An energy-aware routing method using firefly algorithm for flying ad hoc networks
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
PubMed
36693862
PubMed Central
PMC9873979
DOI
10.1038/s41598-023-27567-7
PII: 10.1038/s41598-023-27567-7
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
Flying ad-hoc networks (FANETs) include a large number of drones, which communicate with each other based on an ad hoc model. These networks provide new opportunities for various applications such as military, industrial, and civilian applications. However, FANETs have faced with many challenges like high-speed nodes, low density, and rapid changes in the topology. As a result, routing is a challenging issue in these networks. In this paper, we propose an energy-aware routing scheme in FANETs. This scheme is inspired by the optimized link state routing (OLSR). In the proposed routing scheme, we estimate the connection quality between two flying nodes using a new technique, which utilizes two parameters, including ratio of sent/received of hello packets and connection time. Also, our proposed method selects multipoint relays (MPRs) using the firefly algorithm. It chooses a node with high residual energy, high connection quality, more neighborhood degree, and higher willingness as MPR. Finally, our proposed scheme creates routes between different nodes based on energy and connection quality. Our proposed routing scheme is simulated using the network simulator version 3 (NS3). We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the optimized link state routing (OLSR). These results show that our method outperforms G-OLSR and OLSR in terms of delay, packet delivery rate, throughput, and energy consumption. However, our proposed routing scheme increases slightly routing overhead compared to G-OLSR.
Computer Science University of Human Development Sulaymaniyah Iraq
Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran
Institute of Research and Development Duy Tan University Da Nang Vietnam
Zobrazit více v PubMed
Hosseinzadeh M, Tho QT, Ali S, Rahmani AM, Souri A, Norouzi M, Huynh B. A hybrid service selection and composition model for cloud-edge computing in the internet of things. IEEE Access. 2020;8:85939–85949. doi: 10.1109/ACCESS.2020.2992262. DOI
Rahmani AM, Ali S, Yousefpoor MS, Yousefpoor E, Naqvi RA, Siddique K, Hosseinzadeh M. An area coverage scheme based on fuzzy logic and shuffled frog-leaping algorithm (sfla) in heterogeneous wireless sensor networks. Mathematics. 2021;9(18):2251. doi: 10.3390/math9182251. DOI
Yousefpoor MS, Yousefpoor E, Barati H, Barati A, Movaghar A, Hosseinzadeh M. Secure data aggregation methods and countermeasures against various attacks in wireless sensor networks: A comprehensive review. J. Netw. Comput. Appl. 2021;190:103118. doi: 10.1016/j.jnca.2021.103118. DOI
Rezwan S, Choi W. A survey on applications of reinforcement learning in flying ad-hoc networks. Electronics. 2021;10(4):449. doi: 10.3390/electronics10040449. DOI
Mesbahi MR, Rahmani AM, Hosseinzadeh M. Highly reliable architecture using the 80/20 rule in cloud computing datacenters. Futur. Gener. Comput. Syst. 2017;77:77–86. doi: 10.1016/j.future.2017.06.011. DOI
Mohammadi M, Rashid TA, Karim SHT, Aldalwie AHM, Tho QT, Bidaki M, Rahmani AM, Hosseinzadeh M. A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. J. Netw. Comput. Appl. 2021;178:102983. doi: 10.1016/j.jnca.2021.102983. DOI
Sadrishojaei M, Navimipour NJ, Reshadi M, Hosseinzadeh M. A new preventive routing method based on clustering and location prediction in the mobile internet of things. IEEE Internet Things J. 2021;8(13):10652–10664. doi: 10.1109/JIOT.2021.3049631. DOI
Zhang H, Song L, Han Z. Unmanned Aerial Vehicle Applications Over Cellular Networks for 5G and Beyond. Springer; 2020.
Bhardwaj V, Kaur N, Vashisht S, Jain S. SecRIP: Secure and reliable intercluster routing protocol for efficient data transmission in flying ad hoc networks. Trans. Emerg. Telecommun. Technol. 2021;32(6):e4068. doi: 10.1002/ett.4068. DOI
Darabkh KA, Alfawares MG, Althunibat S. MDRMA: Multi-data rate mobility-aware AODV-based protocol for flying ad-hoc networks. Veh. Commun. 2019;18:100163. doi: 10.1016/j.vehcom.2019.100163. DOI
Yousefpoor MS, Barati H. Dynamic key management algorithms in wireless sensor networks: A survey. Comput. Commun. 2019;134:52–69. doi: 10.1016/j.comcom.2018.11.005. DOI
Kumar S, Raw RS, Bansal A, Mohammed MA, Khuwuthyakorn P, Thinnukool O. 3D location oriented routing in flying ad-hoc networks for information dissemination. IEEE Access. 2021;9:137083–137098. doi: 10.1109/ACCESS.2021.3115000. DOI
Yousefpoor MS, Barati H. DSKMS: A dynamic smart key management system based on fuzzy logic in wireless sensor networks. Wirel. Netw. 2020;26(4):2515–2535. doi: 10.1007/s11276-019-01980-1. DOI
Lakew DS, Sa’ad U, Dao NN, Na W, Cho S. Routing in flying ad hoc networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 2020;22(2):1071–1120. doi: 10.1109/COMST.2020.2982452. DOI
Yousefpoor E, Barati H, Barati A. A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks. Peer-to-Peer Netw. Appl. 2021;14(4):1917–1942. doi: 10.1007/s12083-021-01116-3. DOI
Arafat MY, Poudel S, Moh S. Medium access control protocols for flying ad hoc networks: A review. IEEE Sens. J. 2020;21(4):4097–4121. doi: 10.1109/JSEN.2020.3034600. DOI
Singh K, Verma AK. TBCS: A trust based clustering scheme for secure communication in flying ad-hoc networks. Wireless Pers. Commun. 2020;114(4):3173–3196. doi: 10.1007/s11277-020-07523-8. DOI
Bharany S, Sharma S, Badotra S, Khalaf OI, Alotaibi Y, Alghamdi S, Alassery F. Energy-efficient clustering scheme for flying ad-hoc networks using an optimized LEACH protocol. Energies. 2021;14(19):6016. doi: 10.3390/en14196016. DOI
Kaur M, Singh A, Verma S, Jhanjhi NZ, Talib MN, et al. FANET: Efficient routing in flying ad hoc networks (FANETs) using firefly algorithm. In: Kaur M, et al., editors. Intelligent Computing and Innovation on Data Science. Springer; 2021. pp. 483–490.
