Developing io t applications in the fog a distributed dataflow approach

•

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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.

Technology

Developing IoT Applications
in the Fog: a Distributed
Dataflow Approach
Nam Giang (kyng@ece.ubc.ca), Michael Blackstock, Rodger
Lea, Victor Leung
University of British Columbia
IoT2015, Seoul Korea

Cloud-based IoT applications
a combination of
embedded web
services coordinated
by the cloud
HTTP
CoAP
MQTT
HTTP

Problems of Cloud-based IoT
Timeliness?
Redundancy?
Scalability?
Security?
HTTP
MQTT
CoAP
HTTP
Same room
300ms
500ms

Smart gateway-based IoT
HTTP
Same network
Local
execution
MQTT
HTTP

Fog Computing
Distributed
computation
System architecture
for the IoT
aka. In-network
Processing,
Cloudlets
Local
execution
Intermediate
execution
FOG

Fog-based IoT apps
Local
execution
Intermediate
execution
Four computational parts
1: gesture recog.
2: turn light on
3: if home, update Twitter
4: log activity

Challenges: Perception-Action Cycle
FOG
FOG
fog-to-fog
fog-to-cloud
cloud-to-fog
cloud-to-cloud

Challenges: Heterogeneity
FOG
Horizontal Heterogeneity
VerticalHeterogeneity
Resourceful
devices
Constrained
devices
Compute Nodes
IO Nodes
Edge Nodes

Challenges: Scalability
Largely
distributed
execution
environment
Pull deploym
ent

Challenges: Mobility
Mobility of the
executors
Backups of
executors?
Local
execution
Intermediate
execution
FOG

Distributed Flow-based (Dataflow)
Model
Sensor
Actuator
Web Service
Routines
Routines
Web Service
ActuatorSame Network
On Cloud
At Specific location
Same Device
- reusable nodes
- no centralized coord.
- simple IO model

Dataflow Programming Model
- Well defined execution units
inputs outputs
- Acyclic graphs
- Was created for concurrent
execution, later on was used
as a coordination language
- Customizable nodes library

FRED project https://fred.sensetecnic.com

Distributed Node-RED
Node-RED Cloud
Master Flow
...
Raspberry Pis
Master
Flow
Special node for
inter-devices
wiring

Lessons learned
● Flow-based model is generic enough to
accommodate the heterogeneity but still supports
domain-specific applications
● Node-level development is domain-specific
● Node-RED project is written in JavaScript - very
easy to develop new nodes

Open problems
● Inter-nodes communication is hard: define “same
room/building” communication?
● Distributed Service Discovery/Resource Directory
● Multiple flows per device
○ resource conflicts

Conclusion
- Fog computing is a promising architectural
model for the IoT
- New challenges when developing IoT
applications in the Fog
- Distributed Dataflow model a promising
approach to supporting Fog-based IoT
applications
Thank you

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