Volume 3 , Issue 1 , PP: 17-25, 2021 | Cite this article as | XML | PDF | Full Length Article
Mohamed Elhoseny 1 , X. Yuan 2 , Mohamed Abdel-basset 3
Doi: https://doi.org/10.54216/IJWAC.030102
Recently, unmanned aerial vehicles (UAV) have gained maximum interest in diverse applications ranging from military to civilian areas. The presence of numerous energy-constrained UAVs in an adhoc manner poses several design issues. At the same time, the limited battery, high mobility, and adaptive characteristics of the UAVs need effective design of clustering techniques for UAVs. In this manner, this paper presents a levy flight with a krill herd optimization algorithm (LF-KHOA) for energy-efficient clustering in UAVs. The proposed LF-KHOA technique integrates the concepts of LF to the KHOA to enhance efficiency and search space exploration. In addition, the LF-KHOA technique derives a fitness function involving three input parameters to elect cluster heads (CHs) and organize clusters. The energy consumed by the UAVs depends on the distance from UAVs to nearby nodes. Therefore, the fitness function aims to decrease communication distance, which mitigates energy utilization when transmitting the information. To ensure the better performance of the LF-KHOA technique, an extensive set of simulations takes place, and the results are inspected in terms of different measures. The experimental results highlighted the betterment of the LF-KHOA technique over the current state of art techniques.
Unmanned aerial vehicles, Energy efficiency, Clustering, Levy flight, Metaheuristics
[1] Khelifi, F., Bradai, A., Singh, K. and Atri, M., 2018. Localization and energy-efficient data routing for unmanned aerial vehicles: Fuzzy-logic-based approach. IEEE Communications Magazine, 56(4), pp.129-133.
[2] Wu, Q., Sun, P. and Boukerche, A., 2019. Unmanned aerial vehicle-assisted energy-efficient data collection scheme for sustainable wireless sensor networks. Computer Networks, 165, p.106927.
[3] Smruthi, S., Krishna, R.S. and Panda, M., 2019, April. Low energy sensor data collection using unmanned aerial vehicles. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 740-745). IEEE.
[4] Bacanli, S.S. and Turgut, D., 2020. Energy-efficient unmanned aerial vehicle scanning approach with node clustering in opportunistic networks. Computer Communications, 161, pp.76-85.
[5] Saif, A., Dimyati, K.B., Noordin, K.A.B., Shah, N.S.M., Alsamhi, S.H., Abdullah, Q. and Farah, N., 2021. Distributed Clustering for User Devices Under Unmanned Aerial Vehicle Coverage Area during Disaster Recovery. arXiv preprint arXiv:2103.07931.
[6] Khan, A., Aftab, F. and Zhang, Z., 2019. BICSF: Bio-inspired clustering scheme for FANETs. IEEE Access, 7, pp.31446-31456.
[7] Soorki, M.N., Mozaffari, M., Saad, W., Manshaei, M.H. and Saidi, H., 2016, December. Resource allocation for machine-to-machine communications with unmanned aerial vehicles. In 2016 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE.
[8] Yang, J., Wang, X., Li, Z., Yang, P., Luo, X., Zhang, K., Zhang, S. and Chen, L., 2016, June. Path planning of unmanned aerial vehicles for farmland information monitoring based on WSN. In 2016 12th World Congress on Intelligent Control and Automation (WCICA) (pp. 2834-2838). IEEE.
[9] Li, L., Wu, J., Xu, Y., Che, J. and Liang, J., 2017, June. Energy-controlled optimization algorithm for rechargeable unmanned aerial vehicle network. In 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1337-1342). IEEE.
[10] Keshavarz, M., Shamsoshoara, A., Afghah, F. and Ashdown, J., 2020, July. A real-time framework for trust monitoring in a network of unmanned aerial vehicles. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 677-682). IEEE.
[11] Salam, A., Javaid, Q. and Ahmad, M., 2021. Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture. International Journal of Distributed Sensor Networks, 17(7), p.15501477211034071.
[12] Lin, C., Han, G., Qi, X., Du, J., Xu, T. and Martínez-García, M., 2020. Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach. IEEE Transactions on Industrial Informatics, 17(6), pp.4411-4420.
[13] Valentino, R., Jung, W.S. and Ko, Y.B., 2018. A design and simulation of the opportunistic computation offloading with learning-based prediction for unmanned aerial vehicle (uav) clustering networks. Sensors, 18(11), p.3751.
[14] Khan, A., Khan, S., Fazal, A.S., Zhang, Z. and Abuassba, A.O., 2021. Intelligent cluster routing scheme for flying ad hoc networks. Science China Information Sciences, 64(8), pp.1-14.
[15] Pustokhina, I.V., Pustokhin, D.A., Kumar Pareek, P., Gupta, D., Khanna, A. and Shankar, K., 2021. Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model. International Journal of Communication Systems, 34(8), p.e4786.
[16] Arafat, M.Y. and Moh, S., 2019. Localization and clustering based on swarm intelligence in UAV networks for emergency communications. IEEE Internet of Things Journal, 6(5), pp.8958-8976.
[17] Na, Z., Liu, Y., Shi, J., Liu, C. and Gao, Z., 2020. UAV-supported clustered NOMA for 6G-enabled Internet of Things: Trajectory planning and resource allocation. IEEE Internet of Things Journal.
[18] Pustokhina, I.V., Pustokhin, D.A., Lydia, E.L., Elhoseny, M. and Shankar, K., 2021. Energy Efficient Neuro-Fuzzy Cluster based Topology Construction with Metaheuristic Route Planning Algorithm for Unmanned Aerial Vehicles. Computer Networks, p.108214.
[19] Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S. and Wang, H., 2014. Chaotic krill herd algorithm. Information Sciences, 274, pp.17-34.
[20] Kamaruzaman, A.F., Zain, A.M., Yusuf, S.M. and Udin, A., 2013. Levy flight algorithm for optimization problems-a literature review. In Applied Mechanics and Materials (Vol. 421, pp. 496-501). Trans Tech Publications Ltd.
