Proximity IoT solution with Aruba ALE / Microsoft Azure 2017-05-09 Fabien GIRAUD
Proximity IoT solution with Aruba ALE / Microsoft Azure
2017-05-09 Fabien GIRAUD
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Problem statement
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– Hard to sell added value of ALE
– Lot of small ecosystem partners
– Ecosystem only focusing on few verticals / use models
– No use model for IoT in combination with ALE
– Provide more added value on Aruba Mobile first infrastructure
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Analytics and Location Engine (ALE) overview
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Network Analytics with ALE: Functional Overview
Unified Context
for each user
(Username, IP,
MAC, device
type, Apps)
1Real-time
location
and
Context
decoding
engine
2
ALE
Seamless, secure
connectivity to
analytics platforms
4
Northbound
APIs (publish/
subscribe,
polling)
3
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Analytics with ALE
Contextual Data:User, Device, Application &
Location
ENGAGEMENTLocation / User Specific
Experiences
DATA
MINING /
ANALYTICS
Sensors
Other
Data
Sources
CRM
Venue Traffic
Patterns, A/B
Testing,
Demographic
Analysis, etc.
ALE
MARKETING, AD
PLATFORMS
Context
APIs
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ALE Northbound APIs
Two types of Northbound APIs:
– Publish/Subscribe
– Uses Google Protocol Buffering (“Protobuf”) for encoding and TCP based ØMQ transport
– External Analytics engines can subscribe to various “topics”:
– Location
– Presence
– Applications, Destination URLs
– Campus, building, floor, etc.
– Polling Based: REST API
– Supports standard REST queries for various events/objects
– Example: http://<ip>/api/v1/station will return a list of all stations
– Return data format is JSON
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ALE Modes of Operation (2.0)
• Proximity (Context with no maps or location)
• Rough estimate of location – which AP is closest to the client
• No maps required
• Context with Mapped Locations:
• Estimated:
• Uses floor plans / AP placement info from Airwave
• Uses AP-AP RSSI messages to build a path loss model to create a pseudo Positioning Database (PDB)
• Fingerprinting Mode:
• Uses calibration data from fingerprinting
• New Android App for fingerprinting (NAO Logger) and a web service on ALE (NAO Campus)
• Pattern matching utilizing customer generated PDB is used for location calculation
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Viewing Client Locations and ALE Dashboard
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Demo time !
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Existing Partners Ecosystem
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Key 3rd Party Location Analytics Partners http://www.arubanetworks.com/partners/ecosystem/mobile-applications/
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Software AG – ALE integration
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Software AG – ALE integration (con’t)
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Rifiniti – ALE integration
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Rifiniti – ALE integration (con’t)
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Aislelabs – ALE integration
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SkyFii - ALE Integration
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Ivizone – ALE integration
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Demo time again
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ALE – Azure integration
Add value and scalability to ALE with Azure Power
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Problem statement (reminder)
+
– Hard to sell added value of ALE
– Lot of small ecosystem partners
– Ecosystem only focusing on few verticals / use models
– No use model for IoT in combination with ALE
– Provide more added value on Aruba Mobile first infrastructure
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Steps
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IoTworkshop
Microsoft & Aruba
Hackathon
Solution demo
Pilot @Discover
2016
Pilot @CeBit
2017
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Solution components
Microsoft Azure
+
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Initial use case
– Simple initial solution
– Dwell time
– Visitor counter
– Devices, users, etc
– More complex solution
– User detection
– Visitor counter for a given proximity
– Push notifications based on proximity
– You are at the men’s clothing department
– You are in the Store
– You are in the hall of medieval arts in a museum
+
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Ubuntu
(Python script)
IoT Hub Stream Analytics
Servicebus
Queue
SQL
Notification
Hub
Azure
Function
Aruba ALE
Power BI
Solution Architecture
Clearpass
+
Macvendors.co
API
On-Site WWW Azure Cloud Cloud or local
Websocket tunnel
NoSQL
(DocumentDB)
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Python script flow
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View data in Power BI (geofence)
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View data in Power BI (Location)
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View data in Power BI (CeBit)
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Azure bricks
Python script VM
iOT Hub
SQL Database
App notification
Stream analyser
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Last but not least demo time
Thank [email protected]