Strategically Targeted Academic ResearchStrategically Targeted Academic Research

http://www.eqstar.orghttp://www.coees.orghttp://www.eqstar.orghttp://www.coees.org

On Sensor Networking and Signal Processing On Sensor Networking and Signal Processing for Smart and Safe Buildingsfor Smart and Safe Buildings

Pramod K. Varshney

Department of Electrical Engineering and Computer ScienceSyracuse University

121 Link HallSyracuse, New York 13244 USA

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Overall Structure of the CenterOverall Structure of the Center

Strategically Targeted Academic ResearchStrategically Targeted Academic Research

• 9 Academic Institutions• 2 not-for-profit Research institutes

Technology TransferTechnology Transfer

• 50 Corporate Partners• Fosters University/Industry collaboration

Regional Partnership of Industry & AcademeRegional Partnership of Industry & Academe

• Strategically Targeted Academic Research• Technology Transfer and Commercialization

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Center’s Hub and Distributed FacilitiesCenter’s Hub and Distributed Facilities

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OutlineOutline

Introduction

Key challenges and issues

Illustrative examples

Concluding remarks

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Indoor Air PollutionIndoor Air Pollution

SEALED WINDOWS• No access to outdoor air

CARCINOGENIC PRODUCTS• 70,000 chemical cleaning products on the marketCOPY MACHINE AND PRINTERS• Emit Ozone

THE OFFICE BATHROOM• Mold machine

BUILDING RENOVATIONS•Paint fumes, dust, odors

PEOPLE AND FURNITURE•Paint, carpet emit VOCs

•Clothes/Grooming Products

SMOKING• Circulates through the

ventilation system

EXTERMINATORS• Pesticides contain carcinogens

WHAT FRESH AIR?• Vents located over loading docks

Do you work in a Toxin Factory?*

*Business Week June 5, 2000

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Societal and Economic DriversSocietal and Economic Drivers

Health 17.7 million asthma cases (4.8 million children) 50-100 thousand annual deaths due to elevated levels of particulate

matter

Productivity $40 to $250 billion productivity loss due to poor IEQ

Sustainability $110 billion annual economic loss due to air pollution in urban areas 40% of total building energy consumption is for environmental control

(over 15% of total US energy consumption)

Security Built and urban environments are vulnerable to chemical/biological threats

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The ProblemThe Problem

Wide spectrum of buildings Residences, schools, hospitals, apartment buildings, office buildings,

factories, high-valued assets Indoor air quality goals

Health Productivity Exposure and risk Energy consumption cost

Scenarios Routine day-to-day

Health, productivity, costs Time to react is not critical

Emergency Safety, exposure Rapid response required

Affordability and cost issues New Buildings Retrofit

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The ProblemThe Problem

Some current solutions A single thermal sensor

Uneven/asymmetric conditions inefficient

Provide multiple “knobs” Control system is not adequate

Replace indoor air by fresh air frequently Too costly

Hybrid and demand-controlled ventilation Use sensing and control Maximize benefits of natural driving forces Control needed due to changing weather conditions

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MotivationMotivation

These and other current solutions are fairly “primitive”!

They use “one size fits all” solutions and do not reduce human exposure and maximize comfort to the desirable extent

Due to a wide spectrum of buildings and their scales, multiplicity of goals, and response time requirements, intelligent solutions are required!

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Why Distributed Large-scale Wireless Sensor Networks?Why Distributed Large-scale Wireless Sensor Networks?

Higher resolution and fidelity data available in a sensor-rich environment for customized environments Improved IAQ at different scales, e.g., personal level, thus

increasing productivity without much increase in cost Rapid response in emergency situations Improved reliability and robustness More degrees of freedom for distributed control

Enabling technologies are fairly mature for practical applications

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Conceptual Process Diagram Conceptual Process Diagram

SensorNetwork

Computational Resource Management

SensorNetwork

Controland

ResponsePlan

IntelligentInformationProcessing

System Controllerand/or

Human Interface

External Inputs and Databases

BuiltEnvironment

UrbanEnvironment

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Key Components Key Components

Sensor Networks

Topology, architecture, protocols and management

Intelligent Information Processing

Information fusion, learning algorithms, and knowledge discovery

Control and Mitigation Methodology

Control worthy models based on reduced order models, hierarchical

distributed control, mitigation and evacuation

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Distributed and Pervasive Sensing ParadigmDistributed and Pervasive Sensing Paradigm

Control/Action Devices

Sensor

LocalDecisionMakers

Global Decision Maker

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Challenges and Issues in i-EQS Sensor NetworksChallenges and Issues in i-EQS Sensor Networks

Distribution among wired and wireless sensors is not known

Sensor network architecture including topology, number and placement of sensors, and protocols has not been addressed.

Resource management including bandwidth and energy management has not been investigated.

Security and information assurance requirements are not well understood.

Lack of design principles for sensor networks in buildings

Challenge 1Challenge 1

Challenge 2Challenge 2

Challenge 3Challenge 3

Challenge 4Challenge 4

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Challenges and Issues in i-EQS Information ProcessingChallenges and Issues in i-EQS Information Processing

Inferencing and control mostly based on single sensor measurements.

Systems do not take full advantage of networked sensors, information fusion and intelligent signal processing algorithms.

Spatial and temporal dimensions (e.g. forecasting) are not explored in detail.

Systems are not robust and responsive to evolving dynamic situations.

