The document discusses developing IoT applications using fog computing, addressing challenges like timeliness, scalability, and heterogeneity. It highlights a distributed dataflow approach for executing tasks locally and efficiently while accommodating various device constraints. The conclusion asserts that fog computing presents new opportunities and challenges in IoT application development.

1. 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

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

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

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

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

6. 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

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

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

9. Challenges: Scalability
Largely
distributed
execution
environment
Pull deploym
ent

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

11. 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

12. 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

13. Node-RED project http://nodered.org

14. FRED project https://fred.sensetecnic.com

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

16. Experience with DNR
TI SensorTag

17. 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

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

19. 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