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Mobile Ad-hoc Networks (MANETs) are a special kind of network that organize by itself, and they provide high-quality service despite the cost of routing. In MANET, there exist no option to reach other hosts in a single-hop where it needs multi-hop. Many intermediary hosts relay packets transmitted by the source host before reaching the destination host in a multi-hop situation. The level of energy at each node plays a significant role in MANET. Routes are frequently broken, and new routes are discovered in MANETs because of node mobility. In this paper, Energy-Efficient Perspicacious Ant Colony Optimization Based Routing Protocol (EEPACORP) is proposed to determine the optimum route to transfer the data to reduce energy spent by each node for data transmission. EEPACORP is based on the ant's inherent disposition to seek for food. EEPACORP is inspired from genetic character of ant towards finding its food. Pheromone concentration are modified in EEPACORP to find the most appropriate route. The performance of EEPACORP is analyzed in NS3 using standard network metrics. EEPACORP's studies demonstrate that it reduces delays and energy consumption better than the present routing methods.

Keywords

Optimization, Routing, ACO, Delay, MANET, Energy.

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References

M. U. Farooq and M. Zeeshan, “Connected Dominating Set Enabled On-Demand Routing (CDS-OR) for Wireless Mesh Networks,” IEEE Wirel. Commun. Lett., vol. 10, no. 11, pp. 2393–2397, 2021, doi: 10.1109/LWC.2021.3101476.

A. Medina and S. Bohacek, “Performance modeling of neighbor discovery in proactive routing protocols,” J. Adv. Res., vol. 2, no. 3, pp. 227–239, 2011, doi: https://doi.org/10.1016/j.jare.2011.04.007.

D.-G. Zhang et al., “A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing,” IEEE Access, vol. 8, pp. 69058–69071, 2020, doi: 10.1109/ACCESS.2020.2986078.

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.

A. S. Arsalaan, H. Nguyen, A. Coyle, and M. Fida, “Quality of information with minimum requirements for emergency communications,” Ad Hoc Networks, vol. 111, p. 102331, 2021, doi: https://doi.org/10.1016/j.adhoc.2020.102331.

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.

R. Joseph Manoj, M. D. Anto Praveena, and K. Vijayakumar, “An ACO–ANN based feature selection algorithm for big data,” Cluster Comput., vol. 22, no. 2, pp. 3953–3960, Mar. 2019, doi: 10.1007/s10586-018-2550-z.

B. S. P. Mishra, S. Dehuri, and S. B. Cho, “Swarm intelligence in multiple and many objectives optimization: A survey and topical study on EEG signal analysis,” Stud. Comput. Intell., vol. 592, pp. 27–73, 2015, doi: 10.1007/978-3-662-46309-3_2.

N. Ramluckun and V. Bassoo, “Energy-efficient chain-cluster based intelligent routing technique for Wireless Sensor Networks,” Appl. Comput. Informatics, 2020, doi: 10.1016/j.aci.2018.02.004.

W. Wang, B. Yang, O. Takahashi, X. Jiang, and S. Shen, “On the packet delivery delay study for three-dimensional mobile ad hoc networks,” Ad Hoc Networks, vol. 69, pp. 38–48, Feb. 2018, doi: 10.1016/j.adhoc.2017.10.004.

R. Chaudhry and S. Tapaswi, “Optimized power control and efficient energy conservation for topology management of MANET with an adaptive Gabriel graph,” Comput. Electr. Eng., vol. 72, pp. 1021–1036, 2018, doi: https://doi.org/10.1016/j.compeleceng.2018.03.013.

N. Schweitzer, A. Stulman, T. Hirst, R. D. Margalit, and A. Shabtai, “Network bottlenecks in OLSR based ad-hoc networks,” Ad Hoc Networks, vol. 88, pp. 36–54, 2019, doi: https://doi.org/10.1016/j.adhoc.2018.12.002.

A. Tahir, N. Shah, S. A. Abid, W. Z. Khan, A. K. Bashir, and Y. Bin Zikria, “A three-dimensional clustered peer-to-peer overlay protocol for mobile ad hoc networks,” Comput. Electr. Eng., vol. 94, p. 107364, 2021, doi: https://doi.org/10.1016/j.compeleceng.2021.107364.

B. Hammi, S. Zeadally, H. Labiod, R. Khatoun, Y. Begriche, and L. Khoukhi, “A secure multipath reactive protocol for routing in IoT and HANETs,” Ad Hoc Networks, vol. 103, p. 102118, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102118.

