Prediction of QoS Data for Various Sensors Using AI Algorithms
Keywords:
Feed Forward Neural Network, Gaussian Process, Sensor Data, Error Metrics, QoS PredictionAbstract
Wireless environment monitoring is performed by spectrum sensors and network sniffing in wireless networks. By this, we will get a set of spatially distributed measurements. In this work, we show how machine learning algorithms can be used to generate probabilistic forecasts rather than point estimations using data from numerous sensing devices. In this respect, the QoS data from the pressure sensor is taken and based on that, univariate models were constructed. Feed Forward Neural Network (FFNN) and Gaussian Process (GP) artificial intelligence algorithms were used for the construction of the models. The algorithms are applied to model measurements data collected from a heterogeneous wireless testbed environment. To evaluate the constructed model, throughput has been predicted and examined for the Gas, Humidity, Pressure, and Temperature sensors. The results of various error metrics are used to assess the performance of models like NRMSE, MASE, sMAPE, WQL, and MAE, and based on the error metrics values, it is observed that both the FFNN and GP algorithms provide good results for the different sensors QoS values prediction, but the GP algorithms performs slightly better than FFNN. The results indicate that GP machine learning approach provides accurate results as compared to the FFNN approach for QoS values prediction in the wireless environment. The results will lead to secure message delivery in the network.
Downloads
References
A. A. A. Ari, B. O. Yenke, N. Labraoui, I. Damakoa and A. Gueroui, “A power-efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence-based approach”, Journal of Network and Computer Applications, Vol. 69, pp. 77–97, 2016.
A. A. A. Ari, A. Gueroui, N. Labraoui and B. O. Yenke, “Concepts and Evolution of research in the field of wireless sensor networks”, Journal of Computer Networks and Communications, Vol. 7, No. 1, pp. 81–98, 2015.
Z. Al Aghbari, A.M. Khedr, W. Osamy, I. Arif and D. P. Agrawal, “Routing in wireless sensor networks using optimization techniques: a survey”, Wireless Personal Communications, vol. 111, pp. 2407–2434, 2020.
L. Cao, Y. Cai and Y. Yue, “Swarm intelligence-based performance optimization for mobile wireless sensor networks: survey, challenges, and future directions”, IEEE Access, Vol. 7, pp. 161524–161553, 2019.
Bulla, P. . “Traffic Sign Detection and Recognition Based on Convolutional Neural Network”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 43-53, doi:10.17762/ijritcc.v10i4.5533.
R.J. Chadalavada, S. Raghavendra and V. Rekha, “Electricity requirement prediction using time series and Facebook’s PROPHET”, Indian Journal of Science and Technology, Vol. 13, No. 47, pp. 4631-4645, 2020.
D. Karaboga and E. Kaya, “An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training”, Applied Soft Computing, Vol. 49, pp. 423–436, 2016.
E. Zunic, K. Korjenic, K. Hodzic and D. Donko, "application of facebook's prophet algorithm for successful sales forecasting based on real-world data”, International Journal of Computer Science & Information Technology, Vol. 12, pp. 2, 2020.
Y. Jin, K. Wang and Y. Zhang, “Neighborhood-aware web service quality prediction using deep learning”, J Wireless Com Network 2019, Vol. 222, 2019.
D. Kothona, “A novel hybrid ensemble LSTM-FFNN forecasting model for very short-term and short-term PV generation forecasting”, IET Renew. Power Gener., pp. 1– 16, 2021.
Y. Li, Z. Ma and Z. Pan, “Prophet model and Gaussian process regression-based user traffic prediction in wireless networks”, Sci. China Inf. Sci. Vol. 63, pp. 142301, 2020.
A. Ly and Y.D. Yao, “A review of deep learning in 5g research: channel coding, massive MIMO, multiple access, resource allocation, and network security”, in IEEE Open Journal of the Communications Society, Vol. 2, pp. 396–408, 2021.
M. Abbasi, A. Shahraki and A. Taherkordi, “Deep learning for network traffic monitoring and analysis (NTMA)”, A Survey, Computer Communications, Vol. 170, pp. 19-41, 2021.
M. Azharuddin and P. K. Jana, “Particle swarm optimization for maximizing lifetime of wireless sensor networks”, Computers & Electrical Engineering, Vol. 51, pp. 26–42, 2016.
P. Nayak, G.K. Swetha, S. Gupta and K. Madhavi, “Routing in wireless sensor networks using machine learning techniques”, Challenges and opportunities, Measurement, Vol. 178, pp. 108974, 2021.
