On Sensor Networking and Signal Processing for Smart and Safe Buildings

1. Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University 121 Link Hall Syracuse, New York 13244 USA On Sensor Networking and Signal Processing for Smart and Safe Buildings

2. Overall Structure of the Center • Strategically Targeted Academic Research • 9 Academic Institutions • 2 not-for-profit Research institutes • Technology Transfer • 50 Corporate Partners • Fosters University/Industry collaboration • Regional Partnership of Industry & Academe • Strategically Targeted Academic Research • Technology Transfer and Commercialization

3. Center’s Hub and Distributed Facilities

4. Outline • Introduction • Key challenges and issues • Illustrative examples • Concluding remarks

5. Indoor Air Pollution • 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 • SEALED WINDOWS • No access to outdoor air • CARCINOGENIC PRODUCTS • 70,000 chemical cleaning products on the market • COPY MACHINE AND PRINTERS • Emit Ozone • THE OFFICE BATHROOM • Mold machine • BUILDING RENOVATIONS • Paint fumes, dust, odors Do you work in a Toxin Factory?* *Business Week June 5, 2000

6. 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

7. 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

8. 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

9. 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!

10. 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

12. 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 Paradigm Control/Action Devices Global Decision Maker Local Decision Makers Sensor

14. Challenges 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 1 Challenge 2 Challenge 3 Challenge 4

15. Challenges and Issues in i-EQS Information Processing Lack of intelligent information processing algorithms that fully exploit all available information 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. Challenge 1 Challenge 2 Challenge 3 Challenge 4

16. Challenges 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 1 Challenge 2 Challenge 3 Challenge 4

17. 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

18. 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

19. Decision Fusion u1 Data fusion center u2 u0 ... uN

20. Design of Fusion Rules 0, if detector i decides H0 0, if H0 is decided ui= u0= 1, if detector i decides H1 1, otherwise 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

21. Optimum Decision Fusion The optimum fusion rule that minimizes the probability of error is P. K. Varshney, Distributed Detection and Data Fusion, Springer, 1997

22. 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

23. 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

24. Intelligent Control of Building Environmental Systems for Optimal Evacuation PlanningbyJ.S. Zhang1, C.K. Mohan2, P. Varshney2, C. Isik2, K. Mehrotra2, S. Wang1, Z. Gao1, and R. Rajagopalan 21Dept. of Mechanical, Aerospace and Manufacturing Engineering 2Dept. of Electrical Engineering and Computer Science Environmental Quality Systems Center (http://eqs.syr.edu/) College of Engineering and Computer Science Syracuse University

25. i-BES for Optimal Evacuation Planning Occupant Personal Env. Outdoor Airshed Zone/ Room Multizone Building Multi-level Controls: 3 2 1 0 Monitoring of BES Conditions Prediction of Pollutant Dispersion Optimization of People’s Movement Predictive control algorithm Simulated Control Operations

26. Pollutant Dispersion in a 6-zone testbed Zone 3 6 5 1 4 2 Building Energy and Environmental Systems Laboratory (BEESL) at Syracuse University

27. Pollutant Dispersion: Multizone Model Simulations 6 5 1 4 2 Zone 3 Open exhaust dampers d d Shut off supply air Pressurization Turn off Exhaust Fan for the Corridor Zone e Zone 6 Zone 5 b Zone 1 e e Release at Outdoor Air Intake Zone 4 Zone 3 Zone 2 a a Exhaust c e

28. Pollutant Dispersion Control and Evacuation Plan 6 5 Zone 3 1 4 2 Multizone Model Simulation Results Concentration change over time: Evacuation routes:

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

30. 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.