J. Arshad, M. A. Azad, K. Salah, R. Iqbal, M. I. Tariq, and T. Umer, “Performance analysis of content discovery for ad-hoc tactile networks,” Futur. Gener. Comput. Syst., vol. 94, pp. 726–739, 2019, doi: https://doi.org/10.1016/j.future.2018.11.037.

W. A. Hussein, B. M. Ali, M. F. A. Rasid, and F. Hashim, “Smart geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia sensor networks,” Egypt. Informatics J., 2022, doi: https://doi.org/10.1016/j.eij.2021.12.005.

D. Zhang, Y. Cui, and T. Zhang, “New quantum-genetic based OLSR protocol (QG-OLSR) for Mobile Ad hoc Network,” Appl. Soft Comput., vol. 80, pp. 285–296, 2019, doi: https://doi.org/10.1016/j.asoc.2019.03.053.

S. V Simpson and G. Nagarajan, “A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment,” Futur. Gener. Comput. Syst., vol. 125, pp. 544–563, 2021, doi: https://doi.org/10.1016/j.future.2021.06.052.

L. E. Jim, N. Islam, and M. A. Gregory, “Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes,” Comput. Secur., vol. 113, p. 102538, 2022, doi: https://doi.org/10.1016/j.cose.2021.102538.

D. B.D. and F. Al-Turjman, “A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks,” Ad Hoc Networks, vol. 97, p. 102022, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102022.

M. Usman, M. A. Jan, X. He, and P. Nanda, “QASEC: A secured data communication scheme for mobile Ad-hoc networks,” Futur. Gener. Comput. Syst., vol. 109, pp. 604–610, 2020, doi: https://doi.org/10.1016/j.future.2018.05.007.

A. E. Hilal and A. B. MacKenzie, “A distributed coalition game model for cooperation in MANETs,” Ad Hoc Networks, vol. 85, pp. 46–59, 2019, doi: https://doi.org/10.1016/j.adhoc.2018.10.019.

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, “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, “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.

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. How to cite this article:

R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.

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, “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.

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.

Minimizing Delay in Mobile Ad-Hoc Network Using Ingenious Grey Wolf Optimization Based Routing Protocol

Abstract Views :196 | PDF Views:2

Authors

K. Sumathi ¹, D. Vimal Kumar ¹

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

Source

International Journal of Computer Networks and Applications, Vol 9, No 2 (2022), Pagination: 251-261

Abstract

One of the most groundbreaking concepts in wireless networking is the mobile ad hoc network (MANET). It is an ever-shifting network of wireless nodes that may be adaptively and indiscriminately positioned, with the interconnections between nodes constantly changing. Defense networks, in particular, are becoming more prominent, and it is the goal and passion of technology to update and improve its components. There is a significant rise in transmission costs due to the high energy usage. Routing protocols have a critical role in reducing energy utilization. Weak routing protocol leads to exhaustive energy consumption, packet delay and packet loss. Ingenious Grey Wolf Optimization-based Routing Protocol (IGWORP) is proposed in this paper to discover the most efficient path to a destination and reduce the amount of delay and energy spent. IGWORP mirrors the natural tendencies of the grey wolf towards foraging for its prey. IGWORP looks for a global route rather than assembling many local routes. Encircling and hunting characteristics of wolves are used in IGWORP to discover and utilize the route for data transmission. Standard network metrics are used in NS3 to evaluate IGWORP's performance. The findings of IGWORP demonstrate that it reduces delays and energy consumption better than the current routing methods.

Keywords

Delay, Routing, Optimization, Wolf, Delay, Energy.

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References

L.-L. Wang, J.-S. Gui, X.-H. Deng, F. Zeng, and Z.-F. Kuang, “Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles,” IEEE Internet Things J., vol. 7, no. 12, pp. 11701–11712, 2020, doi: 10.1109/JIOT.2020.2999469.

B. Su, C. Du, and J. Huan, “Trusted Opportunistic Routing Based on Node Trust Model,” IEEE Access, vol. 8, pp. 163077–163090, 2020, doi: 10.1109/ACCESS.2020.3020129.

S. Amutha and K. Balasubramanian, “Secured energy optimized Ad hoc on-demand distance vector routing protocol,” Comput. Electr. Eng., vol. 72, pp. 766–773, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.11.031.

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.

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, “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., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.

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.

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.

R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.

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.

A. Patwardhan, J. Parker, M. Iorga, A. Joshi, T. Karygiannis, and Y. Yesha, “Threshold-based intrusion detection in ad hoc networks and secure AODV,” Ad Hoc Networks, vol. 6, no. 4, pp. 578–599, 2008, doi: https://doi.org/10.1016/j.adhoc.2007.05.001.

