Sensor & Computing Infrastructure for Environmental Risks

machinebrainySoftware and s/w Development

Jun 8, 2012 (5 years and 15 days ago)

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Sensor & Computing
Infrastructure for
Environmental Risks


Vassilis

Papataxiarhis

vpap@di.uoa.gr


Department of Informatics and Telecommunications

University of Athens


Greece



"WSNs in the Real
-
World" Workshop,

ZigBee

Alliance Fall 2011 Members Meeting, October 2011
, Barcelona

Integrated Platform for
Autonomic Computing



C
C
C
ipa

Research interests:


Pervasive Computing, Mobile Computing,

Wireless Sensor Networks, Context
-

and Situation
-
Aware Computing

Information Fusion, Distributed Computing, Semantic Web, Intelligent Multimedia

Activities:

Multi
-
layered Data Fusion, Inform. Dissemination, Distributed Intelligence,

Context Prediction, Quality of Context, Optimal Stopping, Context Discovery

Publications:

Ph.D. dissertations: 7

IEEE / ACM Transactions: TMC, TAAS, TSMC, TITB

Top Conf.: WWW, MDM, COMPSAC, MobiDE, ICPADS, Globecom

175 publications, 14 book chapters, 1050 citations

Collaborators/Projects:

ICT/IST (IDIRA, IPAC, SCIER, PoLoS), GSRT (Polysema, Mnisiklis, Pythagoras)

CSEM, Uni. Geneva, Frequentis, Ministry of Defense, Fraunhofer, FIAT, Uni.Cyprus

Sector of Computer Systems and Applications

Pervasive Computing Research Group

(
http://p
-
comp.di.uoa.gr
)


Coordinator:
Stathes Hadjiefthymiades

(3 Faculty Members, 4 Post Doc Researchers, 7 Ph.D.
-

10 M.Sc.
Students)

Sensor & Computing
Infrastructure for
Environmental Risks

SCIER Objectives


S
ensor

network

infrastructures

for

the

detection

and

monitoring

of

disastrous

natural

hazards
.



A
dvanced

sensor

fusion

and

management

schemes
.



R
isk

evolution
models

simulated

on

GRID
.



Multi
-
risk platform.



Public
-
private sector cooperation.

LACU

Computing System

Public infrastructure

private infrastructure

Local
Alerting
Control
Unit
LACU

LACU

LACU

LACU

LACU

LACU

SCIER architecture

SCIER Sensing Subsystem


Sensor Infrastructure


In
-
field sensor nodes (humidity, temp, wind
speed/direction)


Out
-
of
-
field vision sensors (vision sensor)


Sensor Data Fusion

SCIER Computing Subsystem


Computation and Storage


Environmental models


Flash Floods (FL), forest fires (FF)


GIS Infrastructure


Storage, analysis and visualization of monitored data,
spatial calibration and event localization


Predictive Modeling


Front
-
End Subsystem

Local Alerting Control Unit

Data flow

Control flow

DataBase

Worker Node
Worker Node
VO Storage Disk
Pool
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SCIER Site
@
UoA
Computing Subsystem

Alerting Infrastructure

JDBC

Sensor
Infrastructure

Sensing system proxy

XML

LACU

Software

modules

Remote
Administration
console

OSGI

LACU Fusion Component (FF)


Receives sensor data and executes fusion
algorithms.


Generates fused data with degree of
reliability
.


Fused data fed to the Computing Subsystem
.

2
nd

Level Fusion Process (FF) in
CS


Camera data and Fused sensor data from
LACUs are processed .


Algorithms:



Voting algorithm



Dempster Shafer Theory of Evidence


Triggers simulations according to the final
probability of fire, flood, etc.


Simulation of several possible futures through
the GRID infrastructure.


GRID used to simulate many possible future
situations (1
-
100) under different propagation
conditions


results analyzed to identify the size and shape
of the resulting burned area, and provide
probabilities for each of the simulated futures.

FF simulation modeling


Conditions stored in metadata catalog


Engine for parsing and evaluating conditions
based on incoming data.


Interface with Simulation subsystem triggering
model execution based on fusion result

Condition
evaluation engine

Sensor input data

Metadata
Catalog

conditions

Fusion Decision

FL Modeling

SCIER GRID and FL with web
-
services

Fusion

Sensors

Storage for:

-

fire models executables

-

model input data

-

model structural data

-

model output data

-

Pre
-
prepared WS + CS
scenarios

Services

GRID

SCIER central point

Collect data (location+time+value):

-

precipitation

-

temperature

-

humidity

-

wind

ArcGIS

Executes fire modelling jobs

User interface

Simulation PC(s)

Executes 1D flood modelling jobs

Incorporates pre
-
calculated flood maps lookup

Forwards data to storage

Issues simulation jobs

Runs web server with UI

System Validation & Evaluation


Testing includes both fires and flooding



Gestosa, Portugal (experimental and controlled
burns)



Stamata, Attica, Greece (fires, system deployed)



Aubagne, Bouches du Rhone, S. France (fires and
floods)



Brno, Czech Republic (floods, system deployed)

System Validation & Evaluation


Gestosa, Portugal (experimental and
controlled burns)

System Validation & Evaluation


Stamata, Attica, Greece (fires, system
deployed)

