Enhance Lighting for the Internet of Things

Jungnickel, Volker; Hinrichs, Malte; Bober, Kai Lennert; Kottke, Christoph; Corici, A.A.; Emmelmann, Marc; Rufo, Julio; Bök, Patrick-Benjamin; Behnke, Daniel; Riege, M.; Wu, Xinzhou; Singh, Ravinder; O’Brien, Dominic; Collins, Stephen F; Faulkner, F.; Vazquez, Marcos Martinez; Bech, Martin; Geilhardt, Frank; Braun, Ralf-Peter; Deng, Xiaohong; Tangdiongga, E.; Koonen, A.M.J.

doi:10.1109/glc.2019.8864126

Cited by 22 publications

(10 citation statements)

References 16 publications

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“…HARQ is a retransmissionbased protocol that retransmits only the lost data packets, reducing the number of retransmitted packets and the associated latency. Some applications such as 4K video streaming and 3D holographic require high data rates and others such as remote surgery or automated guidance for vehicles (AGV) need low latency [38]. With the growing number of user equipment requiring more bandwidth from WiFi APs and thus increasing the average latency and jitter, one of the possible improvements that would be cost-effective and solve the demand for high packet rate would be implementation of LiFi APs in hybrid software-controlled networks [39].…”

Section: Network Capacity and Qosmentioning

confidence: 99%

Evolution of Hybrid LiFi–WiFi Networks: A Survey

Besjedica

Fertalj

Lipovac

et al. 2023

Sensors

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Given the growing number of devices and their need for internet access, researchers are focusing on integrating various network technologies. Concerning indoor wireless services, a promising approach in this regard is to combine light fidelity (LiFi) and wireless fidelity (WiFi) technologies into a hybrid LiFi and WiFi network (HLWNet). Such a network benefits from LiFi’s distinct capability for high-speed data transmission and from the wide radio coverage offered by WiFi technologies. In this paper, we describe the framework for the HWLNet architecture, providing an overview of the handover methods used in HLWNets and presenting the basic architecture of hybrid LiFi/WiFi networks, optimization of cell deployment, relevant modulation schemes, illumination constraints, and backhaul device design. The survey also reviews the performance and recent achievements of HLWNets compared to legacy networks with an emphasis on signal to noise and interference ratio (SINR), spectral and power efficiency, and quality of service (QoS). In addition, user behaviour is discussed, considering interference in a LiFi channel is due to user movement, handover frequency, and load balancing. Furthermore, recent advances in indoor positioning and the security of hybrid networks are presented, and finally, directions of the hybrid network’s evolution in the foreseeable future are discussed.

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Section: Network Capacity and Qosmentioning

confidence: 99%

Evolution of Hybrid LiFi–WiFi Networks: A Survey

Besjedica

Fertalj

Lipovac

et al. 2023

Sensors

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“…3 shows the connections for the integration between untrusted non-3GPP access networks, especially WLAN or Wi-Fi networks. Jungnickel et al [26] proposed the integration of Li-Fi networks in the same way as Wi-Fi networks, i.e., through N3IWF, which illustrates the flexibility of the untrusted non-3GPP access. The authors also highlight the benefits of integration between non-3GPP access networks and the 5GCN, e.g., the handover support among different networks as an intrinsic feature.…”

Section: B Support For Non-3gpp Access Networkmentioning

confidence: 99%

A Tutorial on Trusted and Untrusted Non-3GPP Accesses in 5G Systems—First Steps Toward a Unified Communications Infrastructure

Lemes¹,

Alberti

Both

et al. 2022

IEEE Access

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“…Examples of the IoT applications under the class of smart industries are water industry [25], automatic interactions among machines [26], quality control, inventory tracking, logistics and supply chain, packaging optimization, and production on demand [27], [28]. Examples of the IoT applications under the category of smart environment are smart water quality sensing [29] - [35], smart agriculture [36] - [39], smart industrial plants, smart lighting [40] - [42], smart homes [43] - [46], and smart water supply [47] - [51]. Examples of the IoT applications under the category of smart health [52] - [55] include the monitoring of the organs or health conditions of a patient, remote surgery, diagnosing a patient's health condition(s), authentication of patients, and making real-time information about a patient's health condition available to the appropriate remote healthcare centers [56], [57].…”

Section: A Benefits Of Iot In Real-life Applicationsmentioning

confidence: 99%

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Olatinwo

Joubert

2022

IEEE Access

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In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in the internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Consequently, the resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of artificial techniques, such as optimization approaches and game theory approaches, for resource management are common while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.

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Enhance Lighting for the Internet of Things

Cited by 22 publications

References 16 publications

Evolution of Hybrid LiFi–WiFi Networks: A Survey

Evolution of Hybrid LiFi–WiFi Networks: A Survey

A Tutorial on Trusted and Untrusted Non-3GPP Accesses in 5G Systems—First Steps Toward a Unified Communications Infrastructure

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

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