J. Prajapati and S.C. Jain, “Machine learning techniques and challenges in wireless sensor networks”, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 233-238, 2018.
I.U. Rehman, M.M. Nasralla and N.Y. Philip, “Multilayer perceptron neural network-based QoS-aware, content-aware and device-aware QoE prediction model: a proposed prediction model for medical ultrasound streaming over small cell networks”, Electronics. Vol. 8, No. 2, pp. 194, 2019.
R. S. Pontoh, S. Zahroh, H.R. Nurahman, R.I. Aprillion, A. Ramdani and D.I. Akmal, “Applied of feed-forward neural network and facebook prophet model for train passengers forecasting”, J. Phys.: Conf. Ser. pp. 1776 012057, 2021.
J. Shen, D. Valagolam and S. McCalla, “Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea”, Peer J, Vol. 8, pp.e9961, 2020.
D. Wang, D. Tan and L. Liu, “Particle swarm optimization algorithm: an overview”, Soft Computing, Vol. 22, pp. 387–408, 2018.
D.N. Wategaonkar, S.V. Nagaraj and T.R. Reshmi, “Multi-hop energy-efficient reliable cluster-based sectoring scheme using markov chain model to improve QoS parameters in a WSN”, Wireless Personal Communications, Vol. 119, pp. 393-421, 2021.
H. Weytjens, E. Lohmann, M. Kleinsteuber, “Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet”, Electronic Commerce Research, Vol. 21, pp. 371-391, 2021.
Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 05–08. https://doi.org/10.17762/ijfrcsce.v8i1.2065
G. White, A. Palade and S. Clarke, “Forecasting QoS attributes using LSTM Networks”, International Joint Conference on Neural Networks (IJCNN), Vol. 1-8, 2018.
M. Chen, Y. Cheng, R. Chen, “A Novel Indoor Positioning Framework”, CMES-Computer Modeling in Engineering & Sciences, Vol. 130, No.3, pp.1459–1477, 2022.
R. Rajender, C. S. S. Anupama, G. Jose Moses, E. Laxmi Lydia, S. Kadry et al., "Artificial intelligence-enabled cooperative cluster-based data collection for unmanned aerial vehicles," Computers, Materials & Continua, Vol. 73, No.2, pp. 3351–3365, 2022.
Q. Yang, W. N. Chen, Z. Yu, T. Gu, , Y. Li, H. Zhang and J. Zhang, "Adaptive multimodal continuous ant colony optimization," in IEEE Transactions on Evolutionary Computation, Vol. 21, No. 2, pp. 191-205, 2017.
P. Garg, A. Dixit and P. Sethi, "Ml-fresh: novel routing protocol in opportunistic networks using machine learning," Computer Systems Science and Engineering, Vol. 40, No.2, pp. 703–717, 2022.
A. Krishnakumar, T. Senthilkumaran, S. Vijayanand, R. Mukesh, P. Kamali,"Deep learning and optimisation for quality-of-service modelling", Journal of King Saud University - Computer and Information Sciences, 2022, https://doi.org/10.1016/j.jksuci.2022.01.016.
P.G. Asteris, P.C. Roussis and M.G. Douvika, “Feed-forward neural network prediction of the mechanical properties of sandcrete materials”, Sensors. Vol. 17, No. 6, pp. 1344, 2017.
M.H. Samed Inyurt, A. Kashani, Sekertekin, “Ionospheric tec forecasting using gaussian process regression and multiple linear regression in turkey”, Astrophysics and Space Science, Vol. 365, No. 99, 2020.
M. Jayalakshmi and S. Nagaraja Rao, "Discrete wavelet transmission and modified pso with aco based feed forward neural network model for brain tumour detection," Computers, Materials & Continua, Vol. 65, No.2, pp. 1081–1096, 2020.
Modiya, P., & Vahora, S. (2022). Brain Tumor Detection Using Transfer Learning with Dimensionality Reduction Method. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 201–206. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1310
N. Syazreen Ahmad, J. Hui Teo and P. Goh, "Gaussian process for a single-channel eeg decoder with inconspicuous stimuli and eyeblinks," Computers, Materials & Continua, Vol. 73, No.1, pp. 611–628, 2022.
Sally Fouad Shady. (2021). Approaches to Teaching a Biomaterials Laboratory Course Online. Journal of Online Engineering Education, 12(1), 01–05. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/43
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
