Gierad Laput
Chris Harrison
Skip Abstract Section
Abstract
Future smart homes, offices, stores and many other environments will increasingly be monitored by distributed sensors, supporting rich, context-sensitive applications. There are two opposing instrumentation approaches. On one end is full sensor saturation, where every object of interest is tagged with a sensor. On the other end, we can imagine a hypothetical, omniscient sensor capable of detecting events throughout an entire building from one location. Neither approach is currently practical, and thus we explore the middle ground between these two extremes: a sparse constellation of sensors working together to provide the benefits of full saturation, but without the social, aesthetic, maintenance and financial drawbacks. More specifically, we target a density of one sensor per room (and less), which means the average home could achieve full coverage with perhaps ten sensors. We quantify and characterize the performance of sparse sensor constellations through deployments across three environments and 67 unique activities. Our results illuminate accuracy implications across key spatial configurations important for enabling more practical, wide-area activity sensing.
- R.E Abbott and S.C. Hadden. 1990. Product Specification for a Nonintrusive Appliance Load Monitoring System. EPRI Report #NI-101, 1990.Google Scholar
- Artem Babenko and Victor Lempitsky. 2015. Aggregating Deep Convolutional Features for Image Retrieval. In IEEE International Conference on Computer Vision (ICCV), Dec. 2015, pp. 1269--1277. arXiv: 1510.07493. Google ScholarDigital Library
- Rajesh Krishna Balan, Archan Misra, and Youngki Lee. 2014. LiveLabs: building an in-situ real-time mobile experimentation testbed. In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications (HotMobile '14). ACM, New York, NY, USA, Article 14, 6 pages. Google ScholarDigital Library
- Alex Beltran, Varick L. Erickson, and Alberto E. Cerpa. 2013. ThermoSense: Occupancy Thermal Based Sensing for HVAC Control. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (BuildSys'13). ACM, New York, NY, USA. Article 11, 8 pages.Google Scholar
- Jeffrey P. Bigham, Chandrika Jayant, HaSmoMwenjie Ji, Greg Little, Andrew MillerCoffe Maker Micro, Robert C. Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, and Tom Yeh. 2010. VizWiz: nearly real-time answers to visual questions. In Proceedings of the 23nd annual ACM symposium on User interface software and technology (UIST '10). ACM, New York, NY, USA, 333--342. Google ScholarDigital Library
- Bosch. Cross Domain Development Kit. Last accessed: September 16, 2018. https://xdk.bosch-connectivity.comGoogle Scholar
- Michael Boyle, Christopher Edwards, and Saul Greenberg. 2000. The effects of filtered video on awareness and privacy. In Proceedings of the 2000 ACM conference on Computer supported cooperative work (CSCW '00). ACM, New York, NY, USA, 1--10. Google ScholarDigital Library
- Leo Bremman. 2001. Random Forests. 2001. In the Journal of Machine Learning. Volume 45-1. 5--32. Google ScholarDigital Library
- Gabe Cohn, Sidhant Gupta, Jon Froehlich, Eric Larson, and Shwetak N. Patel. 2010. GasSense: Appliance-Level, Single-Point Sensing of Gas Activity in the Home. In Pervasive Computing: 8th International Conference (Pervasive 2010), Helsinki, Finland, May 17-20, 2010. Springer. 265--282. Google ScholarDigital Library
- Diane J. Cook, Narayanan C. Krishnan, and Parisa Rashidi. 2013. Activity Discovery and Activity Recognition: A New Partnership. IEEE Transactions on Cybernetics, Volume: 43, Issue: 3, June 2013.Google Scholar
- David Culler, Jason Hill, Mike Horton, Kris Pister, Robert Szewczyk, and Alec Wood. 2002. Mica: The commercialization of microsensor motes. In Sensor Technology and Design.Google Scholar
- Samuel DeBruin, Bradford Campbell, and Prabal Dutta. 2013. Monjolo: an energy-harvesting energy meter architecture. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). ACM, New York, NY, USA, Article 18, 14 pages. Google ScholarDigital Library