System Validation & Evaluation


Aubagne, Bouches du Rhone, S. France
(fires and floods)


IPAC

IPAC Objectives


Integrated Platform for Autonomic Computing


Main goals


Middleware for autonomic computing


Application Creation Environment


Application Creation
Environment

Visual

Editor

Textual

Editor

Code

Generation

Emulator

Debugger

IPAC
Applications

IPAC

Middleware
Services

OSGi Platform

H/W, OS, JVM

IPAC Node

Developer

WiseMAC

WiFi

Short Range
Communication
Interfaces

Sensing
Elements

GPS

SunSPOTs

Visual Sensors

IPAC Node

H
/
W
,
OS
,
JVM
Alarm
Chatting
Monitoring
Querying

S
E
C
IPAC Middleware
Sensing
Elements
Wireless
Network
Interfaces
Application Layer
IPAC
Embedded System
SRCC Proxy
&
Information
Dissemination
SEC Proxy

Reasoner
User Interaction Service

Reconfiguration Service
Event Checker Service

Scheduler

Application Manager
Alarm
S
R
C
C
Service Layer
OSGI
Framework
Service Registry
Event Admin
Service Tracking
Public Segment
Storage
Private Segment
Storage
Light
-
weight IPAC node


A lean version of the middleware (WiseMAC case only)


On an embedded wireless sensor node platform (WiseNode)


Targeted functionality


IPAC
-
compatible communication
-
wise


A single, customized application


To be used as relay node, simple sensor node, beacon, ... where full
IPAC complexity is not necessary



-
> more nodes...



-
> cheaper...





WiseNode

IEEE1451 in IPAC



IEEE1451 standard has inspired the implementation of the
Sensing Element Components as “smart sensor”.



The philosophy which the IEEE1451 is based on is one of
the features of the IPAC system, namely the uniform
treatment

of all IPAC sensors
.



The standard is still under development and some parts are
not well defined.



Commercial products (sensors, dev kit or adapter) are no
available, partially available or with very short lifetime



A Java implementation of the IEEE1451 has been
performed based on the SUNSpot platform

IEEE1451 software architecture

NCAP component:

-

“soft NCAP”,
SECproxy OSGI module that provide NCAP functionalities

-

embedded in the SEC Proxy service

-

new sensor discovery and sensor removal

-

sensor data retrieval

-

integration with Reasoner, Storage and ECS service



TIM component (Sunspot board):

-

SEC midlet on SUNSpot that provide TIM functionalities

-

physical sensor reading

-

r
espond to discovery queries

-

respond to transducer access requests

-

handle transducer management tasks

-

support TEDS management functions




SEC hardware platform

Hardware:

-

Dimensions 41 x 23 x 70 mm

54 grams

-

180 MHz 32 bit ARM920T

core
-

512K RAM/4M Flash

-

2.4 GHz IEEE 802.15.4 radio with integrated antenna

-

USB interface

-

3.7V rechargeable 720 mAh lithium
-
ion battery


-

32 uA deep sleep mode

-

General Purpose Sensor Board

-

2G/6G 3
-
axis accelerometer

-

Temperature sensor

-

8 tri
-
color LEDs

-

6 analog inputs

-

2 momentary switches

-

5 general purpose I/O pins and 4 high current output
pins


Software:

-

Virtual Squawk Machine

-

Fully capable J2ME CLDC 1.1 Java VM
with OS functionality

-

VM executes directly out of flash
memory

-

Device drivers written in Java

-

Automatic battery management

-

Developer Tools

-

Use standard IDEs. e.g. NetBeans, to
create Java code

-

Integrates with J2SE applications

-

Sun SPOT wired via USB to a computer
acts as a base
-
station

IPAC
-

Platooning


Two main scenarios: Road Condition & Road Availability


8 applications


Applications have specific business logic


Applications react when specific events are triggered


Events are based on: messages (data, etc) or sensor
values

Scenario 1: Road Condition


A convoy should avoid a non safe area
(e.g. ice in the road)



Applications used:


First Vehicle


the node has a vision sensor attached on
it but no temperature sensor


reacts in an ice event. The event is
triggered based on the vision sensor
indication and other vehicles’
temperature indication


in case of an ICE event is sends a
warning message to the rest of the
vehicles



Convoy Vehicle



has a temperature sensor attached on it


reacts in a warning message by
presenting the ICE warning in the
application interface

Scenario 2: Road Availability


Two convoys have intersecting routes


and should avoid simultaneous


use of a road junction.



Applications used:


Head Vehicle (for both convoys)


sends a ‘data’ message containing the node ID as the convoy moves


stops or continues its route according to the message sent by the route scheduler


Tail Vehicle (for both convoys)


sends a ‘data’ message containing the node ID as the convoy moves


Route scheduler


accepts ‘data’ messages (data events) and based on the Rssi values it decides which
convoy should proceed first

RSSI
-
based logic


Thorough handling of RSSI measurements from
convoy vehicle.



The route scheduler
assesses

the absolute RSSI
value to roughly determine the
distance

of the
approaching vehicle and the time derivative to
determine its
speed
.



Similar approach is followed for the departure
from the junction.

Thank you!

IPAC website:
http://ipac.di.uoa.gr


SCIER website:
http://www.scier.eu