Łukasz Romaszewski on Internet of Things using Raspberry Pi and Java Embedded. Presentation given on JavaCamp #13, for Polish JUG (@polishjug).

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Home Security System
Java Embedded in Action
Łukasz Romaszewski
ISV Migration Consultant
Oracle
November 22, 2014

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Java in the Internet of Things
One Language, One Platform, Runs Everywhere
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Enterprise Data & Applications
Cloud for Embedded Devices
Meters
Smart Appliances & electronics
Personal Devices
Med-Large Embedded / Multi- function Devices
VoIP Comm
Industrial controls / Network Appliances
Management / Monitoring / Operations
Sensors / Microcontrollers
Connected Vehicles

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The Spectrum of Java Embedded
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Small Embedded
Medium Embedded
Large Embedded
Platform Footprint
Device CPU/
GPU/I-O
50KB-1MB
1MB-10MB
10MB-100MB

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Java Embedded in Practice
Example Devices
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•ATMs
•Parking Meters
•POS Systems
•Lottery/Gaming Systems
•Multi Function Printers
•Intelligent Power Module
•Netbooks
•Routers & Switches
•Storage Appliances
•Network Management Systems
•Medical Imaging Systems
•Radar Systems
•Industrial PCs
•Factory Automation Systems
•Geo-Imaging Devices
•Smart Meters
•RFID Readers
•Video Conferencing Systems
•In-Flight Entertainment Systems
•Video Streaming Systems
•Electronic Voting Systems
•Voice Messaging Systems
•Security Systems

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Java Embedded Suite
On-device Java Platform

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Introducing Oracle Java Embedded Suite
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Enterprise Data & Applications
Java Embedded Suite
Embedded
DB
•Includes capabilities to support today’s intelligent devices
•Java Embedded runtime plus full SQL DB and Web Server/Services

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Oracle Java Embedded Suite (JES)
JES consist of three parts:
•Java SE Embedded
•Java Standard Edition for embedded devices
•Java DB
•Java SQL Database (Derby)
•Glasfish Embedded
•Subset of JEE Web Profile
•Servlet container
•RESTful web services
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OS & Hardware
Java SE Embedded
Java DB
(SQL Database)
Glassfish
(Web Server)
Your applications

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JES - Sample Deployment Architecture
JES Deployment
•JES acting as a concentrator (hub) of multiple edge devices
•Collects and forwards the data and messages to the backend
•Performs local analytics
•Hosts web services and applications
•Backend system gets aggregated and pre- processed data
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Oracle Event Processing for Java Embedded
On-device event processing

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Complex Event Processing (CEP)
Complex Event Processing
•Method of tracking and analyzing data combined from multiple streams to infer events or patterns that suggest more complicated circumstances
•Goal is to identify business meaning of events (such as opportunities or threats) and respond to them as quickly as possible.
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Event Patterns
Event Stream
Fine-grained simple events from sensors and edge devices
Complex Events
Machine module non-functional, power outage, device tampering, etc.

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Complex Event Processing (CEP)
CEP event patterns
•Filtering:
New stream filtered for specific criteria, e.g. temperature > 200 F
•Correlation & Aggregation:
Scrolling, time-based window metrics, e.g. average heart pulse rate in the last 3 days
•Pattern Matching:
Notification of detected event patterns, e.g. machine events A, B and C occurred within 15 minute window
•Geospatial, Predictive Modeling and beyond
Immediate recognition of geographical movement patterns, apply historical business intelligence models using data mining algorithms
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Event Patterns
Event Stream
Fine-grained simple events from sensors and edge devices
Complex Events
Machine module non-functional, power outage, device tampering, etc.

