Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challenges

Qadir, Zakria; Munawar, Hafiz Suliman; Saeed, Nasir; Lê, Khoa N.

doi:10.48550/arxiv.2111.06596

Cited by 3 publications

(3 citation statements)

References 101 publications

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“…Companies such as "Globalstar" and "Iridium Communications" have undertaken "satellite-based IoT" projects for the above-mentioned use case scenarios, which were challenging tasks for traditional "cellular communication systems" and "terrestrial technologies". Moreover, in the post 6G era, integration of "non-terrestrial technologies" the UAV, and satellite-assisted "communication technologies" will bring benefits of not only higher data rates, but facilitate the lives of people living in far flung regions, effective natural calamities, and disaster management opportunities [6,79,80].…”

Section: Satellite Communicationmentioning

confidence: 99%

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

2022

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The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

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Section: Satellite Communicationmentioning

confidence: 99%

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

2022

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“…-The Internet of Nano-Things and Bodies: This technology can be used in many application scenarios such as smart city, health care, military, etc. which will be using implantable sensors, smart wearable devices and low-power devices [56], [57]. -Massive IoT integrated smart cities: Realization of futuristic smart cities will heavily depend on the development of a highly secure and reliable system capable of handling massive data in various sectors such as transportation, healthcare, parking, utilities, etc.…”

Section: Potential Smart Services Enabled By 6gmentioning

confidence: 99%

State-of-the-Art Strategies and Research Challenges in Wireless Communication for Building Smart Systems

Khuntia¹,

Das²,

Sinha³

et al. 2022

IJWMN

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The upcoming 6G communication network will revolutionize the scenario of customer services and related applications by building smart, autonomous systems. We first discuss here about 6G enabled smart applications like healthcare, smart city building, industrial IoTs, etc. We next provide a brief survey of the enabling technologies for achieving the required goals of 6G, such as terahertz communication, cell-free communication, holographic communication using beamforming, wireless power transfer, ultra-low latency communication, etc., with a view to tackle very large amounts of data traffic from several billions of smart interconnected devices while supporting ultra-low end-to-end communication latency. Finally, we present various research challenges involved in 6G communication towards building such smart systems.

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“…Though there are well-established systems that are in place to maintain patient safety, the field of drug interactions require further research [9] because recent reports indicate that drug reactions are one of a major cause of hospital-acquired conditions and readmissions [10][11][12][13][14]. Pharmacovigilance, which was initiated by the WHO, is gaining more attention, with the literature showing that almost one tenth of the adverse reactions seen in ICU subjects are due to DDIs causing ADRs [15,16]. Different evaluation and automation systems are being used for ADR detection in different medicines, but, in most cases, reports of ADRs are investigated and revealed by the healthcare professionals quite a bit later.…”

Section: Introductionmentioning

confidence: 99%

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

Salman

Munawar

Latif

et al. 2022

BDCC

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The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

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Towards 6G Internet of Things: Recent Advances, Use Cases, and Open Challenges

Cited by 3 publications

References 101 publications

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

State-of-the-Art Strategies and Research Challenges in Wireless Communication for Building Smart Systems

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

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