Optimizing the Performance of Wireless Sensor Network Based on Software Defined Network and Gaussian Filter

Khearallah, Jumana S.; Rijab, Khalida Sh.

doi:10.30684/etj.v40i2.2221

	Optimizing the Performance of Wireless Sensor Network Based on Software Defined Network and Gaussian Filter
Engineering and Technology Journal
Article 9, Volume 40, Issue 2, 2022, Pages 379-385 PDF (823.51 K)
Document Type: Research Paper
DOI: 10.30684/etj.v40i2.2221
Authors
Jumana S. Khearallah^* ¹; Khalida Sh. Rijab²
¹Electrical Engineering Dept., University of Technology-Iraq, Alsina’a Street, 10066 Baghdad, Iraq
²Electrical Engineering Dept., University of Technology-Iraq, Alsina’a Street, 10066 Baghdad, Iraq.
Abstract
This paper presents software defining network SDN control in wireless sensor networks (WSN) to estimate packet flow, which relies on a Gaussian filter to filter the transmitted signal. The practical aim of this method is to predict the next step of packet flow earlier, which helps reduce congestion if it occurs. The proposed model (SDN-WSN with Gaussian filter) is applied to enhance signal transmission, reduce data error, reduce congestion network and reduce data overflow data. The methodology of the proposed work can be explained as follows: first: distributing nodes randomly; second: Applying the K-mean cluster to choose to select the optimum position of the head cluster node; third: connecting the network using LEACH protocol. Moreover. In this work, SDN with a Gaussian filter is proposed to control the network and minimize data error. It is possible to achieve that by adding buffer memory for each node to store data. The data transmission process is controlled by SDN, and a Gaussian filter is applied before transmitting data to minimize error data. The proposed method's simulation results proved its effectiveness in prolonging the network lifetime by nearly more than 30% rounds than out-of-date WSN, reducing the average density of memory to 20% than out-of-date WSN, and increasing the average capacitance of memory to 20% than out-of-date WSN
Highlights
The use of SDN in WSN has become popular due to its outstanding dynamic performance. The Gaussian filter is widely used to smooth, blur and remove signal noise. QoS in SDN-WSN has proven to be very effective in extending the network's life. The process of transmitting and receiving data was organized.
Keywords
Wireless Sensor Network; Software define network; Gaussian filter

References
[1] M. A. Khalil, Wireless Sensor Networks Optimisation Using Software Defined Networking Concept in Cloud Based End-to-end Application. University of Leicester, 2020. [2] M. A. Hassan, Q.-T. Vien, and M. Aiash, Software defined networking for wireless sensor networks: a survey, Adv. Wirel. Commun. Networks, 3 (2017) 10–22. [3] R. Abrishambaf and M. Bal, A study on the optimal base station placement for connected smart factories, in IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, 1 (2019) 5527–5531. [4] A. Kumar, H. Shwe, K. Wong, and P. Chong, Location-based routing protocols for wireless sensor networks: a survey. Wireless Sens. Netw. 9 (2017) 25–72. [5] Z. Niu, X. S. Shen, Q. Zhang, and Y. Tang, Space-air-ground integrated vehicular network for connected and automated vehicles: Challenges and solutions, Intell. Converg. Networks, 1 (2020) 142–169. [6] S. H. Mohamed, T. E. H. El-Gorashi, and J. M. H. Elmirghani, A survey of big data machine learning applications optimization in cloud data centers and networks, arXiv Prepr. arXiv1910.00731 (2019). [7] A. Stamou, N. Dimitriou, K. Kontovasilis, and S. Papavassiliou, Autonomic handover management for heterogeneous networks in a future internet context: A survey, IEEE Commun. Surv. Tutorials, 21 (2019) 3274–3297. [8] A. Padhy, S. Joshi, S. Bitragunta, V. Chamola, and B. Sikdar, A survey of energy and spectrum harvesting technologies and protocols for next generation wireless networks, IEEE Access, (2020). [9] P. Tedeschi, S. Sciancalepore, and R. Di Pietro, Security in energy harvesting networks: a survey of current solutions and research challenges, IEEE Commun. Surv. Tutorials, 22 (2020) 2658–2693. [10] A. Dvir, Y. Haddad, and A. Zilberman, The controller placement problem for wireless SDN, Wirel. Networks, 25 (2019) 4963–4978. [11] Z. Fan, J. Yao, X. Yang, Z. Wang, and X. Wan, A multi-controller placement strategy based on delay and reliability optimization in SDN, in 2019 28th Wireless and Optical Communications Conference (WOCC), (2019) 1–5. [12] B. Han, X. Yang, and X. Wang, Dynamic controller-switch mapping assignment with genetic algorithm for multi-controller SDN, in 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), (2019) 980–986. [13] S. Torkamani-Azar and M. Jahanshahi, A new GSO based method for SDN controller placement, Comput. Commun., 163 (2020) 91–108. [14] S. Tahmasebi, M. Safi, S. Zolfi, M. R. Maghsoudi, H. R. Faragardi, and H. Fotouhi, Cuckoo-PC: an evolutionary synchronization-aware placement of SDN controllers for optimizing the network performance in WSNs, Sensors, 20 (2020) 3231. [15] A. Sharma, P. K. Singh, A. Sharma, and R. Kumar, An efficient architecture for the accurate detection and monitoring of an event through the sky, Comput. Commun., 148 (2019)115–128. [16] M. F. Mosleh and D. S. H. Talib, Hardware Implementation of Wireless Sensor Network Using Arduino and Zigbee Protocol, Eng. Technol. J., 34 (2016) 816-829. [17] D. Ramotsoela, A. Abu-Mahfouz, and G. Hancke, A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study, Sensors, 18 (2018) 2491. [18] R. Priyadarshi, B. Gupta, and A. Anurag, Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues, J. Supercomput., 76 (2020) 7333–7373. [19] K. S. Rijab and S. M. Sadiq, Implementing a reconfigurable Internet of Things Nodes using non-IP network based on Wireless Sensor Network, Diyala J. Eng. Sci., 12 (2019) 60–66. [20] B. N. Yuvaraju and M. Narender, To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN), in Computer Science On-line Conference, (2020) 73–93. [21] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, Deep learning for IoT big data and streaming analytics: A survey, IEEE Commun. Surv. Tutorials, 20 (2018) 2923–2960. [22] H. T. Truong, Software-Defined Network Application for Inter-domain Routing in Transit ISPs, (2020). [23] G. Zhao, X. Wang, Y. Kong, and Y. Cheng, Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System, Remote Sens., 13 (2021) 583. [24] E. I. Abbas, K. S. Rijab, and A. F. Ahmed, Optimal Wavelet Filter for De-noising Surface Electromyographic Signal Captured From Biceps Brachii Muscle, Eng. Technol. J., 33 (2015) 198-207. [25] H. Gupta, H. Chauhan, A. Bijalwan, and K. Joshi, International Conference on Advances in Engineering Science Management & Technology (ICAESMT) - 2019, Uttaranchal University, Dehradun, India, A Review on Image Denoising, (2019). [26] R. Zhang et al., Multi-color space learning for image segmentation based on a support vector machine, OSA Contin., 2 (2019) 3050–3065. M. C. Eze, An Improved Gaussian Filter Technique for Biomedical Image Processing: an Early Lung Cancer Detection Technique, M.Sc. Thesis, University of Nigeria, Nsukka, Nigeria, (2017).
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