Multipath Computation Offloading for Mobile Augmented Reality

Braud, Tristan; Zhou, Pengyuan; Kangasharju, Jussi; Hui, Pan

doi:10.1109/percom45495.2020.9127360

Cited by 47 publications

(39 citation statements)

References 32 publications

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“…The links must have sufficient bandwidth to accommodate the data rate required by the applications. A study on offloading via WiFi and LTE to several potential offloading devices [225] reveals that the latency of LTE is noticeable over a strong WiFi connection. A noticeable latency is not a suitable condition for the proper functionality of MAR.…”

Section: ) Limitations/requirements In Marmentioning

confidence: 99%

“…2) How 5G Helps A possible solution proposed to minimize the latency in offloading is the exploitation of multiple available links and offload via multiple links simultaneously using a scheduling algorithm [225]. Multi-connectivity in 5G mmWave cellular networks [226] and coordinated multi-point transmission [227] are proposed solutions to increase the robustness, performance and service quality of the link.…”

Section: ) Limitations/requirements In Marmentioning

confidence: 99%

“…• How the offloading process will dynamically adapt based on channel conditions so that the strict application requirements are guaranteed? 3) Preliminary Solutions Research work on multipath offloading for MAR [225] discusses a multi-server, multiple-links architecture for offloading to companion devices, edge and cloud, and proposes an algorithm for task allocation. Latency aware hybrid edge and cloud offloading [250] makes use of proximity of MEC in 5G to offload AR processing to either edge or cloud intelligently.…”

Section: Service Offloading 1) Lessons Learnedmentioning

confidence: 99%

“…With the drastic changes proposed with 5G (i.e., mmWaves, small cells, massive MIMO, beamforming), more sophisticated and evolved network architectures will emerge with more resources available close to the user. Offloading algorithms [225] can consider these features and can be improved to enable dynamic and more sophisticated offloading scenarios suitable for MAR applications. The adaptive multipath offloading subjected to packet loss constraint [256] can be applied in MEC enabled 5G UDN based MAR communications, as the users are in the vicinity of multiple base stations.…”

Section: ) Future Directivesmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Siriwardhana

Porambage

Liyanage

et al. 2021

IEEE Commun. Surv. Tutorials

453

108

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The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users to run AR applications on mobile devices with greater mobility and at a lower cost. The emerging 5G communication technologies act as critical enablers for future MAR applications to achieve ultra-low latency and extremely high data rates while Multi-access Edge Computing (MEC) brings enhanced computational power closer to the users to complement MAR. This paper extensively discusses the landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC. The paper especially provides an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options. The paper discusses key application areas for MAR and their future with the advent of 5G technologies. The paper also discusses the requirements and limitations of MAR technical aspects such as communication, mobility management, energy management, service offloading and migration, security, and privacy and analyzes the role of 5G technologies.

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Section: ) Limitations/requirements In Marmentioning

confidence: 99%

Section: ) Limitations/requirements In Marmentioning

confidence: 99%

Section: Service Offloading 1) Lessons Learnedmentioning

confidence: 99%

Section: ) Future Directivesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Siriwardhana

Porambage

Liyanage

et al. 2021

IEEE Commun. Surv. Tutorials

453

108

View full text Add to dashboard Cite

show abstract

“…Since its inception 30 years ago (Mattern 2001;Weiser 1991), ubiquitous and pervasive computing has changed fundamentally how we perceive computers and interact with them. The ability of a pervasive system to detect the situation of its users in the physical world (Clemente et al 2020;Sadhu et al 2019) and to adapt itself to it (Braud et al 2020;Breitbach et al 2019) are central building blocks of such systems. Besides context management (Elmalaki et al 2015) and runtime adaptation frameworks (Becker et al 2003(Becker et al , 2004Cervantes et al 2017;Handte et al 2012;Herdin et al 2017), artificial intelligence (AI) and machine learning (ML) algorithms have proven to be of major importance to realize this vision.…”

Section: Introductionmentioning

confidence: 99%

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Cichiwskyj

Schmeißer

Qian

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

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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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Multipath Computation Offloading for Mobile Augmented Reality

Abstract: If it is the author's pre-published version, changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published version.

Cited by 47 publications

References 32 publications

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Elastic AI: system support for adaptive machine learning in pervasive computing systems

The realm of metaverse: A survey

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