Improving communication precision of IoT through behavior-based learning in smart city environment

Li, Daming; Deng, Lizhen; Liu, Wenjian; Su, Qinglang

doi:10.1016/j.future.2020.02.053

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…When the ASAS is used, the fog gives devices to exchange information with PCS. Authors in [8] reported the advancements of remote sensor organization (WSN), correspondence innovation, and IoT innovation. Authors in [9] used ML techniques such as KNN, SVM, DT, Naïve Bayes, neural networks, and RF which can be applied in IDS.…”

Section: Related Workmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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Section: Related Workmentioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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“…Because of the importance of smart cities to various stakeholders and the advantages and drawbacks associated with their implementation, the concept of smart cities has piqued the interest of researchers in a variety of fields, including informatics. Smart cities also address crucial issues of today's urban living, from transportation [10], energy use [11], and the environment to local government and citizen inclusion [12]. The concept of smart cities has revolutionized the modern world by providing a possible solution to air-pollution-related problems by providing a unique blend of technology [13], data [14], and the Internet Things [15].…”

Section: Introductionmentioning

confidence: 99%

“…A considerable rise has occurred in the types and number of sources that emit air pollutants in the region [23]. Intense industrial activity, large populations, and an unprecedented rise in motor vehicle usage pose severe environmental impacts in the region [11]. Air pollution is a significant threat to environment departments, which have not been updated for many years in South Asia, especially in Pakistan [7].…”

Section: Introductionmentioning

confidence: 99%

Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities

Myeong

Shahzad

2021

Sustainability

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The COVID-19 pandemic has demonstrated that creative leadership based on data and citizen volunteers is more significant than vaccines themselves, so this study focuses on the collaboration of sophisticated technologies and human potential to monitor air pollution. Air pollution contributes to critical environmental problems in various towns and cities. With the emergence of the smart city concept, appropriate methods to curb exposure to pollutants must be part of an appropriate urban development policy. This study presents a technologically driven air quality solution for smart cities that advertises energy-efficient and cleaner sequestration in these areas. It attempts to explore how to incorporate data-driven approaches and citizen participation into effective public sector pollution management in smart cities as a major component of the smart city definition. The smart city idea was developed as cities became more widespread through communication devices. This study addresses the technical criteria for implementing a framework that public administration can use to prepare for renovation of public buildings, minimizing energy use and costs and linking smart police stations to monitor air pollution as a part of an integrated city. Such a digital transition in resource management will increase public governance energy performance and provide a higher standard for operations and a healthier environment. The study results indicate that complex processes lead to efficient and sustainable smart cities. This research discovered an interpretive pattern in how public agencies, private enterprises, and community members think and what they do in these regional contexts. It concludes that economic and social benefits could be realized by exploiting data-driven smart city development for its social and spatial complexities.

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“…Developing countries are challenging where the urbanization rate is up to 50% [9]. Smart cities address crucial issues of today's urban living, from transportation [10], energy [11], the environment to government, and citizen inclusion [12]. The concept of smart cities has revolutionized the modern world by providing a possible solution for the above problems by providing a unique blend of technology [13], data [14], and internet things [15].…”

Section: Introductionmentioning

confidence: 99%

“…A considerable rise has occurred in the types and number of emission sources of air pollutants in the region [23]. Intense industrial activity, a large population, an unprecedented rise in motor vehicle usage pose severe environmental impacts in the region [11]. Air pollution is a significant threat to the environmental departments, which is not updated for many years in South Asia, especially in Pakistan [7].…”

Section: Introductionmentioning

confidence: 99%

How to Integrate the Data-based Strategies and Advanced Technologies into Efficient Air Pollution Management in Smart Cities?

Myeong¹,

Shahzad²

2021

Preprint

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Air pollution contributes to a critical environmental problem in various towns and cities. With the emergence of the smart cities concept, appropriate methods to curb associated with exposure to pollutants must have been a portion of appropriate urban development policy. This study presents a technologically driven air quality solution in smart cities to advertise energy-efficient and cleaner sequestration in these areas. It aims to address the issue of how to integrate the data-based strategies and artificial intelligence into efficient public sector pollution management in smart cities as a core part of the smart city definition. Exploratory research has been used in 152 smart cities, and environmental experts contributed to this study. It further addresses the technical criteria for implementing such a framework that the public administration uses to prepare the renovation of public buildings, minimize energy use and costs, and link these smart police stations to monitor air pollution as a part of integrated cities. Such a digital transition in resource management will increase public governance's energy performance, a higher standard of operation, and a healthier environment.

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Improving communication precision of IoT through behavior-based learning in smart city environment

Cited by 31 publications

References 27 publications

Attack Detection in IoT using Machine Learning

Attack Detection in IoT using Machine Learning

Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities

How to Integrate the Data-based Strategies and Advanced Technologies into Efficient Air Pollution Management in Smart Cities?

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