Big Data Analysis and Deep Learning in Smart Environments:

Uncovering Key Technologies for an Intelligent Future

Jiahao Huang

Emergency Technology and Management, Nanjing Tech University, Puzhu South Road, Nanjing, China

Keywords: Smart Environment, Deep Learning, Big Data.

Abstract: Smart City (SC) is a hot issue in the economic and social development of various countries today. In the

developed digital era, big data analysis (BDA) plays an indelible role in all aspects of SC. This study pays

special attention to the environmental aspect in the development of SC. This article summarizes previous

research, analyzes and summarizes the application of multiple digital technologies such as BDA and deep

learning (DL) in smart environment (SE) in air pollution index monitoring and urban Solid Waste

Management Applications. The prediction model processed by BDA realizes the prediction of PM2.5 and

other air pollutants and data visualization processing to achieve real-time monitoring of urban air pollutants.

City managers can understand the air quality status through real-time monitoring data; through DL The

architecture's data model enables intelligent management of urban waste, further enhancing waste processing

and energy recycling. Research points out that BDA and DL still have great potential in the development of

smart environments. The development of smart environments can be studied through a deep neural network

model for a comprehensive and systematic model. In the era of information management, city managers use

data-driven decisions to help improve the efficiency and quality of urban governance.

1 INTRODUCTION

In recent years, smart cities (SC) (Gracias et al., 2023)

have been receiving widespread attention around the

world. With the advancement of the times and

technology, the growth of data is tending to a new

peak. Statistics show that by 2050, nearly 70% of the

world’s population will live in cities (Mirza et al.,

2024). The concept of smart cities is proposed to

provide people with a more convenient and intelligent

life. Governments around the world are strengthening

the construction of smart cities. With the explosion of

big data, people's interest in smart cities and big data

analysis (BDA) (Olaniyi et al., 2023) is increasing

steadily. Big data and smart cities have become two

unavoidable topics. Indeed, a smart city is a

comprehensive and complex system. The problems

faced in smart cities from education, health,

environment, transportation, management, and all

aspects of life cannot be separated from the support

of data. As a data-driven tool, BDA has great

potential. The BDA framework supports the ability of

data-driven decision-making in SC (Olaniyi, 2023).

https://orcid.org/0009-0006-6895-1023

At present, economic, social and other sustainable

development issues are attracting widespread

attention around the world, among which the

environment is one of the most important factors

(Ullo, S. L., and Sinha, G. R., 2020). And the growth

of population migration to cities has led to excessive

density of residents and increased urban economic

burdens. Automobilization has caused traffic

problems. Technological development has led to

changing requirements of residents and enterprises

for the quality and capabilities of the urban

environment. All of this ultimately leads to increased

environmental pressure and the emergence of

environmental problems (Turgel et al., 2019).

Therefore, it is particularly important for the

construction of smart environment in smart cities. At

this stage, there are many studies using big data

processing platforms, machine learning methods and

Artificial Intelligence (AI) intelligent technology to

analyze and process smart environments including air

pollution index prediction, urban sewage system

Huang, J.

Big Data Analysis and Deep Learning in Smart Environments: Uncovering Key Technologies for an Intelligent Future.

DOI: 10.5220/0012909100004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 80-84

ISBN: 978-989-758-713-9

construction and real-time monitoring of traffic

conditions. Smart cities must provide a unified

framework to manage and control these services in an

intelligent manner and, where possible, facilitate

integration between these services. One service can

have a significant impact on the quality of other

services. Shih et al has established six integrated

prediction models by collecting PM2.5 sensor data

from the open-source community LASS to predict

Taiwan's PM2.5 concentration in the next 30 to 180

minutes at a 30-minute frequency (Shih et al., 2021).

In terms of taking the prediction of PM2.5 as an

example, some research uses Gaussian SVM

(Bhuvaneshwari et al., 2022) to predict air pollutant

concentrations, classifying polluted areas into heavy

traffic and not heavy traffic, this method only divides

the areas with heavy traffic or not, and does not

integrate the real-time traffic data of an area into the

air pollutant pre-detection model. There is no

intuitive and usable model for comprehensive

preprocessing of multi-faceted data. Judging from

current research, there are few studies that can

provide a unified overview of such a complex and

large system.

This article aims to review the views of latest

studies on smart environment, analyzes the current

development issues of smart environment, and

comprehensively introduce the multiple applications

of digital technologies such as BDA and machine

learning methods in the field of smart environment,

and propose innovative solutions to provide

reference.

2 METHOD

2.1 The Framework of Big Data

Technologies for the Environment

in Smart Cities

The application of big data analysis in smart

environments refers to the use of big data technology

and analysis methods to process large amounts of data

generated in smart environments, thereby achieving

intelligent management and optimization of smart

environments and helping people better understand

the operation of the environment. Figure1

Demonstrates how static and real-time data flow in

information systems, including how data flows

through information systems, how external processes

or entities create or use data, and how data is stored.