- James Fogarty, Carolyn Au, and Scott E. Hudson. 2006. Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In Proceedings of the 19th annual ACM symposium on User interface software and technology (UIST '06). ACM, New York, NY, USA, 91--100. Google ScholarDigital Library
- Jon E. Froehlich, Eric Larson, Tim Campbell, Conor Haggerty, James Fogarty, and Shwetak N. Patel. 2009. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. In Proceedings of the 11th international conference on Ubiquitous computing (UbiComp '09). ACM, New York, NY, USA, 235--244. Google ScholarDigital Library
- Anhong Guo, Xiang 'Anthony' Chen, Haoran Qi, Samuel White, Suman Ghosh, Chieko Asakawa, and Jeffrey P. Bigham. 2016. VizLens: A Robust and Interactive Screen Reader for Interfaces in the Real World. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16). ACM, New York, NY, USA, 651--664. Google ScholarDigital Library
- Sidhant Gupta, Matthew S. Reynolds, and Shwetak N. Patel. 2010. ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of the 12th ACM international conference on Ubiquitous computing (UbiComp '10). ACM, New York, NY, USA, 139--148. Google ScholarDigital Library
- Sidhant Gupta, Ke-Yu Chen, Matthew S. Reynolds, and Shwetak N. Patel. 2011. LightWave: using compact fluorescent lights as sensors. In Proceedings of the 13th international conference on Ubiquitous computing (UbiComp '11). ACM, New York, NY, USA, 65--74. Google ScholarDigital Library
- George Hart. Advances in Nonintrusive Appliance Load Monitoring. In Proc. of EPRI Inf. and Automation Conf. '91.Google Scholar
- George Hart. Nonintrusive Appliance Load Monitoring. 1992. In Proceedings of the IEEE EPRI Information and Automation Technology Conference, Washington, DC, June 26-28, 1992. 80(12), 1870--1891.Google Scholar
- Scott E. Hudson and Ian Smith. 1996. Techniques for addressing fundamental privacy and disruption tradeoffs in awareness support systems. In Proceedings of the 1996 ACM conference on Computer supported cooperative work (CSCW '96). ACM, New York, NY, USA, 248--257. Google ScholarDigital Library
- International Code Council, Inc. (2011). 2012 International Building Code (IBC). Country Club Hills, IL.Google Scholar
- Nicholas Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, and Andrew T. Campbell. A survey of mobile phone sensing. In IEEE Communications Magazine, vol. 48, no. 9, pp. 140--150, Sept. 2010. Google ScholarDigital Library
- Gierad Laput, Walter S. Lasecki, Jason Wiese, Robert Xiao, Jeffrey P. Bigham, and Chris Harrison. 2015. Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM, New York, NY, USA, 1935--1944.Google ScholarDigital Library
- Gierad Laput, Yang Zhang, and Chris Harrison. 2017. Synthetic Sensors: Towards General-Purpose Sensing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 3986--3999.Google ScholarDigital Library
- Walter S. Lasecki, Young Chol Song, Henry Kautz, and Jeffrey P. Bigham. 2013. Real-time crowd labeling for deployable activity recognition. In Proceedings of the 2013 conference on Computer supported cooperative work (CSCW '13). ACM, New York, NY, USA, 1203--1212. Google ScholarDigital Library
- Beth Logan, Jennifer Healey, Matthai Philipose, Emmanuel Munguia Tapia, and Stephen Intille. 2007. A long-term evaluation of sensing modalities for activity recognition. In Proceedings of the 9th international conference on Ubiquitous computing (UbiComp '07). Springer-Verlag, Berlin, Heidelberg, 483--500. Google ScholarDigital Library
- MeSchup - Build connected products. Last accessed: September 17, 2018. http://www.meschup.comGoogle Scholar
- Ganesh R. Naik and Wenwu Wang. 2014. Blind Source Separation: Advances in Theory, Algorithms and Applications. In Signals and Communication Technology, Springer 2014. ISBN 9783642550164. Google ScholarDigital Library
- Notion - Wireless Home Monitoring Made Easy. Last accessed: September 15, 2018. https://getnotion.comGoogle Scholar
