Fit: A Fog Computing Device for Speech Tele-Treatments

Monteiro, Admir; Dubey, Harishchandra; Mahler, Leslie; Yang, Qing; Mankodiya, Kunal

doi:10.1109/smartcomp.2016.7501692

Cited by 76 publications

(48 citation statements)

References 9 publications

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“…A new Fog computing interface (FIT) [74] is created for Android smart-watches connected with a smart tablet that collects, records and processes speech data from patients with Parkinson's disease. Instead of transmitting the entire audio data, FIT extracts features like volume, short-time energy, zero-crossing rate and spectral centroid from speech and sends to the Cloud for long-term analysis.…”

Section: Collection and Pre-processing Of Speech Datamentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

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Section: Collection and Pre-processing Of Speech Datamentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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“…In this solution, Fog computing is used as a middleware to perform realtime data processing, feature extraction and classification to feed machine learning and data caching mechanisms. A Fog-driven IoT interface, named as FIT is proposed in by Monteiro et al [39]. The purpose of their solution is to enable the communication between IoT devices (e.g.…”

Section: Healthcarementioning

confidence: 99%

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Velásquez

Abreu

Assis

et al. 2018

J Internet Serv Appl

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Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people's daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

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“…Since visualization is an important aid to find out insights of your data, we used Tableau to visualize our data and its results [7]. Since we are dealing with big data, we connected tableau with Hive to take advantage of working in distributed environment [26,27,28]. CONCLUSION This model provides easy and efficient way to find faculties and their research domains in India.…”

Section: Hybrid Model For Faculty Retrievalmentioning

confidence: 99%

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

Gupta¹,

Lenka²,

Barik³

et al. 2017

Preprint

Self Cite

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Abstract:In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.

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Fit: A Fog Computing Device for Speech Tele-Treatments

Cited by 76 publications

References 9 publications

Fog computing security: a review of current applications and security solutions

Fog computing security: a review of current applications and security solutions

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

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