Abstract
Vehicular ad hoc network (VANET) can manage live traffic and send emergency messages to the base station in any smart city and is emerging as a connectivity network. In VANET, every vehicle acts as a sensor node, which collects the surrounding information and sends information to the base station. VANET network is created when communication between cars with wireless transceiver is needed. Despite the fact that VANET and mobile ad hoc network (MANET) have some similarities; the dynamic nature of VANET has posed a challenge on routing protocols designing; VANET is composed of models based communication among vehicles and vehicle with a high mobility feature. Presently the artificial neural networks is often used in several fields. Neural networks are usually trained by conventional backpropagation learning algorithm that minimizes the training data mean square error (MSE). The goal of this paper is to investigate VANET performance in terms of packet loss rate and throughput using robust neural networks learning based on the robust M-Estimators performance function instead of the traditional MSE performance function. Robust M-estimators performance functions outperform the traditional MSE performance function in terms of RMSE and training speed as simulation results show.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chadha, D.: Reena, vehicular ad hoc network (VANETs): a review. Int. J. Innov. Res. Comput. Commun. Eng. 3(3), 2339–2346 (2015)
Zahra, M.M., Essai, M.H., Ellah, A.R.A.: Performance functions alternatives of MSE for neural networks learning. Int. J. Eng. Res. Technol. (IJERT) 3(1), 967–970 (2014)
Zahra, M.M., Essai, M.H., Ellah, A.R.A.: Robust neural network classifier. Int. J. Eng. Dev. Res. (IJEDR) 1(3), 326–331 (2013). ISSN 2321-9939
Huber, P.J.: Robust Statistics. Wiley, New York (1981)
Li, C., Wang, M., Zhu, L.: Connectivity-sensed routing protocol for vehicular ad hoc networks: analysis and design. Int. J. Distrib. Sens. Netw. 11(8), 1–11 (2015). https://doi.org/10.1155/2015/649037
Rehman, M.U., Ahmed, S., Khan, S.U., Begum, S., Ahmed, S.H.: Performance and execution evaluation of VANETs routing protocols in different scenarios. EAI Endorsed Trans. Energy Web Inform. Technol. 5(17), 1–5 (2018)
Hassan, A., Ahmed, M.H., Rahman, M.A.: Performance evaluation for multicast transmissions in VANET. In: 2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE), Niagara Falls, pp. 001105–001108 (2011)
Ellah, A.R.A., Essai, M.H., Yahya, A.: Comparison of different backpropagation training algorithms using robust M-estimators performance functions. In: the IEEE 2015 Tenth International Conference on Computer Engineering and Systems (ICCES), 23–24 December, Cairo, Egypt, pp. 384–388 (2015)
El-Melegy, M.T., Essai, M.H., Ali, A.A.: Robust training of artificial feedforward neural networks. In: Hassanien, A.E., Abraham, A., Vasilakos, A.V., Pedrycz, W. (eds.) Foundations of Computational, Intelligence, Volume 1, vol. 201, pp. 217–242. Heidelberg, Springer (2009). https://doi.org/10.1007/978-3-642-01082-8_9
Essai, M.H., Ellah, A.R.A.: M-estimators based activation functions for robust neural network learning. In: the IEEE 10th International Computer Engineering Conference (ICENCO 2014), 29–30 December, Cairo, Egypt, pp. 76–81 (2014)
Ellah, A.R.A., Essai, M.H., Yahya, A.: Robust backpropagation learning algorithm study for feed forward neural networks. Thesis, Al-Azhar University, Faculty of Engineering (2016)
Vegni, A.M., Biagi, M., Cusani, R.: Smart vehicles, technologies and main applications in vehicular ad hoc networks. In: Vehicular Technologies - Deployment and Applications. INTECH Open Access Publisher (2013). https://doi.org/10.5772/55492
Bodhy Krishna, S.: Study of ad-hoc networks with reference to MANET, VANET. FANET. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(7), 390–394 (2017). ISSN 2277-128X
Saggi, M.K., Sandhu, R.K.: A survey of vehicular ad hoc network on attacks and Security Threats in VANETs. In: International Conference on Research and Innovations in Engineering and Technology (ICRIET 2014), 19–20 December 2014
Bronsted, J., Kristensen, L.: Specification and performance evaluation of two zone dissemination protocols for vehicular ad-hoc networks. In: Proceedings of the 39th Annual Simulation Symposium (ANSS 2006). IEEE (2006)
Boban, M., Vinhoza, T.T.V.: Modeling and simulation of vehicular networks: towards realistic and efficient models. In: Xin W. (eds.) Mobile Ad-Hoc Networks: Applications, pp. 41–66. INTECH Open Access Publisher (2011) https://doi.org/10.5772/12846
Nam, J.: Implementation of VANET simulator using Matlab. J. Korea Inst. Inform. Commun. Eng. 20(6), 1171–1176 (2016)
Zhang, Z.: Parameter estimation techniques: a tutorial with application to conic fitting, October 1995
Acknowledgments
The publication has been prepared with the support of the “RUDN University Program 5-100”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdellah, A.R., Muthanna, A., Koucheryavy, A. (2019). Robust Estimation of VANET Performance-Based Robust Neural Networks Learning. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2019 2019. Lecture Notes in Computer Science(), vol 11660. Springer, Cham. https://doi.org/10.1007/978-3-030-30859-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-30859-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30858-2
Online ISBN: 978-3-030-30859-9
eBook Packages: Computer ScienceComputer Science (R0)