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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
2
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
3
Center’s Hub and Distributed FacilitiesCenter’s Hub and Distributed Facilities
4
OutlineOutline
Introduction
Key challenges and issues
Illustrative examples
Concluding remarks
5
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
6
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
7
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
8
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
9
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!
10
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
11
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
12
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
13
Distributed and Pervasive Sensing ParadigmDistributed and Pervasive Sensing Paradigm
Control/Action Devices
Sensor
LocalDecisionMakers
Global Decision Maker
14
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
15
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
16
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
17
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
18
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
19
Decision FusionDecision Fusion
Datafusioncenter
u1
u2
uN
...
u0
20
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 =
21
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
22
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
23
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
24
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
25
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
26
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
27
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:
29
A 73-Zone Example (a floor section of 22,000 ft2)A 73-Zone Example (a floor section of 22,000 ft2)
30
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.