ABSTRACT
Understanding building usage patterns and resource consumption, particularly for existing buildings, requires a sensing infrastructure for the building. Often, deploying these sensors and obtaining real-time information is hindered by installation and maintenance difficulties resulting from scaling down and powering these devices. Devices that rely on batteries are limited by the scale of the batteries and the maintenance cost of replacing them while AC mains powered sensors incur high upfront installation costs. To mitigate these burdens, we present a new architecture for designing building-monitoring focused energy-harvesting sensors. The key to this architecture is masking the inevitable inter-mittency provided by energy-harvesting with a trigger abstraction that activates the device only when there is useful work to be done. In this paper, we describe our architecture and demonstrate how it supports existing energy-harvesting sensor designs. Further, we realize three additional design points within the architecture and demonstrate how the sensors are effective at building monitoring and event detection. The sensors, however, are classically disruptive: they improve ease of installation and maintenance, but to do so, they sacrifice some fidelity and reliability. Whether this trade-off is acceptable remains to be explored, but the technology needed to do so is now here.
- Building Energy Efficiency Frontiers and Incubator Technologies (Benefit) - 2014 Funding Opportunity Announcement. http://www1.eere.energy.gov/financing/solicitationsdetail.html?sol_id=740.Google Scholar
- Cube Sensors. https://cubesensors.com/.Google Scholar
- Kill-A-Watt Wireless. http://www.p3international.com/products/consumer/p4220.html.Google Scholar
- Micro Crystal RV-3049-C3 RTC. http://www.microcrystal.com/index.php/products/real-time-clocks.Google Scholar
- Ninja Blocks Temperature Senor. http://shop.ninjablocks.com/products/temperature-and-humidity-sensor.Google Scholar
- Omron D6F-V03A1. http://components.omron.com/components/web/pdflib.nsf/0/343C62397126E69185257201007DD6C6/$file/D6F-V_1010.pdf.Google Scholar
- Piezo Sensor - LDT Series. http://meas-spec.com/product/t_product.aspx?id=2484&terms=LDT0-028K*.Google Scholar
- Department of Energy (DOE) Buildings Energy Data Book. http://buildingsdatabook.eren.doe.gov/, 2012.Google Scholar
- B. Balaji, H. Teraoka, R. Gupta, and Y. Agarwal. Zonepac: Zonal power estimation and control via HVAC metering and occupant feedback. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 18:1--18:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- A. Beltran, V. L. Erickson, and A. E. Cerpa. Thermosense: Occupancy thermal based sensing for hvac control. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 11:1--11:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- S. DeBruin, B. Campbell, and P. Dutta. Monjolo: An energy-harvesting energy meter architecture. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys '13, 2013. Google ScholarDigital Library
- S. DeBruin, J. Grunnagle, and P. Dutta. Scaling the wireless AC power meter. In Proceedings of the 11th International Conference on Information Processing in Sensor Networks, IPSN '12, pages 153--154, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- A. Frye, M. Goraczko, J. Liu, A. Prodhan, and K. Whitehouse. Circulo: Saving energy with just-in-time hot water recirculation. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 16:1--16:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- L. A. Hang-yat and D. Wang. Carrying my environment with me: A participatory-sensing approach to enhance thermal comfort. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 21:1--21:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- S. R. Iyer, V. Sarangan, A. Vasan, and A. Sivasubramaniam. Watts in the basket?: Energy analysis of a retail chain. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 4:1--4:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and implementation of a high-fidelity AC metering network. In IPSN '09: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, pages 253--264, Apr. 2009. Google ScholarDigital Library
- Y. Jiang, K. Li, L. Tian, R. Piedrahita, X. Yun, O. Mansata, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. MAQS: A mobile sensing system for indoor air quality. In Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp '11, pages 493--494, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- D. Jung and A. Savvides. Estimating building consumption breakdowns using on/off state sensing and incremental sub-meter deployment. In SenSys'10: In Proceedings of the Eighth ACM Conference on Embedded Networked Sensor Systems, Nov. 2010. Google ScholarDigital Library
- J. Kadengal, S. Thirunavukkarasu, A. Vasan, V. Sarangan, and A. Sivasubramaniam. The energy-water nexus in campuses. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 15:1--15:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- N. Klingensmith, D. Willis, and S. Banerjee. A distributed energy monitoring and analytics platform and its use cases. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 5:1--5:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, and J. A. Paradiso. A platform for ubiquitous sensor deployment in occupational and domestic environments. In IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks, Cambridge, Massachusetts, Apr. 2007. Google ScholarDigital Library
- J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. The smart thermostat: Using occupancy sensors to save energy in homes. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys '10, pages 211--224, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- P. Mansourifard, F. Jazizadeh, B. Krishnamachari, and B. Becerik-Gerber. Online learning for personalized room-level thermal control: A multi-armed bandit framework. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 20:1--20:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- A. Marchiori and Q. Han. Using circuit-level power measurements in household energy management systems. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys '09, pages 7--12, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- P. Martin, Z. Charbiwala, and M. Srivastava. DoubleDip: Leveraging thermoelectric harvesting for low power monitoring of sporadic water use. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys '12, pages 225--238, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- A. Parisio, D. Varagnolo, D. Risberg, G. Pattarello, M. Molinari, and K. H. Johansson. Randomized model predictive control for HVAC systems. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 19:1--19:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- U. Park and J. Heidemann. Data muling with mobile phones for sensornets. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys '11, pages 162--175, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- J. Ploennigs, B. Chen, A. Schumann, and N. Brady. Exploiting generalized additive models for diagnosing abnormal energy use in buildings. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 17:1--17:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- A. Rogers, S. Ghosh, R. Wilcock, and N. R. Jennings. A scalable low-cost solution to provide personalised home heating advice to households. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys'13, pages 1:1--1:8, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- U.S. EPA. The inside story: A guide to indoor air quality, Sept. 1993.Google Scholar
- T. Xiang, Z. Chi, F. Li, J. Luo, L. Tang, L. Zhao, and Y. Yang. Powering indoor sensing with airflows: A trinity of energy harvesting, synchronous duty-cycling, and sensing. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys '13, pages 16:1--16:14, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- L. Yerva, B. Campbell, A. Bansal, T. Schmid, and P. Dutta. Grafting energy-harvesting leaves onto the sensornet tree. In Proceedings of the 11th International Conference on Information Processing in Sensor Networks, IPSN '12, pages 197--208, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
Index Terms
- An energy-harvesting sensor architecture and toolkit for building monitoring and event detection
Recommendations
An Improved Cluster Head Selection in Routing for Solar Energy-Harvesting Multi-heterogeneous Wireless Sensor Networks
AbstractWireless sensor network (WSN) is an effective and efficient technology for field information collection in Internet of Things arena. Generally, the lifetime of any WSN is restricted due to the limited battery capacities available with its sensor ...
Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review
Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have ...
Delay-bounded utility-based event detection in energy harvesting sensor networks
CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information TechnologyIn many sensor network applications, sink node needs to actively communicate with other sensor nodes in order to perform event detection operations. For those applications, there is usually a delay-bounded associated with them and require the messages ...
Comments