SAFIRE: Situational Awareness for Firefighters SITUATIONAL AWARENESS FOR FIRE FIGHTERS (SAFIRE) Goal: Improve the safety of firefighters by providing decision.

SAFIRE: Situational Awareness for Firefighters

SITUATIONAL AWARENESS FOR FIRE FIGHTERS (SAFIRE)

Goal: Improve the safety of firefighters by providing decision makers with

greatly improved situational awareness during response activities

SAFIRE Project DHS Update – September 15, 2009

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Agenda for the Day

Project Overview SAFIRE Concept / Technology SAFIRE Streams Testing/Validation/Outreach Future Plans: Project Outcomes, Continuity FEMA participation in upcoming DHS workshop on

“Incident Management, Resource Management, and Supply Chain Management” Nov. 5 and 6th at CERT, UCI, Irvine.

SAFIRE: Situational Awareness for Firefighters

SAFIRE Concept Overview

SAFIRE: Situational Awareness for Firefighters

SAFIRE System

FICB Visualization

External DataSources

Acousticdata

SAFIRE Core Technology Areas GIS

hazmat

occupancy

Multimodal Sensing Robust Network Infrastructure Visualization and User Interfaces (FICB) Sensor stream processing Integration of external data sources (Ebox) Speech

Video data

Environmentalsensors

Sensor database

FF physio.& location.

SAFIRE: Situational Awareness for Firefighters

Progress: Core Technology Areas

Speech Speech for situational awareness

Networking & Sensing Incorporation of new sensors (Co, SpCO, motes) New antenna array for increased coverage, multi-network & store-

and-forward architecture Stream management

ability to incorporate variety of sensors , multimodal sensor archival and retrieval functionality

FICB New functionalities in FICB – simplified UI, annotations, ebox

integration, etc. Ebox

Prototype development, ontologies for resource selection, integration of static and dynamic data such as sensing infrastructure of buildings

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May 29, 2009

July 15, 2009

Today

demo/video July 15, 2009

July 15, 2009

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SAFIRE Streams: A Semantic Middleware for Multi Sensor Applications

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SATDeployer

SATQL

Sensor and computing infrastructure Heterogeneous sensors and processing nodes

Distributed Mobile-agent based runtime

Deployment of operators

Convert Query -> VS -> opGraph

FICB / SAFIRE Server

SATRuntime

SAFIRE Streams Architecture

SATSchedulerSATMonitor

Scheduleto meet QoS

Query results

Semantic context

Query

(entity, attribute,

value)

VSVS<opGraph>context1

<opGraph>Query i

InfrastructureDB

SA

TR

ep

osi

tory

OperatorDB

PolicyDB

SemanticDB

(entities, Relationships,

VS)

Semanticknowledge

...

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SAFIRE Stream Middleware

Writing sensor applications is hard: -Continuous data-Sensor heterogeneity -Diversity of platforms-Tolerance to failures

• Powerful programming abstractions to ease application development

•Hide heterogeneity, failures, concurrency

•Core Services•alerting, triggering, data & stream management, queries.

•Mediation•application needs with resource constraints of devices & networks

Sensor

FICB FiltersAlerts Analysis

Networks

SA Applications

Middleware – glue between H/w, networks, OS and applications

Networks

Stream Middleware Goals

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Key Concepts Driving SAFIRE Streams

Semantic Level: Entities -- people, appliances, and

buildings, rooms; Relationships – interactions.

Infrastructure Level: sensing devices, computing devices,

network devices.

Virtual Sensors: maps data captured by sensors into

events in the semantic world.

Event Logs: evolution of physical world as

observed by the sentient system

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SAFIRE Streams models sensor embedded spaces at two levels

sentient Applications

Virtual Sensor

High level stream language like CQL

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Key Concept: Virtual Sensors

Provide the “bridge” between sensors & the semantic “real” world concepts.

