NoCloud: Exploring Network Disconnection through On-Device Data Analysis

Rawassizadeh, Reza; Pierson, Timothy J.; Peterson, Ronald; Kotz, David

doi:10.1109/mprv.2018.011591063

Cited by 35 publications

(34 citation statements)

References 20 publications

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“…When in continuous use, battery life for most hearables is typically not longer than 5 h [60], thus limiting their ability to be worn all day long. Similar battery life issues have been overcome in the design of smart wristwatches [61]. If hearables are to properly blend into the background of everyday life, advances in battery technology, wireless charging [62] wireless protocols, or self-charging devices [63] will be required.…”

Section: Battery Lifementioning

confidence: 97%

“…In those cases where information is processed locally (e.g., the Bragi Dash has an on-board mp3 player), battery life tends to be improved. However, with the proliferation of on-device machine learning chips and machine learning algorithm proxies [61,64], it may soon be possible to improve both the battery life and computational capabilities of ear-mounted wearable computing devices.…”

Section: Computational Capacitymentioning

confidence: 99%

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A Survey on the Affordances of “Hearables”

Plazak

Kersten-Oertel

2018

Inventions

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Recent developments pertaining to ear-mounted wearable computer interfaces (i.e., "hearables") offer a number of distinct affordances over other wearable devices in ambient and ubiquitous computing systems. This paper provides a survey of hearables and the possibilities that they offer as computer interfaces. Thereafter, these affordances are examined with respect to other wearable interfaces. Finally, several historical trends are noted within this domain, and multiple paths for future development are offered.

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Section: Battery Lifementioning

confidence: 97%

Section: Computational Capacitymentioning

confidence: 99%

A Survey on the Affordances of “Hearables”

Plazak

Kersten-Oertel

2018

Inventions

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“…Therefore, analyzing data on a remote host such as the cloud or cloudlet will be a trend in future research. However, the network response time will increase and privacy-related issues should also be taken into account [19]. In recent years, cloud-dew architecture was proposed to solve the problem of all resources existing far from users' premises and far from users' control [20].…”

Section: Further Workmentioning

confidence: 99%

A System of Mining Semantic Trajectory Patterns from GPS Data of Real Users

2019

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Positioning devices allow users’ movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation—they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users’ real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns.

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“…In this context, if network securities are assumed to have minimal effect on dew computing, localized privacy and authentication challenges will trouble dew computing to settle down. Lightweight security and privacy means will be an essential part of dew devices as they are prone to restricted resources . For example, shortage of battery power, biometric authentication, and private files may be intruded by external entity or dew users at any point of time.…”

Section: Challengesmentioning

confidence: 99%

“…Lightweight security and privacy means will be an essential part of dew devices as they are prone to restricted resources. 19 For example, shortage of battery power, biometric authentication, and private files may be intruded by external entity or dew users at any point of time. On other hand, diverse network configurations and complex data synchronization will make security and privacy issues vulnerable ones.…”

Section: Lightweight Security and Privacymentioning

confidence: 99%

Minimizing dependency on internetwork: Is dew computing a solution?

Ray

2018

Trans Emerging Tel Tech

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Since the inception of the internetwork facility, it has been continuously serving as the fundamental backbone of existing aspects, such as cloud, fog, and edge computing. Several application perspectives are in practice that mandate the involvement of application domains including smart retail, transportation, and data analytics. However, the deployed service platforms do not conform to facilitate the minimal and/or internetwork‐free orientation to current paradigms. Hence, to cater this approach, a dew computing paradigm that goals at bringing cloud, fog, or edge resources and services to the nearest of user's periphery without or minimal use of internetwork has been introduced. This article advocates the feasibility, applicability, and appropriation of dew computing while leveraging several real‐life case studies with special attention to internetwork‐free movement. From earlier published literary works, a taxonomy has been devised in the dominion of dew computing. Moreover, highlights are made on selective requirements that would be key to the advancement of dew computing. Finally, challenges are discussed to provide future research directions in the dew computing domain.

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NoCloud: Exploring Network Disconnection through On-Device Data Analysis

Cited by 35 publications

References 20 publications

A Survey on the Affordances of “Hearables”

A Survey on the Affordances of “Hearables”

A System of Mining Semantic Trajectory Patterns from GPS Data of Real Users

Minimizing dependency on internetwork: Is dew computing a solution?

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