Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection

Gumaei, Abdu; Al-Rakhami, Mabrook S.; Hassan, Mohammad Mehedi; Pace, Pasquale; Alai, Gianluca; Lin, Kai; Fortino, Giancarlo

doi:10.1109/mnet.011.2000204

Cited by 53 publications

(28 citation statements)

References 15 publications

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“…Gumaei et al [85] combined blockchain and DRNN for edge computing 5G-enabled drone identification. Dataset was collected from raw radio frequency signals taken from many drones under several flight modes for the training of the DRNN model.…”

Section: Exploring Long Short-term Memory In 5g Wireless Mobile Networkmentioning

confidence: 99%

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi

2022

Mathematical Problems in Engineering

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The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.

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Section: Exploring Long Short-term Memory In 5g Wireless Mobile Networkmentioning

confidence: 99%

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi

2022

Mathematical Problems in Engineering

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“…In this way, the population will have services contracted from this technology to meet their needs from a fixed or mobile device. It will meet the needs of government and business projects in different areas [30]. Generally, the most distinguished features of 5G technology are as follow:…”

Section: G Technologymentioning

confidence: 99%

FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks

et al. 2021

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Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and the internet of medical things (IoMT) to address this research area. Edge computing can reduce the cost of cloud communication, including high latency and bandwidth use, by moving conventional healthcare services and applications closer to end-users. Artificial intelligence (AI) techniques such as deep learning (DL) have been used recently for automatic fall detection, as well as supporting healthcare services. However, DL requires a vast amount of data and substantial processing power to improve its performance for the IoMT linked to the traditional edge computing environment. This research proposes an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications. We also propose the use of a deep gated recurrent unit (DGRU) neural network to improve the accuracy of existing DL-based fall detection methods. DGRU has the advantage of dealing with time-series IoMT data, and it can reduce the number of parameters and avoid the vanishing gradient problem. The experimental results on two public datasets show that the DGRU model of the proposed framework achieves higher accuracy rates compared to the current related works on the same datasets.

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“…Public key infrastructure (PKI) can realize anonymous P2P trusted communication in distributed networks and has been extensively studied in the identity information management of edge computing. Considering the fact that identity trust is a part of the entire trust evaluation, and some of the contents are simplified, a lightweight key generation method is proposed [19,20]. In the process of the resource request, the identity trust of ECU is defined as Vi.…”

Section: Attribute-based Trust Evaluationmentioning

confidence: 99%

Digital Community Management Mobile Information System Based on Edge Computing

2021

Mobile Information Systems

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Edge computing refers to an open platform that integrates network, computing, storage, and application core capabilities on the side close to the source of things or data and provides nearest-end services nearby. “Digital Community” refers to the use of various information technologies and methods to integrate community resources and build a network platform for interactive exchanges and services between the government, property service agencies, residents, and various intermediary organizations within the community. In order to reduce the management tasks of community managers, reduce their community management burden, and improve the management efficiency of community managers, this article is based on the description of several keywords such as distributed edge computing, edge computing business usage scenarios, and digital community management. Edge computing technologies such as centralized traffic model and attribute-based trust evaluation avoid waste of network resources, reduce time delay and system energy consumption, and improve system operation efficiency. A digital community management mobile information management system is designed and developed. 102 owners of the community conducted visits and questionnaire surveys. After investigation and research, it was found that this system has good performance, meets the management needs of most communities, and can improve the management efficiency of community managers by 4%–6%; it has also improved community residents’ satisfaction with digital community management by 2-3%.

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Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection

Cited by 53 publications

References 15 publications

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks

Digital Community Management Mobile Information System Based on Edge Computing

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