Event-Driven Deep Learning for Edge Intelligence (EDL-EI).

Sayed Khushal Shah,Yugyung Lee,Zeenat Tariq,Jeehwan Lee

doi:10.3390/s21186023

Abstract

Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.

Highlights

For a healthy life, sophisticated techniques are needed to observe and identify airquality levels
We considered traffic as an event and performed an analysis of two cities of China, i.e., Beijing and Shanghai. We considered these two main cities because, during our search, we found that Beijing and Shanghai were the two main polluted cities among all eight cities that we considered for COVID-19 analysis
We considered the pre-pandemic and pandemic periods of 2019 and 2020 for specific cities, i.e., Seoul, Daegu, Incheon, and Busan from South Korea, and Wuhan, Beijing, Shanghai, and Hefei from China

Summary

Introduction

For a healthy life, sophisticated techniques are needed to observe and identify airquality levels. The Internet of Things (IoT) and artificial intelligence play a crucial part in enhancing the quality of care and sustaining urban growth. Social identity is one of the critical determinants for user satisfaction and ecological convenience when taking into consideration various environmental criteria such as greenhouse gases, air temperature, relative humidity, wind speed, and noise levels [1]. IoT and mobile computing have shown significant advances in the deployment of sensors and IoT devices and real-time data collection from diverse settings, such as environmental sensing and the monitoring of human activities via video/audio surveillance [2,3]. Artificial intelligence with deep learning has achieved remarkable success in various applications, including computer vision, Natural Language

Results

Discussion

Conclusion