Sensing multimedia contexts on mobile devices

Hoque, Mohammad A.; Rao, Ashwin; Kumar, Abhishek; Ammar, Mostafa; Hui, Pan; Tarkoma, Sasu

doi:10.1145/3386290.3396935

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Traffic control in core networks can be handled using QoE metrics through queuing policies, software-defined networks, or network slicing. [30][31][32][33][34][35][36][37] Additionally, the metaverse will take into consideration the impact of human activities, such as mobility and handover, on network communication.…”

Section: Data Transmission and Qualitymentioning

confidence: 99%

The realm of metaverse: A survey

Venugopal

Subramanian

Peatchimuthu

2023

Computer Animation & Virtual

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The next step of digital development is the metaverse, which has the potential to drastically alter how people use technology and expand the range of services available beyond conventional systems that can be accessed online. As the efficiency, performance, and quality of service access reach their peak levels, the focus has shifted to the user experience. Due to this, there is an increasing demand for more involved and thorough customer service, and service providers are willing to increase their present standards. Consumers are genuinely asking for tactile and immersive elements in their digital interfaces, but these features can only be made possible by the metaverse's potentially futuristic subfields of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR). However, the metaverse may not be widely used due to significant security and privacy issues either from underlying technology or produced by the new digital environment. A variety of fundamental problems, such as scalability and interoperability, can arise in terms of ensuring security for the metaverse because of the metaverse's inherent properties, such as immersive realism, sustainability, and heterogeneity. In this survey, we propose a hypothetical meta‐stack framework to understand the various components in the realm of metaverse and then provide wide‐ranging insights on the most recent development in metaverse realm in the context of cutting‐edge technologies, security vulnerabilities and preventive measures specific to the metaverse and the research challenges pertaining to metaverse.

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Section: Data Transmission and Qualitymentioning

confidence: 99%

The realm of metaverse: A survey

Venugopal

Subramanian

Peatchimuthu

2023

Computer Animation & Virtual

View full text Add to dashboard Cite

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“…While ubiquitous devices enable a diverse set of multimedia activities [3], users generally have no control over their resulting traffic after such traffic leaves their device. Fortunately, a new set of personalized or user-centric networking applications are emerging, such as Personal Virtual Network (PVN) [4] and Middle Box Zero (MBZ) [5].…”

Section: Introductionmentioning

confidence: 99%

Context-driven encrypted multimedia traffic classification on mobile devices

Hoque

Finley

Rao

et al. 2023

Pervasive and Mobile Computing

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Context-driven Encrypted Multimedia Traffic Classification on Mobile Devices

Hoque¹,

Finley²,

Rao³

et al. 2022

2022 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device's detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.

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Sensing multimedia contexts on mobile devices

Cited by 4 publications

References 14 publications

The realm of metaverse: A survey

The realm of metaverse: A survey

Context-driven encrypted multimedia traffic classification on mobile devices

Context-driven Encrypted Multimedia Traffic Classification on Mobile Devices

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