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Input Adapter
Channel
Input Adapter
Channel
Business Logic
(CQL)
Channel
Channel
Channel
Output Adapter
Output Adapter
Oracle Event Processing Application
DB
Input adapters connect to data sources
Channels help control the flow of data and can be tuned for optimal performance
Databases, Coherence caches, Hadoop (HDFS) and NoSQL database can be referenced directly in CQL processors
CQL processors contain filtering, correlation, aggregation and pattern matching business logic
Output adapters send data and alerts to downstream systems and business processes
Business Logic
(CQL)
Business Logic
(CQL)
Big Data
Coherence
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High-Performance In-Memory Data Processing
Input Adapter
Data
Input Adapter
Data
Channel
Business Logic
(CQL)
Channel
Data
Data
Analytics
Channel
Business Logic
(CQL)
Enrich
Output Adapter
Data
Data
Data
Data
Analytics: Continuously Sliding Windows of Streaming Data, Filtering, Correlations, Calculations, Aggregations, Pattern Matching, Missing Event Detection, Spatial Analysis, etc.
Enrichment: Integrate with data from DB, Coherence, NoSQL, Hadoop etc.
Oracle Event Processing
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OEP
Oracle Event Processing: From Noise to Value
Connecting the Internet-of-Things to Your Enterprise
Distributed Intelligence
•Filtering
•Correlation
•Aggregation
•Pattern matching
Devices / Gateways
SOA
Internet of Things
SOA / Enterprise
“Sea of data”
Macro-event
High-value
Actionable
In-context
OEP
embedded
OEP
• High Volume
• Continuous Streaming
• Sub-Millisecond Latency
• Disparate Sources
• Time-Window Processing
• Pattern Matching
• High Availability / Scalability
• Unique Coherence Integration
• Geospatial, Geo-fencing
• Big Data & IoT Integration
•Action!
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Complex Event Processing vs „old school” database
Inverted Database
Data is ‘static’
Queries are ‘dynamic’
•Data (event) is ‘dynamic’
•Queries are ‘static’

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OEP for Java Embedded Components Architecture
OEP Architecture
•OEP is a collection of OSGi bundles.
•OEP supports dynamic deployment of OEP applications (also packaged as OSGi bundles).
•Modular architecture simplifies customization of OEP for different environments
•Core OEP programming model is uniform (Java, CQL) across all environments.
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Modular Architecture based on OSGi
OSGi Module Framework
Module management, lifecycle & classloading
Core service registry
OEP Server Modules (OSGi bundles)
CQL Engine
Config & Admin
Logging
Adapters
. . .
OEP Applications (OSGi bundles)
Application 1
Application 2
. . .
Embedded JRE
JavaDB
Java Embedded Suite
OEP Embedded

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Continuous Query Language (CQL)
•CQL is based on standard SQL with extensions for streaming data
–CQL queries support filtering, partitioning, aggregation, correlation (joins across streams), and pattern matching on streaming and relational data
–Extends standard SQL by adding notion of Stream, operators for mapping between relations and streams, and extensions for pattern matching
–Window operator (e.g. RANGE 1 MINUTE) transforms stream into a relation
•Example:
SELECT AVG(temperature) AS avgTemp, tempSensorId
FROM temperatureInputStream[RANGE 1 MINUTE]
GROUP BY tempSensorId
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Demo
Using JES + OEP + USB webcamera on Raspberry Pi to create smart home security system

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Home Surveillance System Concept
Oracle Confidential – Internal/Restricted/Highly Restricted
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Secure VPN
Internet
Data Center OEP + BAM + BPM
Push
House owner
Thief
USB
web camera
Raspberry Pi
+ JES + OEP Embedded

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Home Surveillance System – Incident Processing
Oracle Confidential – Internal/Restricted/Highly Restricted
23
Data Center OEP + BAM + BPM
House owner
Owner confirms the incident using MAF application
Operator dispatches the incident to a brigade
System generates a task for an operator
The nearest brigade takes the task

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What do we need?
Demo Environment:
•Raspberry Pi Model B
•Debian Wheezy (armel version) http://bit.ly/1jpsN7s
•Java Embedded Suite http://bit.ly/Rw0e1d
•Oracle Event Processing Embedded http://bit.ly/1kjMenb
•Free Webcam Capture library from SarXos http://bit.ly/1zKleCy
•USB web camera
•From any store, starting from 5 Euro
•Deliberately low resolution (it works mainly as a motion detector)
•Any iOS or Android device
•Optional push notification service
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Push

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Let’s see how it works...

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Q&A
26
Lukasz Romaszewski
Oracle ISV Migration Center FMW Consultant
lukasz.romaszewski@oracle.com
ISV Migration Center blog: http://blogs.oracle.com/imc

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Snapshots from JavaCamp #13