Application of 5G next generation network to Internet of Things

Borkar, Suresh; Pande, Himangi

doi:10.1109/iota.2016.7562769

Cited by 79 publications

(45 citation statements)

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“…In this context, 5G technology can lead the high data rate IoT applications such as smart city, smart grid, smart medicines, connected cars, and connected homes . By 2020, more than 50 billion devices will be connected by internet . Alaa et al discussed the IoT‐based smart home application issues and challenges such as latency and reliability.…”

Section: Background and History Of Tactile Internetmentioning

confidence: 99%

Tactile internet and its applications in 5G era: A comprehensive review

Gupta

Tanwar

Tyagi

et al. 2019

Int J Communication

127

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Over the last few years, communication latency has been a major hurdle for most of the applications deployed in different network domains. During this era, a number of communication protocols and standards were developed and used by the community. However, still, the problem of latency persists keeping in view of the quality of service (QoS) and quality of experience (QoE) for different applications. To mitigate the aforementioned issues, in this paper, we present an in-depth survey of state-of-the art proposals having tactile internet as a backbone for delay mitigation using 5G networks for future ultra-reliable low-latency applications such as Healthcare 4.0, Industry 4.0, virtual reality and augmented reality, and smart education. From the existing proposals, it has been observed that tactile internet can provide interactions between virtual objects to give a feel of real environment with maximum latency of 1 millisecond. Also, this paper highlights the key differences between the tactile internet and Internet of Things in context with 5G revolution. Then open issues and challenges of tactile internet for smart applications are analyzed. Finally, a comparison of existing proposals with respect to various parameters is presented, which allows the end users to select one of the proposals in comparison with its merits over the others. KEYWORDSHealthcare 4.0, Industry 4.0, IoT, latency, reliability, tactile internet, 5G INTRODUCTIONMobile communication allows users to exchange data using smart devices ubiquitously using the mobile internet (MI). MI has millions of connected smart devices irrespective of their locations. It has revolutionized the different segments of industries such as health care, logistics, education, gaming, and transportation for maintaining quality of service (QoS) and quality of experience (QoE) for different applications. Moreover, it provides a device-to-device (D2D) communication, ie, ubiquitous communication for data sharing and communication between devices. This D2D communication coined the new term, the Internet of Things (IoT), 1 which provides the low power devices or equipments to perform specific tasks in the environment where they are deployed. IoT is being used in various smart city applications such as smart lightening, smart parking, waste management, traffic congestion, and forest fire detection. In D2D communication, the mobile devices can share prime or critical information in which a delay of even millisecond can harm the persons life (for example, the information sharing during robotic telesurgery). Existing cellular systems infrastructure is not suitable for such sensitive data sharing because of low data rates and long delay (greater than 20 ms). However, for D2D communications, Int J Commun Syst. 2019;32:e3981.wileyonlinelibrary.com/journal/dac

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Section: Background and History Of Tactile Internetmentioning

confidence: 99%

Tactile internet and its applications in 5G era: A comprehensive review

Gupta

Tanwar

Tyagi

et al. 2019

Int J Communication

127

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“…The amount of User Equipment [UE] within the large and dense mobile networks has suffered a considerable increase in the latest years (Fernández, González, & Hernández, 2014) due to the considerable data demand caused by the current requirements of the mobile services (Borkar & Pande, 2016). In order to support these bandwidth demands, standards such as the so-called Long-Term Evolution [LTE] and Long-Term Evolution -Advanced [LTE-A] are required to perform an adequate radio resource management (Kibria, Villardi, & Nguyen, 2016;Hu & Pang, 2012) and to improve their optimization functions.…”

Section: Introductionmentioning

confidence: 99%

“…El número de Equipos de Usuario [Users Equipment, UE] de las redes móviles con gran densidad ha tenido un crecimiento considerable en los últimos años (Fernández, González, & Hernández, 2014), debido al gran consumo de datos provocado por los requerimientos de los servicios móviles actuales (Borkar & Pande, 2016). Para soportar estas exigencias de ancho de banda se requiere que estándares como LTE [Long Term Evolution] y LTE-A [Long Term Evolution -Advanced] realicen una buena gestión de los recursos radio (Kibria, Villardi, & Nguyen, 2016;Hu & Pang, 2012) y mejoren sus funciones de optimización, para responder con calidad a las necesidades de los escenarios de alta densidad de tráfico (Guio & Hernández, 2013).…”

Section: Introductionunclassified

Machine learning algorithms for inter-cell interference coordination

Narváez

Pérez

2018

sist.telemat.

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The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.

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“…Cognitive IoT systems have to take into account prior information available about signals carried over channels. In addition, scalability is another key IoT-driven requirement, which implies network growth with time [7]. SUs deployed in CRNs may have different types of spectrum detectors due to the heterogeneous nature of IoT systems.…”

Section: Motivationmentioning

confidence: 99%

“…It is shown that in 2020, there will be 50 billion devices connected to the Internet. Thus, it is believed that 75% of network traffic will be accounted for IoT-based devices in 2020 [7]. Most IoT nodes are low-power and are fundamental building blocks of large scale IoT systems.…”

Section: Internet Of Things Conceptmentioning

confidence: 99%

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

Gharib¹

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The emerge of Internet of Things (IoT) brings up revolutionary changes to the field of wireless communications. Providing connection to billions of IoT devices together with challenging IoT environments lead to the need of new technologies in order to fulfill IoT spectrum demands. Cognitive radio (CR) technology can be seen as one of the prominent solutions to the spectrum scarcity issue in IoT, where multiband cooperative spectrum sensing (CSS) is the key. In this thesis, we consider heterogeneous distributed learning-based CR networks (CRNs). Focus is given to learning approaches named as incremental, consensus, and diffusion. We perform a comparative study to investigate which one of them best fits cognitive IoT demands. Simulation results are performed illustrating potentials of diffusion and consensus algorithms and disadvantages of incremental for cognitive IoT systems. Lack of centralized control, increase in number of devices, and dynamic environments place a room for lots of challenges in the CSS process. Conventional CSS techniques have to be improved in order to fulfill sophisticated IoT requirements. One of the main challenges is cooperative secondary users' (SUs') scheduling to sense a subset of channels. To overcome the aforementioned challenge, in this thesis, we propose a novel multi-band CSS scheme, named as heterogeneous multi-band multiuser CSS (HM2CSS). The proposed scheme allows heterogeneous SUs to sense multiple channels and works by selecting cooperative SUs in two stages. Only SUs owning different information about channels are chosen to be cooperative. This is done by selecting leaders for each channel in the first stage and corresponding cooperative SUs in the second stage. Careful choice of leaders reflects the selection of cooperative SUs and hence, improves system performance. Then, diffusion learning algorithm is used to exchange locally sensed information among cooperative SUs for all channels. Further, the decision on the availability of channels is made. Extensive simulation results illustrate that the proposed HM2CSS scheme satisfies the IEEE 802.22 detection performance standard. Detection performance and CRN To my beloved family v Table of Contents vii List of Tables x List of Figures xi Nomenclature xiv List of Tables 4.1 Comparison of incremental, consensus, and diffusion-based learning approaches' characteristics. .

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Application of 5G next generation network to Internet of Things

Cited by 79 publications

References 1 publication

Tactile internet and its applications in 5G era: A comprehensive review

Tactile internet and its applications in 5G era: A comprehensive review

Machine learning algorithms for inter-cell interference coordination

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

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