, pp. 505–

  • , 2018, doi: https://doi.org/10.1016/j.future.2017.07.030.

  • K. Nabar and G. Kadambi, “Affinity Propagation-driven Distributed clustering approach to tackle greedy heuristics in Mobile Ad-hoc Networks,” Comput. Electr. Eng., vol. 71, pp. 988–1011, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.10.014.

  • J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.

  • K. Sumathi and D. Vimal Kumar, “Minimizing Delay in Mobile Ad-Hoc Network Using Ingenious Grey Wolf Optimization Based Routing Protocol,” Int. J. Comput. Networks Appl., doi: 10.22247/ijcna/2022/212340.

  • K. Sumathi and D. Vimal Kumar, “Energy-Efficient Perspicacious Ant Colony Optimization Based Routing Protocol for Mobile Ad-Hoc Network,” Int. J. Comput. Networks Appl., doi: 10.22247/ijcna/2022/212339.


Abstract Views: 191

PDF Views: 1




  • Ambient Intelligence-Based Fish Swarm Optimization Routing Protocol for Congestion Avoidance in Mobile Ad-Hoc Network

Abstract Views: 191  |  PDF Views: 1

Authors

K. Sumathi
Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, India
D. Vimal Kumar
Department of Computer Science, Nehru Arts and Science College, Coimbatore, India

Abstract


In mobile ad hoc networks, path stability estimation is a major difficulty because of connection failures that affect network nodes' mobility. In MANETs, path stability estimates must be based on a unified model that accounts for network node mobility and topology-triggered reactive path distribution statistics between surrounding nodes. It is possible to increase the collaboration between nodes in MANET by implementing an effective, trustworthy cum optimization-based routing protocol. This paper proposes the Ambient Intelligence-based Fish Swarm Optimization Routing Protocol (AIFSORP) to find the most efficient route to a destination and decrease the time and energy required. AIFSORP is designed to mimic the ant's innate instincts to forage its food. In AIFSORP, nodes quickly notify their neighbors when they discover a possible route to their target. Only when the route meets the threshold criterion is it picked for data transmission and shared with neighboring nodes. Optimization plays a significant part in AIFSORP towards determining the best route to the destination. AIFSORP's performance is evaluated using NS3s with standard network metrics. Compared to current routing systems, AIFSORP decreases delays and energy usage more effectively.

Keywords


Routing, Congestion, Delay, MANET, Optimization, Fish-Swarm.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F212559