Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning
ABSTARCT :
Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion 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, this paper 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. The results show that when sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy.
EXISTING SYSTEM :
Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion 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.
DISADVANTAGE :
Presented a survey on mobile edge networks focusing on computing-related issues, edge offloading, and communication techniques for edge-based computing.
The use cases highlighted in their study include IoT, connected vehicles, content delivery, and big data analysis.
PROPOSED SYSTEM :
This paper 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. The results show that when sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy.
ADVANTAGE :
? Edge computing provides advantages of reduced traffic, computing resources available on the edge are not comparable to those present in the cloud.
? Computationally intensive tasks, such as ML, are not well suited for edge devices. Nevertheless, the edge can supplement the cloud computing and perform part of the computation, consequently reducing network traffic and latencies.
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