1. A fast way to deploy IoT application based on EdgeX Foundry
Ximing Xu

2. We manage smart devices and machine data
Smart devices management
ThunderUEM
Access and Identity Management
ThunderIAM
RealTime Data
InstantD
Products
Solutions
OneIoT
Manage kinds of IoT devices used in industrial scenario, and realtime data generated by these devices.
OneSpace
Provide a controllable digital working space across PC and mobile environment. +

3. Strategic investors AirWatch
A global leader in cloud infrastructure and business mobility, helps customers accelerate their digital transformation.
With 2016 revenue of $7.09 billion, VMware is headquartered in Palo Alto, CA and has over 500,000 customers and 75,000 partners worldwide.
Airwatch of VMware is the clear leader for EMM globally recognized by industry analytics and key customers.
AirWatch
Comprehensive Portfolio
Innovate Faster
Market Leader
Built to Scale
Open Ecosystem
Security
World leading smart device operating system and platform technology provider
Partnering with Qualcomm, Intel, ARM, Microsoft and have joint ventures with Qualcomm, ARM, VMware etc.
Core competencies include protocol stack, deep learning, computer graphics, OS optimization, etc on Android, Linux, Windows and HTML5

4. The challenges for industry IoT application
Too many I/O types and communication protocols.
- IO types:
RS485, RS232, CAN, Ethernet, Zigbee, LoRa, GPRS, NB-IoT ……
- Protocols:
Modbus RTU, Modbus TCP, OPC-UA, TCP/IP, Http, MQTT, CoAP, PPI, Profibus, CANopen, KNX  ……
Different data processing requirements for different devices.
Difficult to deploy basing on existing industry infrastructure.
IoT Maintenance costs are extremely huge.

5. Existing solutions for the challenges IoT
Smart device instruments
Smart gateway
SCADA system 5

6. Why EdgeX Foundry will be new trend in industry IoT?
Easy-using:
focus on application itself
plenty of components
Flexible:
assemble micro-services at will.
services loosely coupled.
support both single server deployment and complex fog env.
Easy-using
Flexible
Free:
totally open source
Independent:
run on multiple platforms.
support multiple OS.
support multiple programming languages.
Free
Independent

7. We will contribute instantEdge to EdgeX Foundry
Our goal is to make it fast to deploy IoT application.
Stable and scalable streaming data processing runtime.
Rich computing nodes including data source，processor, sink, predict model, external data.
UI editor to assemble data processing flow.
Online monitor of data processing and data visualization. 7 7

8. InstantEdge architecture overview
External Source
Stream Processor
Export Target
Service Layer
Node Layer
Source
ZoreMQ Consumers
Kafka Consumers
TCP channels
Https services
DB connectors
Service callers
Schedule service
Message queue
Process
Sink
Message queue
Filter
Compare
Function
Http Sender
EdgeX gateway
EdgeX gateway
biz system
Window
cache
Sort
MQTT publisher
Devices
external data
Discard
Merge
DB sinker
Archived data
Data getter
Alert
Map
Align
Msg sender
Predict
Job Editor
IOT platform
Device data
Topology Generator
Stream Engine
JS executor
Window caching
Data aggregator
Caching service
sending
Mlib ……
Http pusher
MQTT sender
DB updater
Msg sender
External DB

9. Embedded with EdgeX Foundry
Flow-based GUI:
Flow Edit UI is the main interface for users to create and edit computing flows in a visual way
Deploy Engine is responsible for deploying certain flows to a designated “edge”
Stream Monitor collects state information from running streams and displays to users
Flow runtime:
A “Runtime” is the core for scheduling in-memory computing jobs
Flow runtime will register itself as an export client and receive events from Distribution service.
Flow runtime will process each events according to computing nodes。
Compute node types include filter, transform, function, split, window, join, sort, notify, export, external data……
Flow Edit UI
Deploy Engine
Stream Monitor
GUI
Runtime
Export Services
Distribution
Registry
Support Services
Flow runtime
logging
Core Services
Core Data
Command
Meta Data
Data Services
Modbus
SNMP ……

10. Example 1:real-time computing and monitoring
EdgeX Gateway
temperature sensor1
msg router
add prosperities:
msg.device_sn,
msg.device_key…
msg.temp=
msg.t/10
group by device_sn:
cache latest status
Data Source
map
temp_sensor_profile1
humidity_sensor_profile2
function
msg.hum=
msg.h/10
cache
cache historian data in one day
latest,max，ave，min…
aggregate
msg.temp>40.0
trigger
msg.hum<10
,sms
alert
send to cloud
sink
humidity sensor1
temperature sensor2
humidity sensor2

11. Example 2: daily oil yields by each pump unit
EdgeX Gateway
pump1
pump2
pump3
pump4
msg.device_id=1 ->msg.device_sn=‘pumpA’
msg.device_id=2 ->msg.device_sn=‘pumpB’
msg.device_id=3 ->msg.device_sn=‘pumpC’
msg.device_id=4 ->msg.device_sn=‘pumpD’ ……
map
function
group by device_sn:
cache historian data in one day
cache
msg.daily_yield
=sum(vol*oil_percent)/
sum(delt_time)*1440
send to cloud
sink
Data Source

12. Example 3: prediction of defective rate
EdgeX Gateway
conveyer belt
front temperature sensor
msg.device_id=1 ->msg.device_sn=‘machine1’
msg.device_id=2 ->msg.device_sn=‘machine1’
msg.device_id=3 ->msg.device_sn=‘machine1’
Data Source
map
group by device_sn
latest status:
speed,temp1,temp2
cache
rear temperature sensor
predictive model
msg.defective_rate=
func(speed,temp1,temp2)
defective_rate>50%?
trigger
,sms
alert
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