Strategically Targeted Academic Research
On Sensor Networking and Signal Processing
for Smart and Safe Buildings
Pramod K. Varshney
Department of Electrical Engineering and Computer Science
Syracuse University
121 Link Hall
Syracuse, New York 13244 USA
http://www.eqstar.org
http://www.coees.org
Overall Structure of the Center
Strategically Targeted Academic Research
Technology Transfer
• 9 Academic Institutions
• 2 not-for-profit Research institutes
• 50 Corporate Partners
• Fosters University/Industry collaboration
Regional Partnership of Industry & Academe
• Strategically Targeted Academic Research
• Technology Transfer and Commercialization
2
Center’s Hub and Distributed Facilities
3
Outline
 Introduction
 Key challenges and issues
 Illustrative examples
 Concluding remarks
4
Indoor Air Pollution
SEALED WINDOWS
PEOPLE AND FURNITURE
• No access to outdoor air
•Paint, carpet emit VOCs
•Clothes/Grooming Products
CARCINOGENIC
PRODUCTS
SMOKING
• 70,000 chemical cleaning
products on the market
• Circulates through the
ventilation system
COPY MACHINE AND
PRINTERS
• Emit Ozone
THE OFFICE BATHROOM
EXTERMINATORS
• Mold machine
• Pesticides contain carcinogens
BUILDING
RENOVATIONS
•Paint fumes, dust, odors
WHAT FRESH AIR?
• Vents located over loading docks
Do you work in a Toxin Factory?*
*Business Week June 5, 2000
5
Societal 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
6
The 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
7
The 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
8
Motivation
 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!
9
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
10
Conceptual Process Diagram
External Inputs and Databases
Urban
Environment
Sensor
Network
Intelligent
Information
Processing
Control
and
Response
Plan
System Controller
and/or
Human Interface
Built
Environment
Computational Resource Management
11
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
12
Distributed and Pervasive Sensing Paradigm
Control/Action
Devices
Global
Decision
Maker
Local
Decision
Makers
Sensor
13
Challenges and Issues in i-EQS Sensor Networks
Lack of design principles for sensor networks in buildings
Challenge 1
Distribution among wired and wireless sensors is not known
Challenge 2
Sensor network architecture including topology, number and
placement of sensors, and protocols has not been addressed.
Challenge 3
Resource management including bandwidth and energy
management has not been investigated.
Challenge 4
Security and information assurance requirements are not well
understood.
14
Challenges and Issues in i-EQS Information Processing
Lack of intelligent information processing algorithms that
fully exploit all available information
Challenge 1
Inferencing and control mostly based on single sensor
measurements.
Challenge 2
Systems do not take full advantage of networked sensors,
information fusion and intelligent signal processing algorithms.
Challenge 3
Spatial and temporal dimensions (e.g. forecasting) are not
explored in detail.
Challenge 4
Systems are not robust and responsive to evolving dynamic
situations.
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Challenges and Issues in i-EQS Control
Lack of robust multi-level intelligent model-based
control algorithms
Challenge 1
Event and state recognition with incomplete
information
Challenge 2
Complex, non-linear and state/objective
dependent dynamics
Challenge 3
Slow system response
Challenge 4
Resources constraints, e.g, sensors, actuators,
computing power, bandwidth
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Sensor 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
17
Data 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 Fusion
u1
uN
...
u2
Data
fusion
center
u0
19
Design of Fusion Rules
Input to the fusion center: ui, i=1, …, N
0,
if detector i decides H0
1,
if detector i decides H1
ui =
Output of the fusion center: u0
0,
if H0 is decided
1,
otherwise
u0 =
Fusion rule: logical function with N binary inputs and one binary output
Number of fusion rules:
22
N
20
Optimum Decision Fusion
The optimum fusion rule that minimizes the probability of error is
PMi  P(ui  0 | H1 )
miss
PFi  P(ui  1 | H1 )
false alarm

thr eshold based or costs and
prior probabilit ies
P. K. Varshney, Distributed Detection and Data Fusion, Springer, 1997
21
Inferencing 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 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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Environmental Quality Systems Center (http://eqs.syr.edu/)
College of Engineering and Computer Science
Syracuse University
Intelligent Control of
Building Environmental Systems for Optimal
Evacuation Planning
by
J.S. Zhang1, C.K. Mohan2, P. Varshney2, C. Isik2, K.
Mehrotra2, S. Wang1, Z. Gao1, and R. Rajagopalan 2
1Dept.
of Mechanical, Aerospace and Manufacturing Engineering
2Dept. of Electrical Engineering and Computer Science
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i-BES for Optimal Evacuation Planning
Occupant
Zone/
Room
Personal
Env.
Multi-level
Controls:
Monitoring
of BES Conditions
Predictive
control
algorithm
3
Outdoor
Airshed
Multizone
Building
2
1
Prediction of
Pollutant Dispersion
0
Optimization of
People’s Movement
Simulated
Control Operations
25
Pollutant Dispersion in a 6-zone testbed
6
4
5
Zone 3
1
2
Building Energy and Environmental Systems Laboratory (BEESL)
at Syracuse University
26
Pollutant Dispersion: Multizone Model Simulations
6
4
e
Zone 6
Zone 5
5
Zone
3
1
2
Open exhaust
dampers
b
d
Zone 1
Release at e
Outdoor Air
Intake
Zone 4
e
d
Zone 3
Zone 2
a
a
Exhaust
Shut off supply air
Pressurization
e
c
Turn off Exhaust Fan
for the Corridor Zone
27
Pollutant
Dispersion
Control andResults
Evacuation Plan
Multizone
Model Simulation
Concentration change over time:
6
4
Evacuation routes:
5
Zone 3 1
2
28
A 73-Zone Example (a floor section of 22,000 ft2)
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Concluding 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.
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