English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data-intensive applications are growing at an increasing rate and there is agrowing need to solve scalability and high-performance issues in them. By theadvent of Cloud computing paradigm, it became possible to harness remoteresources to build and deploy these applications. In recent years, new set ofapplications and services based on Internet of Things (IoT) paradigm, requireto process large amount of data in very less time. Among them surveillance andobject detection have gained prime importance, but cloud is unable to bringdown the network latencies to meet the response time requirements. This problemis solved by Fog computing which harnesses resources in the edge of the networkalong with remote cloud resources as required. However, there is still a lackof frameworks that are successfully able to integrate sophisticated softwareand applications, especially deep learning, with fog and cloud computingenvironments. In this work, we propose a framework to deploy deeplearning-based applications in fog-cloud environments to harness edge and cloudresources to provide better service quality for such applications. Our proposedframework, called EdgeLens, adapts to the application or user requirements toprovide high accuracy or low latency modes of services. We also tested theperformance of the software in terms of accuracy, response time, jitter,network bandwidth and power consumption and show how EdgeLens adapts todifferent service requirements.

EdgeLens: Deep Learning based Object Detection in Integrated IoT, Fog and Cloud Computing Environments