Khanmohammadi E, Barekatain B, Quintana AA. An enhanced AHP-TOPSIS-based clustering algorithm for high-quality live video streaming in flying ad hoc networks. J. Supercomput. 2021;77(9):10664–10698. doi: 10.1007/s11227-021-03645-3. DOI
Khan A, Khan S, Fazal AS, Zhang Z, Abuassba AO. Intelligent cluster routing scheme for flying ad hoc networks. Sci. China Inf. Sci. 2021;64(8):1–14. doi: 10.1007/s11432-019-2984-7. DOI
Tan Y, Liu J, Kato N. Blockchain-based key management for heterogeneous flying ad hoc network. IEEE Trans. Ind. Inf. 2020;17(11):7629–7638. doi: 10.1109/TII.2020.3048398. DOI
Srivastava A, Prakash J. Future FANET with application and enabling techniques: Anatomization and sustainability issues. Comput. Sci. Rev. 2021;39:100359. doi: 10.1016/j.cosrev.2020.100359. DOI
Khan IU, Shah SBH, Wang L, Aziz MA, Stephan T, Kumar N. Routing protocols & unmanned aerial vehicles autonomous localization in flying networks. Int. J. Commun. Syst. 2021 doi: 10.1002/dac.4885. DOI
Agrawal J, Kapoor M. A comparative study on geographic-based routing algorithms for flying ad-hoc networks. Concurr. Comput. Pract. Exp. 2021;33(16):e6253. doi: 10.1002/cpe.6253. DOI
Yadav A, Verma S. A hybrid approach based on ACO and firefly algorithm for routing in FANETs. In: Yadav A, Verma S, editors. International Conference on Computing Science, Communication and Security. Springer; 2021. pp. 234–246.
Bvijitha Ananthi J, Subha Hency P. Mobile Computing and Sustainable Informatics. Springer; 2022. A review on various routing protocol designing features for flying ad hoc networks; pp. 315–325.
Ibrahim MMS, Shanmugaraja P. Optimized link state routing protocol performance in flying ad-hoc networks for various data rates of un manned aerial network. Mater. Today Proc. 2021;37:3561–3568. doi: 10.1016/j.matpr.2020.09.543. DOI
Yang XS. Firefly Algorithm (Chapter 8). Nature-inspired Metaheuristic Algorithms. Luniver Press; 2008.
Lee SW, Ali S, Yousefpoor MS, Yousefpoor E, Lalbakhsh P, Javaheri D, Rahmani AM, Hosseinzadeh M. An energy-aware and predictive fuzzy logic-based routing scheme in flying ad hoc networks (fanets) IEEE Access. 2021;9:129977–130005. doi: 10.1109/ACCESS.2021.3111444. DOI
Mahmud I, Cho YZ. LECAR: Location estimation-based congestion-aware routing protocol for sparsely deployed energy-efficient UAVs. Sensors. 2021;21(21):7192. doi: 10.3390/s21217192. PubMed DOI PMC
Clausen, T. et al. Optimized Link State Routing Protocol (OLSR) (2003).
Ali H, Islam SU, Song H, Munir K. A performance-aware routing mechanism for flying ad hoc networks. Trans. Emerg. Telecommun. Technol. 2021;32(1):e4192. doi: 10.1002/ett.4192. DOI
Rahmani AM, Ali S, Yousefpoor E, et al. OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs) Veh. Commun. 2022 doi: 10.1016/j.vehcom.2022.100489. DOI
Ma Z, Guo Q, Ma J, Zhang Z, Ma H, Peng L, Li Y. VaSe-MRP: Velocity-aware and stability-estimation-based multi-path routing protocol in flying ad hoc network. Int. J. Distrib. Sens. Netw. 2019;15(11):1550147719883128. doi: 10.1177/1550147719883128. DOI
Chen YN, Lyu NQ, Song GH, Yang BW, Jiang XH. A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks. Front. Inf. Technol. Electron. Eng. 2020;21(9):1308–1320. doi: 10.1631/FITEE.1900401. DOI
Oubbati OS, Mozaffari M, Chaib N, Lorenz P, Atiquzzaman M, Jamalipour A. ECaD: Energy-efficient routing in flying ad hoc networks. Int. J. Commun. Syst. 2019;32(18):e4156. doi: 10.1002/dac.4156. DOI
Liu J, Wang Q, He C, Jaffrès-Runser K, Xu Y, Li Z, Xu Y. QMR: Q-learning based multi-objective optimization routing protocol for flying ad hoc networks. Comput. Commun. 2020;150:304–316. doi: 10.1016/j.comcom.2019.11.011. DOI