Lack of intelligent information processing algorithms that fully exploit all available information

Challenge 1Challenge 1

Challenge 2Challenge 2

Challenge 3Challenge 3

Challenge 4Challenge 4

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Challenges and Issues in i-EQS ControlChallenges and Issues in i-EQS Control

Lack of robust multi-level intelligent model-based control algorithms

Event and state recognition with incomplete information

Complex, non-linear and state/objective dependent dynamics

Slow system response

Resources constraints, e.g, sensors, actuators, computing power, bandwidth

Challenge 1Challenge 1

Challenge 2Challenge 2

Challenge 3Challenge 3

Challenge 4Challenge 4

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Sensor Placement ProblemSensor Placement Problem

Problem: Determining the locations where sensors should be placed, maximizing coverage and detection capability while minimizing cost

Factors and Problem Parameters: Building layout Air inlet and outlet (HVAC) locations Air flow simulation and analytic models Sensor characteristics and costs

Approach: Multiobjective optimization Modeling each candidate configuration of sensors as a point in a

multidimensional space Applying evolutionary algorithms to sample search space effectively and

efficiently

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Data Fusion IssuesData Fusion Issues

Problems: Detecting the presence of activities of interest, e.g., abnormally high

pollutant concentration Classifying the type of activity, e.g., the type of pollutant

Factors and Problem Parameters: Sensor Characteristics in terms of their detection ability Sensor location and coverage

Approach Distributed detection theory – decision fusion Algorithms to deal with uncertainties – modeling errors, asynchronous

information Adaptation to changing environmental conditions

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Decision FusionDecision Fusion

Datafusioncenter

u1

u2

uN

...

u0

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Design of Fusion RulesDesign of Fusion Rules

Input to the fusion center: ui, i=1, …, N

Output of the fusion center: u0

Fusion rule: logical function with N binary inputs and one binary output

Number of fusion rules: 22N

0, if detector i decides H0

1, if detector i decides H1

ui =

0, if H0 is decided

1, otherwiseu0 =

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Optimum Decision FusionOptimum Decision Fusion

The optimum fusion rule that minimizes the probability of error is

iesprobabilitprior

and costsor based eshold thr

alarm false )|1(

miss )|0(

1

1

HuPP

HuPP

iFi

iMi

P. K. Varshney, Distributed Detection and Data Fusion, Springer, 1997

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Inferencing in Distributed Sensor NetworksInferencing in Distributed Sensor Networks

Problems: Detecting relationships between pollutant concentrations at

different locations Detecting locations of abnormally high pollutant sources

Factors and Problem Parameters: Fluid flow models and simulations Pollutant source models and locations Potential sensor locations

Approach: Inferencing with time-sensitive probabilistic (Bayesian) network models

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Illustrative ExamplesIllustrative Examples

UC Berkeley study shows that the use of multiple sensors and ad hoc control strategies (Single HVAC) reduced energy consumption as well as predicted percentage dissatisfied (PPD) Energy-optimal scheme

17% reduction in energy consumption 6% reduction in PPD 30%24%

Comfort-optimal scheme 4% reduction in energy consumption 10% reduction in PDD 30%20%

N. Lin, C. Federspiel and D. Auslander, “Multi-sensor Single-Actuator Control of HVAC Systems”, Int. Conf. For Enhanced Building Operations, Richardson, TX, 2002

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Intelligent Control of Intelligent Control of Building Environmental Systems for Optimal Building Environmental Systems for Optimal

Evacuation PlanningEvacuation Planning

byby

J.S. ZhangJ.S. Zhang11, C.K. Mohan, C.K. Mohan22, P. Varshney, P. Varshney22, C. Isik, C. Isik22, K. , K. MehrotraMehrotra22, S. Wang, S. Wang11, Z. Gao, Z. Gao11, and R. Rajagopalan, and R. Rajagopalan 2 2

11Dept. of Mechanical, Aerospace and Manufacturing Engineering Dept. of Mechanical, Aerospace and Manufacturing Engineering 22Dept. of Electrical Engineering and Computer ScienceDept. of Electrical Engineering and Computer Science

Environmental Quality Systems Center (http://eqs.syr.edu/) Environmental Quality Systems Center (http://eqs.syr.edu/)

College of Engineering and Computer ScienceCollege of Engineering and Computer Science

Syracuse UniversitySyracuse University

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i-BES for Optimal Evacuation Planningi-BES for Optimal Evacuation Planning

Prediction of Pollutant Dispersion

Optimization of People’s Movement

Monitoringof BES Conditions

PersonalEnv.

Zone/Room

MultizoneBuilding

OutdoorAirshed

Multi-levelControls:

3 2 1

Occupant

0

Simulated Control Operations

Predictivecontrol

algorithm

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Pollutant Dispersion in a 6-zone testbedPollutant Dispersion in a 6-zone testbedPollutant Dispersion in a 6-zone testbedPollutant Dispersion in a 6-zone testbed

Building Energy and Environmental Systems Laboratory (BEESL)at Syracuse University

Zone 32

6

14

5

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Pollutant Dispersion: Multizone Model SimulationsPollutant Dispersion: Multizone Model Simulations

c

e e

e

e

Zone 1

Zone 2

Zone 3

Zone 4

Zone 5

Zone 6

a a

b

Turn off Exhaust Fan for the Corridor Zone

Pressurization

Exhaust

Shut off supply air

Release at Outdoor Air Intake

d

d

Open exhaust dampers

Zone 3 2

614

5

28

Zone 32

6

14

5

Multizone Model Simulation ResultsPollutant Dispersion Control and Evacuation PlanPollutant Dispersion Control and Evacuation PlanConcentration change over time: Evacuation routes:

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A 73-Zone Example (a floor section of 22,000 ft2)A 73-Zone Example (a floor section of 22,000 ft2)

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Concluding RemarksConcluding Remarks

Management of indoor air quality is an interesting and challenging application.

Theory and implementation is in its infancy.

Design of the headquarters of the Center of Excellence is underway. It will serve as a testbed for the new technology.