@proceedings {6710,
	title = {Leveraging the Power of Machine Learning for Performance Evaluation Prediction in Wireless Sensor Networks},
	journal = {International Conference on Information Technology (ICIT)},
	year = {2021},
	address = {Amman, Jordan},
	abstract = {
	Formal methods are widely exploited in the performance evaluation of Wireless Sensor Networks (WSNs) protocols and algorithms. These methods help researchers to model and to analyse mathematically such protocols. Numerical results obtained by analysis and performance evaluation can be employed to prove the correctness and consistency of the designed models. However, these methods face a scalability problem when the number of components becomes very high, which is often the case in WSNs. To overcome this challenge, this paper proposes to use a Machine Learning (ML) solution to provide predictions when the number of nodes increases and the formal model becomes enable to make the analysis. Indeed, this work deals with the application of effective Artificial Neural Networks (ANNs) for the prediction of a set of crucial performance metrics of CSMA/CA-MAC protocol in WSNs when the number of nodes increases significantly in the network. This prediction process is based on prior results obtained by the formal model when the number of nodes was manageable by that formal model.

},
	url = {https://ieeexplore.ieee.org/abstract/document/9491722},
	author = {Zroug, Siham and Remadna, Ikram and Kahloul, Laid and Benharzallah, Saber and Terrissa, Sadek-Labib}
}
