Volume 9 • Issue 2 • PP: 31-41 • 2022
An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm
View PDF Abstract
One of the most significant uses of the Internet of Things is military infiltration detection (IoT). Autonomous drones play a major role in IoT-based vulnerability scanning (UVs). By relocating UVs remotely, this work introduces a new algorithm called the Moth-Flame Optimization Algorithm (MFO). In particular, MFO is used to proactively manage UVs under various scenarios and under different intrusion-covering situations. According to actual studies, the suggested algorithm is both profitable and efficient.
Keywords
References
[1] Belghith, A., & Obaidat, M. S. (2016). Wireless sensor networks applications to smart homes and cities. Smart Cities and Homes, 17–40.
[2] Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620-633.
[3] Das, A., Sharma, S. C. M., & Ratha, B. K. (2019). The New Era of Smart Cities, From the Perspective of the Internet of Things. In Smart Cities Cybersecurity and Privacy (pp. 1-9). Elsevier.
[4] Hamidi, H. (2019). An approach to develop the smart health using Internet of Things and authentication based on biometric technology. Future Generation Computer Systems, 91, 434-449.
[5] Xu, X., Jin, J., Zhang, S., Zhang, L., Pu, S., & Chen, Z. (2019). Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry. Future Generation Computer Systems, 94, 381-391.
[6] Wu, Q., Shen, J., Yong, B., Wu, J., Li, F., Wang, J., & Zhou, Q. (2019). Smart fog based workflow for traffic control networks. Future Generation Computer Systems.
[7] Lu, H. P., Chen, C. S., & Yu, H. (2019). Technology roadmap for building a smart city: An exploring study on methodology. Future Generation Computer Systems.
[8] Chen, X., Fan, J., He, Q., Wang, Y., Liu, D., & Hu, S. (2019). Economical and balanced production in smart Petroleum Cyber–Physical System. Future Generation Computer Systems, 95, 364-371.
[9] Winkler, M., Tuchs, K. D., Hughes, K., & Barclay, G. (2008). Theoretical and practical aspects of military wireless sensor networks. Journal of Telecommunications and Information Technology, 37-45.
[10] Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. In Computational intelligence for multimedia big data on the cloud with engineering applications (pp. 185-231). Academic Press.
[11] Ismail, W. W., & Manaf, S. A. (2010, December). Study on coverage in wireless sensor network using grid based strategy and particle swarm optimization. In Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference on (pp. 1175-1178). IEEE.
[12] Ab Aziz, N. A. B., Mohemmed, A. W., & Alias, M. Y. (2009, March). A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In Networking, Sensing and Control, 2009. ICNSC'09. International Conference on (pp. 602-607). IEEE.
[13] Aziz, N. A. A., Mohemmed, A. W., & Zhang, M. (2010, April). Particle swarm optimization for coverage maximization and energy conservation in wireless sensor networks. In European Conference on the Applications of Evolutionary Computation (pp. 51-60). Springer, Berlin, Heidelberg.
[14] Aziz, N. A. A., Mohemmed, A. W., Alias, M. Y., Aziz, K. A., & Syahali, S. (2011, September). Coverage maximization and energy conservation for mobile wireless sensor networks: A two phase particle swarm optimization algorithm. In Bio-Inspired Computing: Theories and Applications (BICTA), 2011 Sixth International Conference on (pp. 64-69). IEEE.
[15] Xia, J. (2016, November). Coverage Optimization Strategy of Wireless Sensor Network Based on Swarm Intelligence Algorithm. In Smart City and Systems Engineering (ICSCSE), International Conference on (pp. 179-182). IEEE.
[16] Li, X. L. (2003). A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China.
[17] Sun, H., & Zhao, J. (2011). Application of particle sharing based particle swarm frog leaping hybrid optimization algorithm in wireless sensor network coverage optimization. JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE, 8(14), 3181-3188.
[18] Eusuff, M. M., & Lansey, K. E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources planning and management, 129(3), (pp. 210-225).
[19] Li, W. (2011, August). PSO based wireless sensor networks coverage optimization on DEMs. In International Conference on Intelligent Computing (pp. 371-378). Springer, Berlin, Heidelberg.
[20] Hutchinson, M., & Gallant, J. (2000). Digital elevation models. Terrain analysis: principles and applications, (pp. 29-50).
[21] Wang, X., Wang, S., & Bi, D. (2007, August). Virtual force-directed particle swarm optimization for dynamic deployment in wireless sensor networks. In International Conference on Intelligent Computing (pp. 292-303). Springer, Berlin, Heidelberg.
[22] Yildirim, K. S., Kalayci, T. E., & Ugur, A. (2008, May). Optimizing coverage in a k-covered and connected sensor network using genetic algorithms. In Proceedings of the 9th WSEAS international conference on evolutionary computing (pp. 21-26). World Scientific and Engineering Academy and Society (WSEAS).
[23] Huang, P., Lin, F., Liu, C., Gao, J., & Zhou, J. L. (2015). ACO-based sweep coverage scheme in wireless sensor networks. Journal of Sensors, 2015.
[24] Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy.
[25] Hajjej, F., Ejbali, R., & Zaied, M. (2016). An efficient deployment approach for improved coverage in wireless sensor networks based on flower pollination algorithm. NETCOM, NCS, WiMoNe, GRAPHHOC, SPM, CSEIT, 117-129.
[26] Hajjej, F., Ejbali, R., & Zaied, M.(2017) Multi Objective Nodes placement Approach in WSN based on Nature Inspired Optimisation Algorithms. In IARIA : The Second International Conference on Advances in Sensors, Actuators, Metering and Sensing ,Nice, France, 19-23 march (pp. 30 - 35)
[27] Andaliby Joghataie, A. (2018). Dynamic sensor deployment in mobile wireless sensor networks using multi-agent krill herd algorithm (Doctoral dissertation).
[28] XM1216 Small Unmanned Ground Vehicle (SUGV). (n.d.). Retrieved April 3, 2019, from https://www.army-technology.com/projects/xm1216-small-unmanned-ground-vehicle-sugv/
[29] Pullen, J. P. (2015, April 03). How Do Drones Work? Retrieved April 3, 2019, from http://time.com/3769831/this-is-how-drones-work/
[30] Huang, C. F., & Tseng, Y. C. (2005). The coverage problem in a wireless sensor network. Mobile Networks and Applications, 10(4), 519-528.
[31] Sun, Z., Li, C., Xing, X., Wang, H., Yan, B., & Li, X. (2017). k-degree coverage algorithm based on optimizaton nodes deployment in wireless sensor networks. International Journal of Distributed Sensor Networks, 13(2), 1550147717693242.
[32] Elhabyan, R., Shi, W., & St-Hilaire, M. (2019). Coverage Protocols for Wireless Sensor Networks: Review and Future Directions. arXiv preprint arXiv:1901.00511.
[33] Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249.
[34] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
Cite This Article
Choose your preferred format