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.
Highlights
The world’s attention today is focused on enabling artificial intelligence in a range of areas
At the MAC layer, we find the maximum number of transmissions (MT), the retry delay time for new retransmission (RD), and the maximum queue size (QS) of the queue on top of the MAC layer used to buffer packets when they are waiting for re-transmission
The results showed the impact of AT, IAT, RD, PS, distance between the nodes (DT), Transmission power level (TP), QS, and MT on minimizing the prediction error
Summary
The world’s attention today is focused on enabling artificial intelligence in a range of areas. Wireless sensor networks show their capabilities in achieving this aim in many fields such as healthcare centers, smart buildings, agriculture environments, and industrial factories [1] Those applications often require a large and dynamic distribution of nodes [2]. They are a subject of a set of constraints such as low packet loss, high network lifetime, high throughput, high energy efficiency, and low delay. Most traditional ML techniques applied in IoT (internet of things) systems usually utilize shallow architectures, which are characterized by limited modeling and representational power. For this reason, a deep approach is highly desirable for deep pattern extraction from the invaluable raw data generated in various IoT applications. We will survey the cross-layer approaches with the capability of deep learning in achieving accurate predictions of performance trade-offs between different layers
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