A Survey on Mobile Data Offloading Technologies

Zhou, Huan; Wang, Hui; Li, Xiuhua; Leung, Victor C. M.

doi:10.1109/access.2018.2799546

Cited by 159 publications

(68 citation statements)

References 56 publications

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“…For instance, IoT equipment deployed at a remote experimental site collecting time-insensitive data that can be stored for some period before being transferred to a distant laboratory for analysis and processing. Mobile data offloading enables an alternate transportation method for time-insensitive content during peak times, e.g., when the cellular network is overloaded or during outage, or from the places where it is infeasible to deploy cellular network infrastructure [4]. In such cases, a cellular provider may decide to send delay-tolerant IoT data only to a small subset of mobile nodes, and let these nodes spread the information through peer to peer (P2P) communications during opportunistic contacts [5].…”

Section: Introductionmentioning

confidence: 99%

Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

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Section: Introductionmentioning

confidence: 99%

Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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“…A comunicação D2D move o tráfego de dados das redes celulares para outras interfaces de comunicação, processo conhecido como mobile data offloading [Zhou et al 2018]. Redes D2D podem ser inband quando utilizam o espectro licenciado para conectar os dispositivos, ou outband, quando utilizam as outras faixas de uso não licenciado do espectro (por exemplo, empregando tecnologias como o Wi-Fi, Zigbee 1 e Bluetooth 2 ).…”

Section: Introductionunclassified

QD4G: QoE para Vídeo em Redes D2D/4G com Aprendizado de Máquina

Carvalho

Silva

et al. 2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

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A comunicação Dispositivo-a-Dispositivo (D2D) é um dos mecanismos para a desoneração das redes 4G, via offloading de dados para outras redes. Redes híbridas D2D/4G possuem como desafio a multiplexação eficiente das interfaces de comunicação, o que pode impactar diretamente na Qualidade de Experiência (QoE) dos usuários. Este trabalho apresenta o QD4G, uma plataforma que emprega Aprendizado de Máquina para controlar quando clientes usando o padrão DASH (Dynamic Adaptative Streaming over HTTP) devem empregar a rede 4G ou uma rede D2D. O objetivo é melhorar o QoE ao proporcionar a transmissão de vídeo em uma maior resolução. Testes foram realizados com dispositivos reais e os resultados mostraram que a predição permite atingir até 87% de acerto, com um aumento na resolução média do vídeo entre os usuários via D2D de até 150%, ao mesmo tempo desonerando o tráfego de dados da rede 4G em até 80% nos cenários avaliados.

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“…Coverage probability versus SINR threshold for the typical user for different altitudes. 5.3 Average rate versus altitude of aerial-BSs for the typical user for different densities conferencing, are extensively used [1]. Furthermore, new applications and use cases are to be introduced in the 5G wireless networks and beyond (5G+).…”

Section: 2mentioning

confidence: 99%

“…5. 1. We assume that the where z denotes the Euclidean horizontal distance between the typical user and the projection of the aerial-BS location on the horizontal plane.…”

Section: System Modelmentioning

confidence: 99%

“…The connectivity between small cell BSs or aggregation points and the NFPs is also based on PtP FSO beams. Each small cell BS with a clear LoS link with a NFP delivers/receives its uplink/downlink traffic to/from the intended NFP via FSO link1 . On the other hand, small cell BSs with no LoS with a NFP can be served by a nearby small cell BS which has a clear LoS with a NFP.…”

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confidence: 99%

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Integrated Aerial and Terrestrial Wireless Access Architecture for Beyond-5G Networks

Amroune¹

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With an ever increasing demand for ubiquitous and seamless wireless services, cellular operators are looking for novel network designs to meet such demands. Providing wireless services from the sky, using aerial networks (aerial platforms), has recently gained significant attention. Aerial platforms of various types including unmanned aerial vehicles (UAVs, also referred to as drones), balloons, and highaltitude/medium-altitude/low-altitude platforms (HAPs/MAPs/LAPs) can play a key role in future wireless networks. For example, a base station (BS) mounted on an aerial platform can boost capacity and/or enhance coverage. This thesis aims to address the challenges accompanied with the use of aerial platforms in wireless communication networks. Specifically, we propose novel frameworks that address various issues related to backhauling/fronthauling a dense deployment of small cells and the recently-envisioned concept of aerial-BSs. This thesis starts with proposing a novel backhaul/fronthaul network capable of transporting the backhaul/fronthaul traffic between the terrestrial-BSs and the core network. In the proposed network, the aerial platforms act as aerial-hubs that collect/deliver traffic from/to small cells via free-space optics (FSO) links. We show the main limitations of the proposed network and identify proper ways to tackle these limitations. Aerial platforms can also act as aerial-BSs; therefore, we address the energyefficient aerial-BS placement problem. The aim is to find the 3D location of the aerial-BS that maximizes the number of covered users using the minimum transmit power. We decouple the aerial-BS deployments in the horizontal and vertical dimensions without any loss of optimality. Next, we investigate the 3D aerial-BS placement that maximizes the number of covered users with different quality-of-service (QoS) requirements. This 3D placement problem is modeled as a multiple circles placement problem. We propose an optimal placement algorithm that utilizes an exhaustive I would like to express my sincere gratitude to my supervisor, Prof. Halim Yanikomeroglu, for the continuous support of my Ph.D study and research, for his patience, motivation, and immense knowledge. I could not have imagined having a better supervisor for my Ph.D study. I would also like to express my deep gratitude to Dr. Muhammad Zeeshan Shakir and Dr. Amr El-Keyi for their valuable discussions, comments, and feedback. I am deeply grateful to my parents, Faraj and Sabria, for believing in me and giving me the courage to choose what I desire. I salute you all for your prayers, love, care, and sacrifice. I am extremely thankful to my wife, Aisha for her patience, generous support, and love. A special gratitude goes to my children, Hajar, Maria and Sarah for their smile and love. vi 7 Conclusion and Future Work

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A Survey on Mobile Data Offloading Technologies

Cited by 159 publications

References 56 publications

Data offloading in IoT environments: modeling, analysis, and verification

Data offloading in IoT environments: modeling, analysis, and verification

QD4G: QoE para Vídeo em Redes D2D/4G com Aprendizado de Máquina

Integrated Aerial and Terrestrial Wireless Access Architecture for Beyond-5G Networks

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