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Jeen Marseline, K. S.
- Resilient Artificial Bee Colony Optimized AODV Routing Protocol (RABCO-AODV-RP) for Minimizing the Energy Consumption in Flying Ad-Hoc Network
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Authors
Affiliations
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, Coimbatore, IN
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, Coimbatore, IN
Source
International Journal of Computer Networks and Applications, Vol 11, No 1 (2024), Pagination: 29-45Abstract
Flying Ad-Hoc Networks (FANETs) have gained prominence in various applications, ranging from surveillance to disaster response. Their dynamic and resource-constrained nature makes efficient energy utilization a paramount concern. One significant challenge in FANETs is minimizing energy consumption, which is essential for prolonging the network lifetime and ensuring continuous operation. This paper introduces the Resilient Artificial Bee Colony Optimized AODV Routing Protocol (RABCO-AODV-RP) to address this challenge. RABCO-AODV-RP leverages the Artificial Bee Colony optimization algorithm to enhance AODV routing, optimizing route selection to minimize energy consumption while maintaining network resilience. The working mechanism of RABCO-AODV-RP encompasses two primary phases: route discovery and route maintenance. During route discovery, the protocol intelligently selects energy-efficient paths using the optimization algorithm, reducing energy waste. In the route maintenance phase, RABCO-AODV-RP continuously adapts to network dynamics, updating routes to ensure efficient and resilient communication. Extensive simulations were conducted using the NS3 network simulator to assess its performance using packet delivery ratio, packet drop ratio, throughput, end-to-end delay, energy consumption and hop count as performance metrics. The results and discussions indicate that RABCO-AODV-RP outperforms traditional AODV routing protocol. It improves packet delivery, throughput and reduces packet drop ratio, end-to-end delay and hop count. This research underscores the potential of RABCO-AODV-RP as a promising solution for extending the operational lifetime of FANETs and ensuring reliable communication in demanding environments.Keywords
UAV, ABC, AODV, Optimization, FANET, Routing, Energy.Full Text
References
- B. Wang, Y. Sun, T. Do-Duy, E. Garcia-Palacios, and T. Q. Duong, “Adaptive D-Hop Connected Dominating Set in Highly Dynamic Flying Ad-Hoc Networks,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 3, pp. 2651–2664, 2021, doi: 10.1109/TNSE.2021.3103873.
- O. Bautista, K. Akkaya, and A. S. Uluagac, “Customized novel routing metrics for wireless mesh-based swarm-of-drones applications,” Internet of Things (Netherlands), vol. 11, 2020, doi: 10.1016/j.iot.2020.100265.
- J. Zhang, X. Wu, S. C. H. Hoi, and J. Zhu, “Feature agglomeration networks for single stage face detection,” Neurocomputing, vol. 380, pp. 180–189, 2020, doi: 10.1016/j.neucom.2019.10.087.
- A. Bujari, C. E. Palazzi, and D. Ronzani, “A Comparison of Stateless Position-based Packet Routing Algorithms for FANETs,” IEEE Trans. Mob. Comput., vol. 17, no. 11, pp. 2468–2482, 2018, doi: 10.1109/TMC.2018.2811490.
- M. M. S. Ibrahim and P. Shanmugaraja, “Optimized link state routing protocol performance in flying ad-hoc networks for various data rates of Un manned aerial network,” Mater. Today Proc., vol. 37, no. Part 2, pp. 3561–3568, 2020, doi: 10.1016/j.matpr.2020.09.543.
- 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.
- 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 V. Ramasamy, “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, S. S. Dinakaran, M. Lingaraj, S. Boopalan, and B. Narasimhan, “IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion,” in Lecture Notes in Electrical Engineering, 2023, vol. 975, pp. 17–27. doi: 10.1007/978-981-19-8353-5_2.
- C. Liu, Y. Wang, and Q. Wang, “PARouting: Prediction-supported adaptive routing protocol for FANETs with deep reinforcement learning,” Int. J. Intell. Networks, vol. 4, pp. 113–121, 2023, doi: 10.1016/j.ijin.2023.05.002.
- J. Liu, Q. Wang, and Y. Xu, “AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning,” Comput. Networks, vol. 218, p. 109382, 2022, doi: 10.1016/j.comnet.2022.109382.
- 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: 10.1016/j.comnet.2021.108379.
- A. M. Tadkal and S. V Mallapur, “Red deer optimization algorithm inspired clustering-based routing protocol for reliable data dissemination in FANETs,” Mater. Today Proc., vol. 60, pp. 1882–1889, 2022, doi: 10.1016/j.matpr.2021.12.527.
- V. Sharma, R. Kumar, and N. Kumar, “DPTR: Distributed priority tree-based routing protocol for FANETs,” Comput. Commun., vol. 122, pp. 129–151, 2018, doi: 10.1016/j.comcom.2018.03.002.
- P. Mittal, S. Shah, A. Agarwal, D. Mishra, and S. Debnath, “Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs,” Ad Hoc Networks, vol. 150, p. 103280, 2023, doi: 10.1016/j.adhoc.2023.103280.
- A. M. Rahmani et al., “OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs),” Veh. Commun., vol. 36, p. 100489, 2022, doi: 10.1016/j.vehcom.2022.100489.
- A. M. Khedr, A. Salim, P. Raj P V, and W. Osamy, “MWCRSF: Mobility-based weighted cluster routing scheme for FANETs,” Veh. Commun., vol. 41, p. 100603, 2023, doi: 10.1016/j.vehcom.2023.100603.
- J. J. López Escobar, M. Ricardo, R. Campos, F. Gil-Castiñeira, and R. P. Díaz Redondo, “Resource allocation for dataflow applications in FANETs using anypath routing,” Internet of Things (Netherlands), vol. 22, p. 100761, 2023, doi: 10.1016/j.iot.2023.100761.
- D. Hu, S. Yang, M. Gong, Z. Feng, and X. Zhu, “A Cyber–Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks,” Engineering, vol. 19, pp. 217–227, 2022, doi: 10.1016/j.eng.2021.10.022.
- H. Yang and Z. Liu, “An optimization routing protocol for FANETs,” Eurasip J. Wirel. Commun. Netw., vol. 2019, no. 1, 2019, doi: 10.1186/s13638-019-1442-0.
- I. U. Khan, I. M. Qureshi, M. A. Aziz, T. A. Cheema, and S. B. H. Shah, “Smart IoT control-based nature inspired energy efficient routing protocol for Flying Ad Hoc Network (FANET),” IEEE Access, vol. 8, pp. 56371–56378, 2020, doi: 10.1109/ACCESS.2020.2981531.