A fog-enabled IoT platform for efficient management and data collection

Charalampidis, Pavlos; Τράγος, Ηλίας; Fragkiadakis, Alexandros

doi:10.1109/camad.2017.8031527

Cited by 19 publications

(13 citation statements)

References 8 publications

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“…Charalampidis et al introduced a fog-enabled IoT system utilized for sensory data collection. The experimental result shows some promise where the system reduces energy consumption [35]. In another study by Soultanopoulos et al, the authors presented a system implementation and IoT service architecture for a gateway service running on smart devices [38].…”

Section: B Rq2:what Are the Contributions Been Proposed By The Selecmentioning

confidence: 88%

Data Collection in Sensor-Cloud: A Systematic Literature Review

Ali¹

2020

Preprint

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Section: B Rq2:what Are the Contributions Been Proposed By The Selecmentioning

confidence: 88%

Data Collection in Sensor-Cloud: A Systematic Literature Review

Ali¹

2020

Preprint

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show abstract

“…Charalampidis et al introduced a fog-enabled IoT system utilized for sensory data collection. The experimental result shows some promise where the system reduces energy consumption [36]. In another study by Soultanopoulos et al, the authors presented a system implementation and IoT service architecture for a gateway service running on smart devices [39].…”

Section: B Rq2:what Are the Contributions Been Proposed By The Selecmentioning

confidence: 88%

Data Collection in Sensor-Cloud: A Systematic Literature Review

Ali¹

2020

Preprint

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The integration of cloud computing and Wireless Sensor Networks (WSNs) to create Sensorcloud helps in extending the data processing capability and storage capability of WSNs. Knowing how weak WSNs are with regards to communication ability, how to collect and upload sensory data to the cloud in limited time has become an issue in Sensor-cloud. In the last decade, with increasing interest by researchers in the domain, a considerable amount of research works have been conducted and published in the research domain. The main objective of this study is to systematically review the current research on data collection in Sensor-cloud. Hence, the study also aims at identifying, categorizing, and synthesizing important studies in the field of study. Accordingly, an evidence-based methodology is utilized in this study. By doing so, 43 relevant studies were identified and retrieved to answer the formulated research questions. The systematic methodology offers a methodical and rigorous study selection and evaluation process that is repeatable and precise. The result shows that research on data collection in Sensor-cloud is relatively consistent with stable output in the last five years. Ten proposal contributions were identified with System, Framework, and Algorithm being the most used by the selected studies. In conclusion, key research challenges and future research directions were identified and discussed for researchers to propose effective solutions to the existing challenges. Although research on data collection in Sensor-cloud is gaining some traction in recent years, the works in the domain are not sufficient and concrete proposals are needed to improve data collection.

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“…The author Jain [7], Gómez [8], Bhomble [9], Charalampidis [4], Afzal [3] and Ray [10] proposed a Smart Surveillance Monitoring System for motion detection and a remote camera for video recording.…”

Section: Iot Video Platformsmentioning

confidence: 99%

“…Standard formats and models are needed to support interoperability and ease cooperation among teams using different data formats and tools. Several video data analytics platforms like Wang [1], You [2], Afzal [3], Charalampidis [4] have been proposed addressing issues like scalability, privacy,..., etc. The evolution of these platforms, however, created isolated systems with limited interoperability.…”

Section: Introductionmentioning

confidence: 99%

A Scalable IoT Video Data Analytics for Smart Cities

Doan¹,

Tran²,

Huynh³

et al. 2019

EAI Endorsed Transactions on Context-aware Systems and Applicat

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The smart city is a comprehensive application of information resources and a high degree of information technology integration. With the technical support from IoT (Internet of things), smart city need to have three features of being instrumented, interconnected and intelligent. IoT provides the ability to manage, remotely monitor and control devices from massive streams of real-time data.Our model offers a scalable IoT video data analytics applications for Smart cities to end users, who can exploit scalability in both data storage and processing power to execute analysis on large or complex datasets. This model provides data analytics programming suites and environments in which developers and researchers can design scalable analytics services and applications. A cloud/edge-based automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number of camera devices and easy to integrate analytic application. The system automates the video analysis process and reduces manual intervention. The design of our model is developed to be easily extended for new kinds of IoT devices, message routing and queueing, and data analytics, to permit specific application to be programmed via the paradigm to be flexible yet simple.

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A fog-enabled IoT platform for efficient management and data collection

Cited by 19 publications

References 8 publications

Data Collection in Sensor-Cloud: A Systematic Literature Review

Data Collection in Sensor-Cloud: A Systematic Literature Review

Data Collection in Sensor-Cloud: A Systematic Literature Review

A Scalable IoT Video Data Analytics for Smart Cities

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