School of Architecture Occupancy Estimation in Smart Building

School of Architecture Occupancy Estimation in Smart Building using Hybrid CO 2/Light Wireless Sensor Network Chen Mao 1, Qian Huang (Jenny)2 1 Senior Student, Electrical and Computer Engineering 2 Assistent Professor, School of Architecture Southern Illinois University Carbondale Presenter: Qian Huang

Outline • Introduction • Related Works • Proposed System Prototype • Experimental Results • Conclusion 2

Smart Building • Intelligently delivers useful services to residents at lowest cost and maximum comfort • Through a variety of emerging technologies: • wireless sensor network • Internet of things (Io. T) • Big data analytics Source: http: //www. rcrwireless. com/20160725/business/smart-building-tag 31 -tag 99 • Smart Building applications Intelligent parking, health monitoring, shopping assistance 3

Building Energy Reduction • According to US Department of Energy • 39% CO 2 and 70% electricity from Building operation • Inefficient operation of HVAC (heating, ventilation, and air conditioning) equipment results in remarkable energy loss • It is common that HVAC systems keep running in active ON mode, while certain thermal zone is empty • Smart buildings should be energy efficient and low energy bill to building owners • Demand-driven HVAC control • Adaptive HVAC control based on room occupancy 4

Existing Occupancy Detection • Effectiveness of demand-driven HVAC control heavily depends on accurate occupancy detection • Passive Infrared (PIR) motion sensor • RFID sensor • Acoustic recognition/processing sensor • Video/image sensor Acoustic • CO 2 sensor RFID PIR Video CO 2 5

Related Works • PIR motion sensor (2001, 2009, 2010) • Only detect if a person has moved in or out of an area, cannot detect actual occupancy • RFID (2008, 2012) • The location and trajectory of an occupant wearing a RFID sensor is easily observed and tracked – privacy and security concerns • Acoustic processing (2014) • Detection performance largely depends on environment where this technique is applied (quiet office vs. noisy supermarket) • Video/image sensor (2009, 2011, 2013) • Constraint of light of sight, high cost, privacy concern • CO 2 sensor (2011, 2012, 2015) • CO 2 level proportional to occupants, but varies case by case 6

Literature Review Summary None of existing design meets the requirement of low-cost, high-accuracy, and better privacy 7

Challenges of Occupancy Detection • Low cost • High detection accuracy • Non-intrusive (due to increased concerns on personal privacy) Source: http: //nomvo. com/marketing/seo/why-low-costseo-services-are-usually-bad-idea-but-not-always/ Source: https: //www. linkedin. com/pulse/201406250451362259773 -privacy-and-data-security-violations-what-s-the-harm 8

Our Contribution • We proposed a hybrid occupancy detection method using CO 2 and light sensors. – Unlike video/image sensors, light sensors only report the illuminance level of light situations, hence privacy is protected. – With the assistance of light sensor, the hybrid detection method achieves better accuracy than using CO 2 sensor alone. – We integrated this hybrid sensor with a wireless sensor node, and visualize the measurement data. 9

Wireless Sensor Network • Wireless sensor network is composed of numerous distributed autonomous sensor nodes – Each node senses ambient environment (Humidity, temperature or air quality) – Cost-effective, ease of use, flexibility, small size Source: https: //ns 2 projects. org/ns 2 -simulation-code-for-wireless-sensor-network/ 10

Proposed System Architecture • Entire system consists of proposed hybrid sensors and a central control computer. The measurement results of CO 2 and light levels are transmitted via wireless communication. 11

Prototype Implementation • Wireless sensor node from Texas Instruments (TI), miniature CO 2 sensor from COZIR and light sensor from Adafruit are selected for the proposed system. 12

Experimental Results • Output voltage of CO 2 sensor vs. Time when room ocupancy varies 3 ->5 ->4 ->3 • The output voltage of this CO 2 sensor precisely indicates room occupancy. 13

Experimental Results • A light sensor is taped on a door frame for experimental study • Once a person walks through the door and blocks lighting, the light sensor outputs a deep pulse response to this entrance or exit event 14

Experimental Results • Experimental setup to check response of light sensor under different illuminance conditions • The right figure shows how the output votlage of light sensor varies with illuminance. It looks like logarithmic relationship 15

Experimental Results • To verify the proposed hybrid detection method, we carried out experiments in an office building • Measured response of hybrid sensors when two occupants walk in and out of a room 16

Comparison References (Emmerich, 2001) (Lam, 2009) (Agarwal, 2010) (Lee, 2008) (Li, 2012) Mechanism Cost Intrusive Occupancy Detection Performance Passive infrared High Yes Failure RFID Low Yes Coarse-grained Varying with environment (Uziel, 2013) (Kelly, 2014) (Huang, 2016) Acoustic recognition Low No (Erickson, 2009) (Benezeth, 2011) (Ahmed, 2013) Image camera High Yes Failure when line of sight is not satisfied Failure when people keep silence (Sun, 2011) (Nassif, 2012) (Labeodan, 2015) CO 2 sensor Low No Accuracy depends on case by case, false detection may exist due to CO 2 level fluctuation This work CO 2 + Light Low No Improved accuracy with the assistance of light sensor 17

Conclusion • Smart building has great potential to increase quality of life, while significantly reducing energy usage and cost • Room occupancy is important information, which helps to realize energy-efficient demand-driven HVAC operation • Existing building occupancy detection or estimation methods can not meet all the requirements of low cost, high accuracy and privacy. A hybrid CO 2/light sensor is proposed to meet design challenges. • The proposed system has been assembled and tested experimentally in an office building. The measurement results validate the functionality and benefits. 18

THANK YOU & QUESTIONS? 19