- Shijia Pan, Mostafa Mirshekari, Pei Zhang, Hae Young Noh, "Occupant traffic estimation through structural vibration sensing," In Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 980306 (20 April, 2016).Google Scholar
- Particle Inc, Spark Server. Last accessed: September 15, 2018. https://github.com/spark/spark-serverGoogle Scholar
- Shwetak N. Patel, Thomas Robertson, Julie A. Kientz, Matthew S. Reynolds, and Gregory D. Abowd. 2007. At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In Proceedings of the 9th international conference on Ubiquitous computing (UbiComp '07). Springer-Verlag, Berlin, Heidelberg, 271--288. Google ScholarDigital Library
- Shwetak N. Patel, Matthew S. Reynolds, and Gregory D. Abowd. 2009. Detecting Human Movement by Differential Air Pressure Sensing in HVAC System Ductwork: An Exploration in Infrastructure Mediated Sensing. In Proceedings of Pervasive '08. Springer-Verlag, Berlin, Heidelberg, 1--18. Google ScholarDigital Library
- Fabian Pedregosa et. al. 2011. Scikit-learn: Machine Learning in Python. In the Journal of Machine Learning Research. Vol. 12. 2825--2830, 2011.Google Scholar
- Kristofer SJ Pister, Joseph M. Kahn, and Bernhard E. Boser. 1999. Smart dust: Wireless networks of millimeter-scale sensor nodes. Highlight Article, Electronics Research Laboratory Research Summary, 1999.Google Scholar
- Manuel Ruiz-Sandoval, Tomonori Nagayama, and B. F. Spencer Jr. 2006. Sensor development using Berkeley Mote platform. In Journal of Earthquake Engineering 10, no. 2, 2006. 289--309.Google ScholarCross Ref
- Sen.se Mother. The Universal Monitoring Solution. Last accessed: September 15, 2018. https://sen.se/mother/Google Scholar
- Nagender Kumar Suryadevara and Subhas Chandra Mukhopadhyay. Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly. In IEEE Sensors Journal, vol. 12, no. 6, pp. 1965--1972, June 2012.Google Scholar
- Emmanuel Munguia Tapia, Stephen S. Intille, and Kent Larson. 2004. Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In Pervasive Computing: Second International Conference (PERVASIVE 2004), Linz/Vienna, Austria, April 21-23, 2004. Springer. 158--175.Google Scholar
- Texas Instruments. SimpleLink SensorTag. Last accessed: September 14, 2018. http://www.ti.com/ww/en/wire-less_connectivity/sensortag2015/gettingStarted.htmlGoogle Scholar
- Geoffrey Werner-Allen, Konrad Lorincz, Matt Welsh, Omar Marcillo, Jeff Johnson, Mario Ruiz, and Jonathan Lees. 2006. Deploying a Wireless Sensor Network on an Active Volcano. In IEEE Internet Computing 10, 2 (March 2006). 18--25. Google ScholarDigital Library
- Mengyu Zhou, Kaixin Sui, Minghua Ma, Youjian Zhao, Dan Pei, and Thomas Moscibroda. 2016. MobiCamp: a Campus-wide Testbed for Studying Mobile Physical Activities. In Proceedings of the 3rd International on Workshop on Physical Analytics (WPA '16). ACM, New York, NY, USA, 1--6. Google ScholarDigital Library
Index Terms
- Exploring the Efficacy of Sparse, General-Purpose Sensor Constellations for Wide-Area Activity Sensing
Recommendations
Sensor Networks—Motes, Smart Spaces, and Beyond
As sensor platforms and networks become more and more ubiquitous, we look at the evolution of this technology domain and how it progressed from purely scientific applications to becoming pervasive entities in multiple aspects of our daily lives.
Segmenting sensor data for activity monitoring in smart environments
Within a smart environment, sensors have the ability to perceive changes of the environment itself and can therefore be used to infer high-level information such as activity behaviours. Sensor events collected over a period of time may contain several ...
A three-tiered node scheduling scheme for sparse sensing in wireless sensor networks
In wireless sensor networks, when the sensing range is significantly higher than the transmission range, a very large amount of sensing redundancy is created. Sparse sensing indicates the case of selecting a small number of representative nodes to cover ...
Comments