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Research Proposals or Synopsis | Computer Science & Engineering | India | Volume 11 Issue 9, September 2022 | Rating: 4.6 / 10
An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing Using Deep Learning
Bharathi K.
Abstract: Large numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating large scale of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power, and thus, is not well-suited for ML tasks. Consequently, proposed work aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. In order to process data close to the source, sensors are grouped according to locations, and feature learning is performed on the close by edge node. For comparison reasons, similarity-based processing is also considered. Feature learning is carried out with deep learning-the encoder part of the trained Auto-encoder is placed on the edge and the decoder part is placed on the cloud. The evaluation was performed on the task of human activity recognition from sensor data. When sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy.
Keywords: Auto encoders (AEs), data reduction, deep learning (DL), edge computing (EC), human activity recognition (HAR), Internet of Things (IoT)
Edition: Volume 11 Issue 9, September 2022,
Pages: 837 - 842