Dimensions of Internet of Things: Technological Taxonomy Architecture Applications and Open Challenges—A Systematic Review

Kumar, Krishna; Kumar, Aman; Kumar, Narendra; Mohammed, Mazin Abed; Al-Waisy, Alaa S.; Jaber, Mustafa Musa; Shah, Rachna; Al-Andoli, Mohammed Nasser

doi:10.1155/2022/9148373

Cited by 24 publications

(8 citation statements)

References 139 publications

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“…For both training and testing datasets, the data cleaning is performed. The symbolic features are converted into numerical features since ML and DL approaches are working on real number vectors [13]. The feature label is categorized into normal and abnormal network traffic.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…while Compares to the existing ML based feature selection models [17], at each step it remove features various in PCRFE, irrelevant features are removed at once. The features Correlation Coefficient is calculated using the Equation (13).…”

Section: Feature Extraction Using Proposed Decision Tree With Pcrfe (...mentioning

confidence: 99%

“…Step 14: Weight calculation as correlation and -1 indicates negative ata. while Compares to the existing ML tion models [17], at each step it remove PCRFE, irrelevant features are removed res Correlation Coefficient is calculated (13).…”

Section: Feature Extraction Using Proposed Decision Tree With Pcrfe (...mentioning

confidence: 99%

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Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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Section: Data Preprocessingmentioning

confidence: 99%

Section: Feature Extraction Using Proposed Decision Tree With Pcrfe (...mentioning

confidence: 99%

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Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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“…In the third stage, if the data need further analysis and processing, they are sent to the cloud. Additionally, patients’ information and their medical history are stored in the cloud [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Alatoun

Matrouk

Mohammed

et al. 2022

Sensors

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In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

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“…The Internet of Things (IoT) is becoming one of the most popular technologies, including sensor technology, intelligent technology, and network technology [1]. With the rapid development of the IoT technologies, IoT devices are integrated into people's daily life (brilliant homes, medical services, and others) [2].…”

Section: Introductionmentioning

confidence: 99%

The Development of Privacy Protection Standards for Smart Home

Liu¹,

Yang

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of network technology, society has entered the Internet of Things (IoT) era. Enriching IoT devices enable smart home privacy information to be easily obtained through data mining. Smart home privacy protection (SHPP) has a farreaching impact on the development of all aspects of society. Industrial and academic circles pay great attention to the privacy protection of the smart home, and the ability to protect the privacy needs to be improved. SHPP standards are one way to evaluate privacy protection capabilities at this stage. Based on our research on privacy protection standards, this paper summarizes the development of privacy protection standards for the first time. It divides them into three phases: creation, exploration, and expansion. According to the characteristics of the privacy data rotation, the SHPP standard system is proposed, including basic commonality, critical technology, auxiliary management, test and certification, and device application. Finally, we focus on the opportunity and challenges of smart home privacy security standards brought by the current big data and prospect the trend of privacy protection technologies and standard study. They provide a connection for subsequent standard investigations.

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Dimensions of Internet of Things: Technological Taxonomy Architecture Applications and Open Challenges—A Systematic Review

Cited by 24 publications

References 139 publications

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

The Development of Privacy Protection Standards for Smart Home

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