Smart City Based Mobile Application for Seamless Communication of Differently-Abled

T, Poovam Raj T.; Udhayakumar, S.; J, Silviya Nancy

doi:10.14257/ijmue.2017.12.1.15

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Direct offloading uses some of the major public cloud resources like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform for complex task execution. Many researchers have explored various possibilities of mobile cloud applications with the help of these public cloud infrastructures in the field of mobile healthcare, crowdsourcing and gaming [3], [4]. Though direct offloading is the best case for mobile cloud, the latency, network delay, and intermittent connection to the mobile internet make this form of offloading a distant dream for real-time applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

Udhayakumar

Tamilselvan

et al. 2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Computing with mobile is still in its infancy due to its limitations of computational power, battery lifetime and storage capacity. These limitations hinder the growth of mobile computing, which in-turn affects the growth of computationally intensive applications developed for the mobile devices. So in-order to help execute complex applications within the mobile device, mobile cloud computing (MCC) emerged as a feasible solution. The job of offloading the task to the cloud data center for storage and execution from the mobile seems to gain popularity, however, issues related to network bandwidth, loss of mobile data connectivity, and connection setup does not augment well to extend the benefits offered by MCC. Cloudlet servers filled this gab by assisting the mobile cloud environment as an edge device, offering compute power to the connected devices with high speed wireless LAN connectivity. Implementation constraints of cloudlet faces severe challenges in-terms of its storage, network sharing, and VM provisioning. Moreover, the number of connected devices of the cloudlet and its load conditions vary drastically leading to unexpected bottleneck, in which case the availability to server becomes an issue. Therefore, a scalable cloudlet, Client Aware Scalable Cloudlet (CASC) is proposed with linear regression analysis, predicting the knowledge of expected load conditions for provisioning new virtual machines and to perform resource migration.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

Udhayakumar

Tamilselvan

et al. 2020

Int. J. Interact. Mob. Technol.

Self Cite

View full text Add to dashboard Cite

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Context Aware Self Learning Voice Assistant for Smart Navigation with Contextual LSTM

Nancy

Udhayakumar²,

Pavithra³

et al. 2019

Communications in Computer and Information Science

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Sensible Autonomous Machine Using Deep Learning and Convolutional Neural Networks

Diviya¹,

Raghav²,

Parthiban³

et al. 2020

Advances in Intelligent Systems and Computing

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Smart City Based Mobile Application for Seamless Communication of Differently-Abled

Abstract: The proposed research based mobile-application development is to aid the needs of the differently-abled (deaf and dumb)

Cited by 3 publications

References 3 publications

Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

Context Aware Self Learning Voice Assistant for Smart Navigation with Contextual LSTM

Sensible Autonomous Machine Using Deep Learning and Convolutional Neural Networks

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