IOT: Applications, Challenges and Latest Trends

Jaskaran,; Khan, Nazia; Nandini,; Gupta, Kamali

doi:10.1109/icidea53933.2022.9970100

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…One of the key technical attributes of smart cities is the deployment of many sensors for data collection to enable intelligent real-time decision making for various city functions and citizen needs. Much of the data has strict security requirements because it is private to citizens or sensitive to critical infrastructure [36].…”

Section: Discussionmentioning

confidence: 99%

Smart City Information Systems: Research on Information Published for Citizens and Design of Effective Content in the Czech Republic

Důbravová,

Bureš

2023

Smart Cities

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The concept of Smart Cities integrates innovative technologies to improve citizens’ quality of life in towns and cities worldwide. Crisis management is a separate section directly managed by the leadership of municipalities, cities and counties in cooperation between police, fire and municipal police to ensure the safety of residents and safety in public spaces. The purpose of this study is to investigate to which extent publicly available information related to the field of crisis management is unavailable to residents in municipalities, towns and cities through online information systems. The primary aim is to provide suggestions for a general information system structure and content that would highlight and satisfy the need to address the crisis management issue, especially in providing immediate information to the population through an innovative online form. The achievement of this goal is methodologically based on qualitative research analysing and comparing the information published for residents through Smart City information systems in selected towns and municipalities. Document analysis or conceptual design was applied, and evaluation criteria for objective assessment of Smart City information systems were appropriately determined. The comparative analysis based on this set of criteria enabled the development of the proposals of information systems’ content that can be used to keep the information systems for Smart Cities in cities, municipalities and regions, actual and beneficial. From the available resources, two main modules that focused either on citizens or cities were synthesised. Moreover, SWOT analysis or the Smart Regions Rapid Response structure was derived. Acquired results outline generic structures and contents that support the development of the concept of Smart Cities and can be suitably implemented for the development of the modification of information systems containing relevant information for residents, cities and municipalities, focusing on citizen safety.

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Section: Discussionmentioning

confidence: 99%

Smart City Information Systems: Research on Information Published for Citizens and Design of Effective Content in the Czech Republic

Důbravová,

Bureš

2023

Smart Cities

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“…The continuous and exponential growth of the Internet of Things (IoT) created an automation-driven society, where unique devices are interconnected in order to improve the quality of healthcare, industry, transportation, etc. [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Classification of IoT Traffic

Velichkovska¹,

Cholakoska²,

Atanasovski³

2023

RADIOENGINEERING

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With the rapid expansion and widespread adoption of the Internet of Things (IoT), maintaining secure connections among active devices can be challenging. Since IoT devices are limited in power and storage, they cannot perform complex tasks, which makes them vulnerable to different types of attacks. Given the volume of data generated daily, detecting anomalous behavior can be demanding. However, machine learning (ML) algorithms have proven successful in extracting complex patterns from big data, which has led to active applications in IoT.In this paper, we perform a comprehensive analysis, including 4 ML algorithms and 3 neural networks (NNs), and propose a pipeline which analyzes the influence data reduction (loss) has on the performance of these algorithms. We use random undersampling as a data reduction technique, which simulates reduced network traffic data. The pipeline investigates several degrees of data loss. The results show that models trained on the original data distribution obtain accuracy that verges on 100%. XGBoost performs best from the classic ML algorithms. From the deep learning models, the 2-layered NN provides excellent results and has sufficient depth for practical application. On the other hand, when the models are trained on the undersampled data, there is a decrease in performance, most notably in the case of NNs. The most prominent change is seen in the 4-layered NN, where the model trained on the original dataset detects attacks with a success of 93.53%, whereas the model trained on the maximally reduced data has a success of only 39.39%.

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