The information system first collects data from the

smart city environment through the data transmission

mechanism or directly calls the data from the

database for data preprocessing. The BDAFC

architecture adopts a hybrid approach to process

historical data and real-time data, while utilizing

multiple learning algorithms to implement decision-

making tasks such as classification, association rules,

anomaly detection, and prediction. Data

preprocessing methods are divided into 3 categories:

data cleaning, data integration, and data filtering.

Data preprocessing improves data quality by

performing multiple tasks and activities such as data

conversion, merging, and standardization, and then

visualizes and summarizes the data to the application.

2.2 Air Pollution Index Monitoring

Air pollution problems are becoming increasingly

serious, air quality is deteriorating, and air pollution

levels in urban areas are rising, leading to many

serious environmental problems in many cities.

Adequate air quality prediction has become a serious

challenge to population health, and the impact of air

pollutants on Real-time monitoring is of great

significance to the management of air environment.In

recent years, big data science and technology has

developed vigorously. People have built various

models of big data in various aspects to find the

patterns of problems and methods to solve problems.

At this time, big data analysis is used to monitor air

pollutants in real time and improve the air

Figure 1: BDA in smart environment (Photo/Picture credit: Original).

Big Data Analysis and Deep Learning in Smart Environments: Uncovering Key Technologies for an Intelligent Future

environment. The governance of the country plays an

important role that cannot be ignored.

Shih et al. (Shih, et al., 2021) used PM2.5 sensor

data from the open-source community LASS as input

variables, and used the Spark computing framework

and integrated machine learning methods to train the

PM2.5 concentration prediction model, then

evaluated the model through the evaluation indicators

RMSE and R2. For prediction performance, the data

visualization tool PlotDB was finally used to generate

a map of PM2.5 concentration value distribution. Its

research also found that using ensemble models with

different time periods and other variables can

improve forecasting performance. However, the input

variables of the prediction model only include PM2.5,

PM10, PM1, temperature and relative humidity. It has

not been possible to design a unified model for

various variables related to PM2.5 such as rainfall,

wind direction and traffic data. predict. This is

nothing more than a huge test for the data processing

capabilities, which will require the update of more

powerful algorithms and more powerful computing

power to process the huge data in the context of smart

environments. Myeong et al. (Myeong and Shahzad

et al., 2021) used machine learning and deep learning

methods to solve problems related to air pollution

monitoring. They used Gaussian SVM to predict air

pollutant concentrations and classify polluted areas

into heavy and light traffic congestion areas. Huang

et al developed a deep neural network model, the

CNN-LSTM model (Huang et al., 2018), to learn and

predict the PM2.5 concentration in the next hour

through historical data (such as cumulative hourly

rainfall, cumulative wind speed and PM2.5

concentration).

Different studies have proposed different

monitoring models for various variables related to

PM2.5 prediction. Taking into account the same

differences in the models, a comprehensive BDA

model is studied for real-time prediction of PM2.5.

2.3 Waste Management in Smart Cities

As the air pollution index continues to rise, waste

management in smart cities has become a burning

problem.In recent years, as urbanization, income and

consumption have increased, so has the generation of

waste.It is estimated that the amount of global waste

is expected to increase to 2.8 billion tons by 2050

(Szpilko et al., 2023). The implementation of new

waste monitoring and management systems has

become an important area for the sustainable

development of smart environments.

Sana Shahab et al (Shahab et al., 2022) found that

compared with traditional machine learning and

image processing methods, DL models provide more

advanced technology in smart waste management

(SWM) with significant effects and efficiency.

Therefore, deep learning models have gained enough

momentum in the SWM solid waste management

research community to solve many problems. Patric

Marques, et al. (Marques et al., 2019) implemented

waste management in smart cities by implementing a

smart waste management architecture based on

indoor and outdoor waste management scenarios with

three different protocols designed to provide secure

communication - message queues Telemetry

Transport Protocol (MQTT), Constrained

Application Protocol (CoAP) and Hypertext Transfer

Protocol Secure (HTTPS) are implemented together.

Evaluation metrics such as energy consumption,

latency, jitter, and throughput were considered when

evaluating the system. Based on the results obtained,

scalability was also analyzed, taking into account the

impact on the system of increasing the number of

concurrent bins in the system.

2.4 BDAs Application Analysis of Air

Pollution Index and Waste

Management

First, in response to the air pollution problem,

researchers used big data technology and machine

learning algorithms to build an air quality prediction

model. Take the study by Shih et al. as an example.

They used PM2.5 sensor data from the open source

community LASS and trained a PM2.5 concentration

prediction model through the Spark computing

framework and integrated machine learning methods.

