Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Wardana, I Nyoman Kusuma; Gardner, Julian W.; Fahmy, Suhaib A.

doi:10.3390/s21041064

“…Four recently published models are suggested, such as AC-LSTM by [ 53 ], LSTM-FC by [ 33 ], XGBoost by [ 54 ], and CNN-LSTM by [ 55 ], which are evaluated for comparing the performance of the proposed model. Those four models were also used to forecast pollutant particles .…”

Section: Resultsmentioning

confidence: 99%

Air-pollution prediction in smart city, deep learning approach

Bekkar

¹

,

Hssina

²

,

Douzi

³

et al. 2021

View full text Add to dashboard Cite

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ 2.5 μ m ($$PM_{2.5}$$ P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$ P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method “hybrid CNN-LSTM multivariate” enables more accurate predictions than all the listed traditional models and performs better in predictive performance.

show abstract

“…In [ 24 ], Wardana et al designed a distributed short-term air-quality prediction system for hourly PM2.5 concentrations based on a hybrid deep learning model composed of 1D CNN and long short-term memory networks (CNN-LSTM). They conceived an efficient posttraining quantization method to optimize the LSTM model and make it usable by resource-constrained edge devices wherein a one-dimensional CNN is used as a feature extractor.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

¹

,

Zedadra

²

,

Kouahla

³

et al. 2023

Sensors

View full text Add to dashboard Cite

Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

show abstract

“…For instance, using quantization techniques, which are conversion techniques that convert float-point numbers to minimize precision numbers, intending to shrink the size of the DL model with minimal degradation of accuracy. Pruning techniques allow removal of redundant structures of network and parameters [15,16]. Figure 1 depicts the capability of TinyML to process the data from various IoT devices locally into tiny edge devices (e.g., a microcontroller) without the need to connect to the cloud to process the data.…”

Section: Edge Devicesmentioning

confidence: 99%

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Alajlan

¹

,

Ibrahim

²

2022

View full text Add to dashboard Cite

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions. IoT devices typically have limited resources, which restricts the execution of complex ML models such as deep learning (DL) on them. In addition, connecting IoT devices to the cloud to transfer raw data and perform processing causes delayed system responses, exposes private data and increases communication costs. Therefore, to tackle these issues, there is a new technology called Tiny Machine Learning (TinyML), that has paved the way to meet the challenges of IoT devices. This technology allows processing of the data locally on the device without the need to send it to the cloud. In addition, TinyML permits the inference of ML models, concerning DL models on the device as a Microcontroller that has limited resources. The aim of this paper is to provide an overview of the revolution of TinyML and a review of tinyML studies, wherein the main contribution is to provide an analysis of the type of ML models used in tinyML studies; it also presents the details of datasets and the types and characteristics of the devices with an aim to clarify the state of the art and envision development requirements.

show abstract

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Cited by 40 publications

References 38 publications

Air-pollution prediction in smart city, deep learning approach

Air-pollution prediction in smart city, deep learning approach

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Contact Info

Product

Resources

About