Amalgamation of Advanced Technologies for Sustainable Development of Smart City Environment: A Review

Rani, Sita; Mishra, Ram Krishn; Usman, Mohammed; Kataria, Aman; Kumar, Pramod; Bhambri, Pankaj; Mishra, Amit Kumar

doi:10.1109/access.2021.3125527

Cited by 60 publications

(27 citation statements)

References 199 publications

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Section: Methodsmentioning

confidence: 99%

“…The data collected can be exported in the form of JSON files. This data can be used for the application of machine learning models to create better predictions on the data in a city or smart city [13]. Data set 2 is based on values derived from AQI Index India (Chennai city) and has been retrieved from Kaggle [77].…”

Section: Smart Gatewaymentioning

confidence: 99%

“…A lower AQI value indicates good air quality, whereas a higher number indicates poor. The Internet of Things (IoT) and artificial intelligence (AI) have made human lives easy by improved monitoring and improved control of operational processes in many domains [12][13][14][15][16]. Although IoT finds its use in many industries and applications, its ability to monitor pollution levels within cities by using specific devices and sensors makes it a necessary technology for pollution control [17,18].…”

Section: Introductionmentioning

confidence: 99%

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AI‐ and IoT‐based hybrid model for air quality prediction in a smart city with network assistance

Kataria¹,

Puri

2022

IET Networks

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Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location.

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Section: Methodsmentioning

confidence: 99%

Section: Smart Gatewaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AI‐ and IoT‐based hybrid model for air quality prediction in a smart city with network assistance

Kataria¹,

Puri

2022

IET Networks

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“…Shopping will be futuristic: one of the presently being explored options is the development of stores equipped with a touch screen system that allows anybody to choose and purchase things. Using a home delivery service will alleviate mobility issues [256]. Private automobiles will be prohibited in the city.…”

Section: Smart Energymentioning

confidence: 99%

A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities

et al. 2022

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Smart City has been an emerging research domain for Government, Businesses, and researchers in the last few years. The Indian government is also interested and investing lots of funds to develop smart cities. These cities are technology-based and require interdisciplinary research and development for successful implementation. Over the last few decades, various technological interventions have created a tendency to provide smart everyday objects to make human life more comfortable. The emergence of the smart city paradigm is a response to creating a future city that guarantees the well-being and rights of its citizens from the perspective of industrial development: industry, urban planning, environment, and sustainable development. There are several subdomains in the smart city for the research. To work with the different subdomains in a smart city, proper guidance about the background of the smart city is required. This research paper is a guide for the same. This research paper represents a systematic literature review of the smart city domain. This paper carries out a systematic review of research papers published in various well-reputed journals like IEEE, Springer, Elsevier, etc., between 2011 and. This paper will help the government, businesses, and researchers aiming to enhance the smart city concept. Initially, this paper discusses the origin and emergence of this concept, followed by a few definitions and characteristics with the real roadmap and primary supporting pillars of the smart city. This paper discusses a typical architecture having different layers like Sensing, Transportation, Data Management, and Application Layers. There are various supporting technologies and platforms for the smart city; hence implementations are impossible without these technologies and media. This research paper discusses different components of the smart city. A broad literature survey is being done to observe various challenges, opportunities, and future trends in the smart city. This research paper can guide the researchers and provide the research direction in the smart city domain.

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“…Automation is currently gaining ground in a variety of fields like Digital Assistance [1], Information Security [2][3][4], Medical [5,6], Psychology Management [7], Smart Cities [8], education and training [9]. One of the best tools for learning to program is programming online judge (POJ), which allows students to practice programming and logic building.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

Ramesh¹,

Kannimuthu²

2023

Intelligent Automation &Amp; Soft Computing

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The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners' current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners' skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners' learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners' current skill level and learning goals outperforms the other approaches in practice problem recommender systems.

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Amalgamation of Advanced Technologies for Sustainable Development of Smart City Environment: A Review

Cited by 60 publications

References 199 publications

AI‐ and IoT‐based hybrid model for air quality prediction in a smart city with network assistance

AI‐ and IoT‐based hybrid model for air quality prediction in a smart city with network assistance

A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

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