Sameer Alani, Bassam Hasan


The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance in a negative manner and trouble its behavior. ANN-based- energy consumption shows better performance regarding mean square error but takes a higher number of epochs which are 5 for ANN based-energy consumption and 2 for ANN-based- PDR. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.


WSN, Qos, SDN ,neural network


M. Carlos-Mancilla, E. López-Mellado, and M. Siller, ‘Wireless sensor networks formation: Approaches and techniques’, Journal of Sensors, vol. 2016. 2016.

S. Alani, Z. Zakaria, and H. Lago, ‘A new energy consumption technique for mobile Ad-Hoc networks’, Int. J. Electr. Comput. Eng., vol. 9, no. 5, pp. 4147–4153, 2019.

J. Alamri, A. S. Al-johani, and K. I. Ata, ‘Performance Evaluation of Two Mobile Ad-hoc Network Routing Protocols : Ad- hoc On-Demand Distance Vector , Dynamic Source Routing’, Int. J. Adv. Sci. Technol., vol. 29, no. 5, pp. 9915–9920, 2020.

S. A. Rashid, L. Audah, M. M. Hamdi, and S. Alani, ‘Prediction based efficient multi-hop clustering approach with adaptive relay node selection for VANET’, J. Commun., vol. 15, no. 4, pp. 332–344, 2020.

S. Alani, Z. Zakaria, and M. M. Hamdi, ‘A study review on mobile ad-hoc network: Characteristics, applications, challenges and routing protocols classification’, Int. J. Adv. Sci. Technol., vol. 28, no. 1, pp. 394–405, 2019.

A. Djimli, S. Merniz, and S. Harous, ‘Energy-efficient MAC protocols for wireless sensor networks: a survey’, TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 5, p. 2301, 2019.

A. M. Fahad, A. A. Ahmed, A. H. Alghushami, and S. Alani, ‘Detection of Black Hole Attacks in Mobile Ad Hoc Networks via HSA-CBDS Method’, in Springer Nature Switzerland, vol. 866, Springer International Publishing, 2019, pp. 46–55.

S. Su and S. Wang, ‘A Simple Monitoring Network System of Wireless Sensor Network’, Bull. Electr. Eng. Informatics, vol. 1, no. 4, pp. 251–254, 2012.

M. A. Saad, M. H. Ali, and S. Alani, ‘Performance evaluation and improvement of an ad hoc wireless network’, Int. J. Adv. Sci. Technol., vol. 29, no. 3, pp. 4128–4137, 2020.

S. A. Hussein and D. P. Dahnil, ‘A New Hybrid Technique to Improve the Path Selection in Reducing Energy Consumption in Mobile AD-HOC Networks’, Int. J. Appl. Eng. Res., vol. 12, no. 3, pp. 277–282, 2017.

A. M. Fahad, S. Alani, S. N. Mahmood, and N. M. Fahad, ‘Ns2 based performance comparison study between dsr and aodv protocols’, Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 1.4 S1, pp. 379–393, 2019.

M. P. Beham and S. M. M. Roomi, ‘A review of face recognition methods’, Int. J. Pattern Recognit. Artif. Intell., vol. 27, no. 4, 2013.

O. S. Al-heety, Z. Zakaria, M. Ismail, M. M. Shakir, S. Alani, and H. Alsariera, ‘A comprehensive survey : Benefits , Services , Recent works , Challenges , Security and Use cases for SDN-VANET’, IEEE Access, 2020.

Y. C. Wong, S. H. Tan, R. Singh, S. Singh, and H. Zhang, ‘Low power wake-up receiver based on ultrasound communication for wireless sensor network’, Bull. Electr. Eng. Informatics, vol. 9, no. 1, 2020.

M. A. Saad, S. T. Mustafa, M. H. Ali, M. M. Hashim, M. Bin Ismail, and A. H. Ali, ‘Spectrum sensing and energy detection in cognitive networks’, Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 1, pp. 465–472, 2019.

M. Pradhan, C. K. Nayak, and S. K. Pradhan, ‘Intrusion detection system (IDS) and their types’, Netw. Secur. Attacks Countermeas., pp. 245–299, 2016.

S. Laqtib, K. El Yassini, and M. L. Hasnaoui, ‘A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET’, Int. J. Electr. Comput. Eng., vol. 10, no. 3, pp. 2701–2709, 2020.

R. Jyothi and N. G. Cholli, ‘An efficient approach for secured communication in wireless sensor networks’, Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1641–1647, 2020.

S. Bitam, S. Zeadally, and A. Mellouk, ‘Bio-inspired cybersecurity for wireless sensor networks’, IEEE Commun. Mag., vol. 54, no. 6, pp. 68–74, 2016.

A. Mahboub, E. M. En-naimi, M. Arioua, H. Barkouk, Y. El Assari, and E. Oualkadi, ‘An energy-efficient clustering protocol using fuzzy logic and network segmentation for heterogeneous WSN’, Int. J. Electr. Comput. Eng., vol. 9, no. 5, pp. 4192–4203, 2019.

M. R. Hossain, M. S. Kaiser, F. I. Ali, and M. M. A. Rizvi, ‘Network flow optimization by Genetic Algorithm and load flow analysis by Newton Raphson method in power system’, 2nd Int. Conf. Electr. Eng. Inf. Commun. Technol. iCEEiCT 2015, no. May, 2015.

N. A. Alrajeh, S. Khan, J. Lloret, and J. Loo, ‘Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting’, Ad-Hoc Sens. Wirel. Networks, vol. 22, no. 1–2, pp. 109–133, 2014.

O. Avci, O. Abdeljaber, S. Kiranyaz, and D. Inman, ‘Convolutional neural networks for real-time and wireless damage detection’, Conf. Proc. Soc. Exp. Mech. Ser., pp. 129–136, 2020.

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