Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Lin, Di; Tang, Yu; Labeau, Fabrice; Yao, Yuanzhe; Imran, Muhammad; Vasilakos, Athanasios V.

doi:10.1109/jsyst.2015.2441720

Cited by 23 publications

(9 citation statements)

References 25 publications

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“…It is expected that the advent of 5G technologies will play an active part in providing patient-oriented healthcare applications which can be easily managed by the patient or the carer. It is expected that 5G networks would carry a significantly higher volume of data while maintaining reliability [70], [71]. The critical requirement for 5G networks is ultra-low latency which will allow the deployment of mission critical applications such as tele-consulting, tele-surgery etc.…”

Section: Healthcarementioning

confidence: 99%

5G Communication: An Overview of Vehicle-to-Everything, Drones, and Healthcare Use-Cases

et al. 2019

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Advances in technology are not only changing the world around us but also driving the wireless industry to develop the next generation of network technology. There is a lot of buzz building over the advent of 5G that will facilitate the entire planet through continuous and ubiquitous communication connecting anybody to anything, anywhere, anytime, and anyhow regardless of the device, service, network, or geographical existence. 5G will also prove to be a paradigm shift including high carrier frequencies with massive bandwidths, having a large number of antennas, and with an extreme base station and device densities. In this paper, we investigate the potential beneficiaries of 5G and identify the use-cases, where 5G can make an impact. In particular, we consider three main use-cases: vehicle-to-everything (V2X) communication, drones, and healthcare. We explore and highlight the problems and deficiencies of current cellular technologies with respect to these use-cases and identify how 5G will overcome those deficiencies. We also identified the open research problems and provide possible future directions to cope with those issues. 5G, V2X communication, drones, healthcare, ultra-low-latency, ultra-high-reliability. INDEX TERMS

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Section: Healthcarementioning

confidence: 99%

5G Communication: An Overview of Vehicle-to-Everything, Drones, and Healthcare Use-Cases

et al. 2019

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“…The coming 5G provides a wider perspective in terms of those parameters. The surveys in [ 18 , 19 ] concluded that 5G, being reliable, can handle voluminous data having three properties—embb, urllc, and umtc—that allow tele-consulting and telesurgery applications to work. Working of WTS is just like Mater–Slave system where a Human surface interface (HSI) having a video console, headphones, and haptic device at the master side can operate on the patient using a robot by giving certain commands.…”

Section: Generations and Challenges Of Healthcare Systemsmentioning

confidence: 99%

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Anand

Rani

Anand

et al. 2021

Sensors

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The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

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“…Similarly, each organization, such as intelligent transportation, e-health, and military usage, has different perspectives on IoVs. 54 …”

Section: Incorporation Of Iovsmentioning

confidence: 99%

Characterizing the role of vehicular cloud computing in road traffic management

Ahmad

Noor

Ali

et al. 2017

International Journal of Distributed Sensor Networks

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Vehicular cloud computing is envisioned to deliver services that provide traffic safety and efficiency to vehicles. Vehicular cloud computing has great potential to change the contemporary vehicular communication paradigm. Explicitly, the underutilized resources of vehicles can be shared with other vehicles to manage traffic during congestion. These resources include but are not limited to storage, computing power, and Internet connectivity. This study reviews current traffic management systems to analyze the role and significance of vehicular cloud computing in road traffic management. First, an abstraction of the vehicular cloud infrastructure in an urban scenario is presented to explore the vehicular cloud computing process. A taxonomy of vehicular clouds that defines the cloud formation, integration types, and services is presented. A taxonomy of vehicular cloud services is also provided to explore the object types involved and their positions within the vehicular cloud. A comparison of the current state-of-the-art traffic management systems is performed in terms of parameters, such as vehicular ad hoc network infrastructure, Internet dependency, cloud management, scalability, traffic flow control, and emerging services. Potential future challenges and emerging technologies, such as the Internet of vehicles and its incorporation in traffic congestion control, are also discussed. Vehicular cloud computing is envisioned to have a substantial role in the development of smart traffic management solutions and in emerging Internet of vehicles.

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Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Cited by 23 publications

References 25 publications

5G Communication: An Overview of Vehicle-to-Everything, Drones, and Healthcare Use-Cases

5G Communication: An Overview of Vehicle-to-Everything, Drones, and Healthcare Use-Cases

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

Characterizing the role of vehicular cloud computing in road traffic management

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