Analysis of the Effect of the Reliability of the NB-Iot Network on the Intelligent System

Jia, Gangyong; Zhu, Yujie; Li, Youhuizi; Zhu, Zongwei; Zhou, L.

doi:10.1109/access.2019.2932870

Cited by 15 publications

(10 citation statements)

References 45 publications

(49 reference statements)

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“…A Dropout layer (1-10) is included to randomly deactivate certain neurons during training to avoid overfitting and improve the model's generalisation. The two classes (like "Nor-mal" and "Phishing") are represented by the two output units in the last fully linked layer, Linear (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). It features 258 customizable settings.…”

Section: Robustnessmentioning

confidence: 99%

“…The Internet of Things (IoT) environment is currently experiencing several developments. One notable development is the expansion of the scale and diversity of IoT data, which has led to improvements in the industry ecosystem and the emergence of innovations in different areas [1,2,3]. Technological advancements have driven application products towards intelligence, convenience, low power consumption, large connectivity, and wide coverage [1,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…One notable development is the expansion of the scale and diversity of IoT data, which has led to improvements in the industry ecosystem and the emergence of innovations in different areas [1,2,3]. Technological advancements have driven application products towards intelligence, convenience, low power consumption, large connectivity, and wide coverage [1,4,5]. Additionally, there has been a focus on improving IoT intrusion detection systems (IDS) using artificial intelligence (AI) techniques, particularly machine learning (ML) algorithms [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

Gupta,

Gaurav,

Attar

et al. 2024

Preprint

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There is a serious risk to information privacy and security in the IoT ecosystem from phishing attacks. This research offers a unique strategy for increasing phishing attack detection by employing the Cuckoo Search algorithm to tune hyperparameters in a deep Convolutional Neural Network (CNN) model. Our suggested approach is very effective, with a 90\% success rate in detecting phishing attacks. To distinguish between legal and fraudulent IoT network activity, we systematically tune hyperparameters using Cuckoo Search to optimise the deep CNN model's performance. This study helps ensure the safety of sensitive information and IoT devices by providing a novel and effective approach for real-time phishing detection.

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Section: Robustnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

Gupta,

Gaurav,

Attar

et al. 2024

Preprint

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“…Reliable architecture, operation and application development must address errors in the hardware, the software, interactions with the physical environment, and interactions with the human users [32]. The authors in [33] explored the reliability of the NB-IoT network in intelligent systems.…”

Section: Reliabilitymentioning

confidence: 99%

IoE: Towards application-specific technology selection

Paul¹,

Biswas²,

Rashvand³

2021

ITU-J FET

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Determining the suitability of any technology for an Internet of Everything (IoE) application is essential in the presence of diverse technologies and application requirements. Some of the IoE applications include smart metering, wearables, healthcare, remote monitoring, inventory management and industrial automation. Energy efficiency, scalability, security, low-cost deployment and network coverage are some of the requirements that vary from one application to another. Wireless technologies such as WiFi, ZigBee, Bluetooth, LTE, NB-IoT, LoRa and SigFox will play crucial roles in enabling these applications. Some of the technological features are transmission range, bandwidth, data rate, security schemes and infrastructure requirements. As there is no one-size-all network solution available, the key is to understand the diverse requirements of different IoE applications and specific features offered by different IoE enabling technologies. Application-specific technology selection will ensure the best possible utilization of any technology and the quality of service requirements. An overview of network performance expectations from various IoE applications and enabling technologies, their features and potential applications are presented in this paper.

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“…The adding nodes are numbered from 24 to 45 accordingly, and the load power is set as the average power value of the test system. Using the same method to solve the optimal configuration model, the optimal installation number of the acquisition unit of the fault indicator increases from 14 to 29, and the corresponding installation position is point 2, 3,4,5,6,8,9,10,11,12,13,15,18,22,25,26,27,28,30,31,32,33,34,35,40,42,43,44, and point 45, as shown in Figure 11. The optimal installation number of the collection unit still equals 1, but the corresponding installation position changed to point 28, as shown in Figure 11.…”

Section: Effect Of the Junction Nodes Of The Power Linementioning

confidence: 99%

Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network

Tang

Wang

et al. 2020

Energies

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Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

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Analysis of the Effect of the Reliability of the NB-Iot Network on the Intelligent System

Cited by 15 publications

References 45 publications

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

A Novel Cuckoo Search-Based Optimized Deep CNN Model for Phishing Attack Detection in IoT Environment

IoE: Towards application-specific technology selection

Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network

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