O. S. Younes and U. A. Albalawi, “Analysis of Route Stability in Mobile Multihop Networks Under Random Waypoint Mobility,” IEEE Access, vol. 8, pp. 168121–168136, 2020, doi: 10.1109/ACCESS.2020.3023142.

M. A. K. Akhtar and G. Sahoo, “Enhancing cooperation in MANET using neighborhood compressive sensing model,” Egypt. Informatics J., vol. 22, no. 3, pp. 373–387, 2021, doi: https://doi.org/10.1016/j.eij.2016.06.007.

B. Yang, Z. Wu, Y. Shen, X. Jiang, and S. Shen, “On delay performance study for cooperative multicast MANETs,” Ad Hoc Networks, vol. 102, p. 102117, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102117.

M. A. Gawas and S. S. Govekar, “A novel selective cross layer based routing scheme using ACO method for vehicular networks,” J. Netw. Comput. Appl., vol. 143, pp. 34–46, 2019, doi: https://doi.org/10.1016/j.jnca.2019.05.010.

L. Zhang, L. Hu, F. Hu, Z. Ye, X. Li, and S. Kumar, “Enhanced OLSR routing for airborne networks with multi-beam directional antennas,” Ad Hoc Networks, vol. 102, p. 102116, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102116.

I. Manolopoulos, K. Kontovasilis, I. Stavrakakis, and S. C. A. Thomopoulos, “Methodologies for calculating decision-related event occurrence times, with applications to effective routing in diverse MANET environments,” Ad Hoc Networks, vol. 99, p. 102068, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102068.

D. Sarkar, S. Choudhury, and A. Majumder, “Enhanced-Ant-AODV for optimal route selection in mobile ad-hoc network,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 1186–1201, 2021, doi: https://doi.org/10.1016/j.jksuci.2018.08.013.

D. S. K. Tiruvakadu and V. Pallapa, “Confirmation of wormhole attack in MANETs using honeypot,” Comput. Secur., vol. 76, pp. 32–49, 2018, doi: https://doi.org/10.1016/j.cose.2018.02.004.

C. R. da C. Bento and E. C. G. Wille, “Bio-inspired routing algorithm for MANETs based on fungi networks,” Ad Hoc Networks, vol. 107, p. 102248, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102248.

A. M. Bamhdi, “Efficient dynamic-power AODV routing protocol based on node density,” Comput. Stand. Interfaces, vol. 70, p. 103406, 2020, doi: https://doi.org/10.1016/j.csi.2019.103406.

L. A. L. F. da Costa, R. Kunst, and E. Pignaton de Freitas, “Q-FANET: Improved Q-learning based routing protocol for FANETs,” Comput. Networks, vol. 198, p. 108379, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108379.

V. K. Sharma, L. P. Verma, and M. Kumar, “CL-ADSP: Cross-Layer Adaptive Data Scheduling Policy in Mobile Ad-hoc Networks,” Futur. Gener. Comput. Syst., vol. 97, pp. 530–563, 2019, doi: https://doi.org/10.1016/j.future.2019.03.013.

B. Deokate, C. Lal, D. Trček, and M. Conti, “Mobility-aware cross-layer routing for peer-to-peer networks,” Comput. Electr. Eng., vol. 73, pp. 209–226, 2019, doi: https://doi.org/10.1016/j.compeleceng.2018.11.014.

D. Zhang, T. Zhang, Y. Dong, X. Liu, Y. Cui, and D. Zhao, “Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning,” J. Netw. Comput. Appl., vol. 122, pp. 37–49, 2018, doi: https://doi.org/10.1016/j.jnca.2018.07.018.

K. Poularakis, Q. Qin, E. M. Nahum, M. Rio, and L. Tassiulas, “Flexible SDN control in tactical ad hoc networks,” Ad Hoc Networks, vol. 85, pp. 71–80, 2019, doi: https://doi.org/10.1016/j.adhoc.2018.10.012.

A. Chriki, H. Touati, H. Snoussi, and F. Kamoun, “FANET: Communication, mobility models and security issues,” Comput. Networks, vol. 163, p. 106877, 2019, doi: https://doi.org/10.1016/j.comnet.2019.106877.

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.

S. Dalal et al., “An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks,” Soft Comput. 2022, pp. 1–12, Apr. 2022, doi: 10.1007/S00500-022-07099-4.

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 ¹, D. Vimal Kumar ²

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

Source

International Journal of Computer Networks and Applications, Vol 9, No 3 (2022), Pagination: 340-349

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.

Full Text

References

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.

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