730 713
Full Length Article
Journal of Cybersecurity and Information Management
Volume 9 , Issue 2, PP: 31-41 , 2022 | Cite this article as | XML | Html |PDF

Title

An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm

  Lobna Osman 1 *

1  Delta Higher Institute for Engineering & Technology, Department of Electronics and Communications Engineering, Egypt.
    (lobna.aziz@dhiet.edu.eg)


Doi   :   https://doi.org/10.54216/JCIM.090203

Received: January 25, 2022 Accepted: April 03, 2022

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 :

Internet of Things (IoT); Spatial coverage; Intrusion Detection; Moth-Flame Optimization; Metaheuristic.

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 as :
Style #
MLA Lobna Osman. "An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm." Journal of Cybersecurity and Information Management, Vol. 9, No. 2, 2022 ,PP. 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)
APA Lobna Osman. (2022). An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, 9 ( 2 ), 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)
Chicago Lobna Osman. "An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm." Journal of Journal of Cybersecurity and Information Management, 9 no. 2 (2022): 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)
Harvard Lobna Osman. (2022). An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, 9 ( 2 ), 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)
Vancouver Lobna Osman. An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, (2022); 9 ( 2 ): 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)
IEEE Lobna Osman, An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm, Journal of Journal of Cybersecurity and Information Management, Vol. 9 , No. 2 , (2022) : 31-41 (Doi   :  https://doi.org/10.54216/JCIM.090203)