Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

Abstract

The dynamism and successive changes in the distribution of nodes are among the important characteristics of wireless sensor networks (WSNs). To adapt with those changes, network designers frequently have to configure multiple parameters for each layer on WSN architecture (i.e physical, medium access control, network, and application layer). This tuning has an important impact on the network performances (e.g packet loss, energy efficiency, throughput, network lifetime, etc). However, finding the optimal configuration is the main challenge. Deep learning (DL) based on neural network layers can be used to extract patterns from high-dimensional data provided by sensor nodes. In this paper, we survey the most recent DL approaches which aim to predict WSN performances by finding the pattern on the network parameters (such as transmission power level, MAC protocol type, spectrum availability, congestion points, etc.). Moreover, we classify the studied articles by considering the targeted network layer or cross-layer. This paper can be considered as a starting point for researchers to review the recent DL applications on the optimization of WSNs performances based on multiple network parameters.