Leveraging the Power of Machine Learning for Performance Evaluation Prediction in Wireless Sensor Networks

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.

I. Introduction

Wireless sensor networks (WSNs) [1] consist of many sensor nodes that communicate with each other to perform a collective task such as sensing an environment. The main duty of these nodes is sensing and communicating the gathered information from the ambient environment to the Base Station (BS) or sink. Meanwhile, a sensor node is characterised by a limited computational and communication power. These constraints make the designing of an effective medium access control (MAC) protocol in WSNs a challenging task in aim to determine the channel access control capabilities and the energy consumption properties of WSNs. The MAC protocol of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) for carrier transmission in WSNs avoids collisions by transmitting only when being sure that channel is idle [2].