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Cited by 293IoT and Edge/Fog ComputingArduino and IoT ApplicationsInternet of Things and AI

Developing iot applications in the fog: a distributed dataflow approach

Open access
Giang, Nam Ky, Blackstock, Michael, Lea, Rodger, Leung, Victor C.M.·2015·2015 5th International Conference on the Internet of Things (IOT)
DOI 10.1109/iot.2015.7356560
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Abstract

In this paper we examine the development of IoT applications from the perspective of the Fog Computing paradigm, where computing infrastructure at the network edge in devices and gateways is leverage for efficiency and timeliness. Due to the intrinsic nature of the IoT: heterogeneous devices/resources, a tightly coupled perception-action cycle and widely distributed devices and processing, application development in the Fog can be challenging. To address these challenges, we propose a Distributed Dataflow (DDF) programming model for the IoT that utilises computing infrastructures across the Fog and the Cloud. We evaluate our proposal by implementing a DDF framework based on Node-RED (Distributed Node-RED or D-NR), a visual programming tool that uses a flow-based model for building IoT applications. Via demonstrations, we show that our approach eases the development process and can be used to build a variety of IoT applications that work efficiently in the Fog.

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  • Fog computing architecture is well situated to process machine-to-machine data streams, for example from connected vehicles.

    “Data handling is an obvious concern in the Big Data era; FC architecture is well situated to process machine-to-machine data streams with, for example, connected vehicles [172,173].”

    From Edge AI: A survey (2023) · cites this paper as [173]

    The Introduction says fog computing moves processing closer to IoT devices and that local processing of sensor data can reduce costs and improve responsiveness. This supports the general point about processing IoT data, but our copy does not identify connected vehicles as an example or specifically discuss machine-to-machine data streams. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 27

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Cited by

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Authors
Giang, N. K., Blackstock, M., Lea, R., & Leung, V. C. M.
Affiliations
University of British Columbia (CA)
ORCID
Venue
2015 5th International Conference on the Internet of Things (IOT)
Publisher
IEEE
Published
2015-10
Pages
155-162
DOI
10.1109/iot.2015.7356560
OpenAlex
W2211262776
Cited by
293
References
0
Citations by year
2026: 4 · 2025: 5 · 2024: 10 · 2023: 16 · 2022: 38 · 2021: 43 · 2020: 39 · 2019: 53 · 2018: 44 · 2017: 30 · 2016: 10 · 2015: 1
FWCI
21.10
Type
conference paper
Language
en
Open access
Locations
  • International Conference on the Internet of Things (IoT) (publishedVersion)
  • Lancaster EPrints (Lancaster University) (submittedVersion)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
Internet of Things, fog computing, Node-RED, visual programming, flow-based programming, edge computing, IoT application development, heterogeneous devices

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