The model evaluates its predictive performance by

evaluating indicators such as RMSE and R2, and uses

data visualization tools to generate maps of the

distribution of PM2.5 concentration values, providing

an important reference for decision-makers. On the

other hand, in the field of waste management, big data

analysis also shows great potential. Researchers have

made significant progress in smart waste

management through big data analysis technology,

especially deep learning models. Compared with

traditional machine learning and image processing

methods, deep learning models show higher

efficiency and effectiveness in smart waste

management.

Although big data analysis has made some

progress in air pollution index monitoring and waste

management in smart cities, it still faces many

challenges. First, the quality and completeness of data

EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence

are critical to the accuracy and reliability of the

model. Secondly, model complexity and

computational resource requirements limit the

possibility of large-scale applications. In addition,

privacy and security issues also need to be fully

considered. In the future, we can address these

challenges by further improving algorithms and

models, improving data quality and integrity,

strengthening data sharing and cooperation, and

strengthening privacy protection and security

measures. At the same time, interdisciplinary

cooperation is also the key to promoting smart city

environmental monitoring and management,

combining professional knowledge and technology in

computer science, environmental science, sociology

and other fields to jointly promote the sustainable

development of smart cities.

3 DISCUSSIONS

Environmentally sustainable smart cities are a rapidly

developing trend (Bibri et al., 2023). It can be seen

from the above research that with the advent of the

digital age, intelligent management of cities and

intelligent monitoring of the environment are

particularly important. With the advent of the digital

age and the intelligent management of cities,

intelligent monitoring of the environment has become

particularly important, including air pollution index

monitoring, urban waste management, etc. Relying

on powerful computing algorithms, it can be analyzed

real-time data of the environment. However, it can

also be seen from a large number of studies that in the

smart environment, Construction-wise, people still

have a long way to go.

The PM2.5 prediction model studied by Shih et al

(Shih et al., 2021) has only five input variables,

namely PM2.5, PM10, PM1, temperature and

humidity, excluding other variables related to PM2.5,

such as rainfall, Wind direction and traffic data.

K.S.Bhuvaneshwari et al. (Bhuvaneshwari et al.,

2022) used Gaussian vector computer to roughly

consider traffic data into PM2.5 prediction, but there

are few studies on a comprehensive and systematic

model. However, in the face of such a huge amount

of data, a model for real-time prediction of

environmental monitoring will undoubtedly require a

very large algorithm to process data from many

aspects in real time. The update of computing power

may become a major limitation in the subsequent

development of smart environments.

Data show that 700-900 million tons of waste are

produced globally every year (Chen et al., 2020).

Especially in developing countries, SWM has

received more and more attention in recent years.

Real-time monitoring and effective management of

urban waste have become the key to smart

environment construction. an important topic. Lin,

Kunsen, et al. (Lin et al., 2022) reviewed the

application of deep learning in solid waste

management and used different algorithm models

such as ANN, CNN, RNN/LSTM, Attention and

GAN to recycle waste. The DL model has been used

in the field of SWM. Four main applications include

waste detection, identification, bin level detection and

waste generation prediction (Shahab et al., 2022; Lin

K et al., 2022). The use of deep learning algorithms

to solve solid waste problems has great potential, but

the application of deep learning in solid waste

management is still at a relatively preliminary stage

and requires further development. In the future

development process, the use of deep learning

algorithms to analyze historical and real-time data can

be studied to predict the amount and type of solid

waste generated in the future. This helps plan and

optimize the construction and operation of waste

treatment facilities, realize automated control and

intelligent management of waste treatment

equipment, improve processing efficiency and reduce

energy consumption.

4 CONCLUSIONS

Good environmental quality is an important reflection

of a city's image. This article mainly studies the

application of BDA in smart environments. Through

a review of previous research, we can understand that

BDA penetrates into all aspects of smart cities in

today's digital information age. This article

specifically focuses on air The prediction of pollution

index and the management and regulation of

municipal solid waste are described. The BDA model

for monitoring and prediction of multiple air

pollutants such as PM2.5 uses a deep neural network

model to combine CNN and LSTM architecture to

predict PM2.5 concentration. Perform real-time

monitoring; at the same time, deep learning models

also play an important role in urban waste

management. Research has found that DL provides

more advanced technology in waste management,

making waste identification and control more

efficient. Through BDA's research, city managers can

understand air quality conditions and identify solid

waste through real-time monitoring data, make

decisions based on scientific data, and take

corresponding governance measures to improve air

Big Data Analysis and Deep Learning in Smart Environments: Uncovering Key Technologies for an Intelligent Future

quality and protect citizens' quality of life. The above

data-driven decision-making can help improve the

efficiency and quality of urban governance, promote

the development of circular economy, help reduce

resource consumption and environmental load,

promote the transformation of cities into a circular

economy model, and achieve sustainable

development goals.

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