L, Room12, t>Filter

[L=Room1]

AP Readings Listener

AP Readings

to location

Translate Location to

Lon./Lat.

FingerprintDB

Location Virtual Sensor

WiFi fingerprints, t>

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Virtual Sensors: Multi-Sensor Fusion to improve quality

<Peter, L, PDF, t>

AP Readings Listener

AP Readings to location

FingerprintDB

<Pet

er, L

, PD

F, t>

Signal strength Listener

Signal strength

triagulation

APlocations

Merge

<Person, L, Room12, t>

Location Virtual SensorUsing fingerprints

Location Virtual SensorUsing signal Strength

triangulation

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Building Applications using Semantic Model

Virtual Sensors “hide” complexity of sensor programming from application developers Convert heterogeneous sensor streams into semantic event streams Hide sensor failures / imprecision through

Noise reduction (e.g., averaging over multiple samples) multi-sensor fusion (e.g., multiple location sensing technologies provide more accurate

location assessment) Semantics (e.g., speech sensors exploit word correlation to improve on ASR)

Applications can view the system as consisting of high level concepts such as entities, events, artifacts, spaces, etc.

SAFIRE Streams supports high level query languages for implementing queries & triggers: SQL style stream language (at design stage – not yet implemented) Event graph based language

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Demo

5/27/09

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Multi-sensor localization in SAFIRE Streams

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Event Graphs in SAFIRE Streams

Triggers/continuous queries are converted into an event graph network. SATWARE Deployer submits the resulting event graph into an executable

pipeline based on available resources, machines and networks. Mediates with resources to guarantee application needs are met Multiple optimizations possible in executing such networks.

Locoperator

[FF1]

<FF1, L, Room12, t>

<FF1, L, Room12, t>

Join[t]

Filter[L=first floor]

Locoperator

[FF2]

<FF2, L, Room15, t>

{<FF1, L, Room12,t><FF2, L, Room15, t>}

Near[5 Rooms]

Detect when Fire Fighter 1 is on the 1st floor

Detect when FF1 & FF2 are near each other

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Multi-sensor store / query / visualize in SAFIRE Streams

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SAFIRE Streams Summary Middleware to ease multi-sensor applications

provides a powerful semantic interface for complex multi-sensor applications this feature used extensively in building SAFIRE SA

Applications

Supports core services Alerts, triggers, storage, archival, & replay capabilities.

Mediation between application needs & system resources E.g., sensor stream scheduling based on application quality

requirement

5/27/09

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Safire Project: Outreach Presentation

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SAFIRE: Future Directions

Completion of Project Creation of Robust technology

Testing Test system with Firefighters

Outcomes Continuity

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SAFIRE: Future DirectionsOutcomes

Safire System Product FICB

Research Products Ebox Speech Networking / Sensing

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SAFIRE: Future DirectionsOutcomes

Thoughts / Expectations on Technology Transfer? SAFIRE Advisory Board

Newport Beach FD LA County Fire City of Ontario OCFA EH&S

Deltin Corporation

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SAFIRE: Future DirectionsContinuity

Future Project Directions Ebox Speech Networking / Sensing SpCO data management [FireTrack]

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FireTrack SystemGoals of FireTrack1. A prototype framework for collecting, communicating, storing, and analyzing

exposure data entitled FireTrack

2. A detailed evaluation of the FireTrack system in a pilot study including specific recommendations on how to expand the system to a large scale deployment.

3. Data collected during the pilot study including exposure information at both the level of individual firefighters as well as at the environment under different conditions.

4. Database design to represent data captured about respiratory environments during fires including taxonomies to appropriately classify and analyze such data.

5. Specific recommendations on interventions techniques that can be realized through exposure monitoring that minimize avoidable exposure to toxins during firefighting.

6. Identification of long term partners who will be willing to (a) maintain such a system for capturing and managing exposure information, and (b) work with research groups to launch further (more comprehensive) data collection and analysis studies the FireTrack system will enable in the future.

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FireTrack System