An Evaluation of Autoencoder Neural Network Role in Iot Edge Computing

Abstract

With rapid increase in numbers of connected Internet of Things (IoT) devices, huge amount of data is generated and sent to Cloud Computing nodes to be stored and analysed. Cloud computing is an effective paradigm for storage and data analysis since IoT devices are restricted machines in terms of energy, computation power and storage. Despite the advantages of cloud computing, it causes network congestion and latency due to generally located at long distances. Besides, security and privacy issues are also drawbacks of the cloud. Edge Computing is a promising system to eliminate the flaws of cloud computing by getting computational power closer to data sources. Edge Computing has more computation power than IoTD but lower than cloud computing. Although the deficiencies of cloud computing decrease with edge computing, they are not completely eliminated because computation intensive tasks still should be sent from edge to cloud resources. Since Autoencoder is an unsupervised neural network technique that learns to efficiently encode/compress input data and learns to efficiently decode it as closer to the original input, it is an ideal candidate for reducing data traffic and latency in edge computing and cloud computing. Instead of sending all data to the cloud, the data of bottleneck hidden layers in which input data is encoded are sent from edge to cloud. The compressed data is decoded on the cloud to reconstruct the original input to be analysed and learnt. In this paper, we investigate the studies using AE in edge computing and their performance implications with respect to network traffic and delay. The performance results of the proposals that have used autoencoder between edge and cloud layer are evaluated in terms of eliminating big data, network traffic and accuracy.