Abstract
The simple design principle of IoT (Internet of Things) is to ubiquitously connect different end devices and sensors, and build a network with the surrounding environment to provide intelligent recognition and management. The appliance recognition is a technology to distinguish electric appliances by the operating features of each appliance. It can allow the users to learn information regarding the power consumption of appliances during usage, and provide an easy and effective way of recognition for home the underlying network objects. However, the huge and complex computing required by the recognition of features of various appliances, a central server will be needed in order to implement the technology in most cases. The additional cost will reduce the user intentions and create difficulty in promotion. In view of this, this study proposes a distributed energy management system implemented within family heterogeneous network architecture. Through the communication and coordination of things in IoT, the system automatically search nodes with comparatively low computing load within the micro-processors in neighboring appliances, and implements distributed computing by using multiple nodes and the recognition algorithm. This way, the appliance recognition can utilize the multi-node recognition results, and fully exploit the computing power of the micro-processors with comparatively lower working load. The acquired features are recognized to reduce the load of appliance recognition and context power saving. Meanwhile, using the information of the surrounding sensors, it further provides context sensing and measuring services to allow the user immediate access to the information regarding types of appliances functioning in the house, and analyze the power consumption behavior of the user by a variety of sensing nodes distributed in the house. The system will recommend the most appropriate power consumption pattern or switch off unwanted appliances to achieve the effectiveness of energy management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Saguan, M.: Advanced Metering: summary and conclusion. In: Proceedings of the Smart Metering Workshop Organized by Florence School of Regulation, pp. 1–12 (February 2009)
Bennett, C., Highfill, D.: Networking AMI Smart Meters. In: Proceedings of the IEEE Energy 2030 Conference, pp. 1–8 (November 2008)
Cho, H.S., Kato, T., Yamazaki, T., Hahn, M.: Simple and Robust Method for Detecting the Electric Appliances Using Markers and Programmable Logic Devices. In: Proceedings of the IEEE 13th International Symposium on Consumer Electronic, pp. 334–338 (May 2009)
Serra, H., Correia, J., Gano, A.J., de Campos, A.M., Teixeira, I.: Domestic Power Consumption Measurement and Automatic Home Appliance Detection. In: Proceedings of the IEEE International Workshop on Intelligent Signal Processing, pp. 128–132 (September 2005)
John La Grou plugs smart power outlets, http://www.ted.com/talks/john_la_grou_plugs_smart_power_outlets_1.html (retrieved on May 2012)
Ito, M., Uda, R., Ichimura, S., Tago, K., Hoshi, T., Matsushita, Y.: A method of appliance detection based on features of power waveform. In: Proceedings of the International Symposium on Applications and the Internet, pp. 291–294 (August 2004)
Lam, H.Y., Fung, G.S.K., Lee, W.K.: A Novel Method to Construct Taxonomy of Appliances Based on Load Signatures. IEEE Trans. Consumer Electronics 53(2), 654–660 (2007)
Ruzzelli, A.G., O’Hare, G.M.P., Schoofs, A., Nicolas, C.: Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. In: Seventh Annual IEEE Communications Society Conference on Sensor, Proceedings of the IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, pp. 1–9 (June 2010)
Akbar, M., Khan, D.Z.A.: Modified nonintrusive appliance load monitoring for nonlinear devices. In: Proceedings of the IEEE International Multi-topic Conference, pp. 1–5 (December 2007)
Brdiczka, Reignier, P., Crowley, J. L., Vaufreydaz, D., Maisonnasse, J.: Deterministic and probabilistic implementation of context. In: The Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW 2006), March 13-17, on pages: 5, p. 50. IEEE Computer Society (2006)
Oliver, B., Patrick, R., James, L.C.: Automatic Development of an Abstract Context Model for an Intelligent Environment. In: Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE Computer Society (2005)
Oriana, R., Di Cristiano, F., Stefano, R., Kimmo, R.: Unearthing Design Patterns to Support Context-Awareness. In: Proceedings of the 4th Annual IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 383–387 (2006)
Sven, M., Andry, R.: A survey of research on context-aware homes. In: Proceedings of the Australasian Information Security Workshop Conference on ACSW Frontiers 2003, Adelaide, Australia, vol. 21, pp. 159–168 (2003)
Thomas, S., Kay, K., Frank, S., Ming, Y.: Middleware Support for Context-Awareness in 4G Environments. In: Proceedings of the 2006 International Symposium on World of Wireless, Mobile and Multimedia Networks, pp. 203–211 (2006)
Schilit, B.N., Adams, N., Want, R.: Context-Aware Computing Applications. In: IEEE Workshop on Mobile Computing Systems and Applications, pp. 85–90 (1994)
Schilit, B.N., Theimer, M.M.: Disseminating Active Map Information to Mobile Hosts. IEEE Network, 22–32 (1994)
Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)
Beigl, M., Gellersen, H.-W., Schmidt, A.: MediaCups: Experience with Design and Use of Computer-Augmented Everyday Objects. Computer Networks 35(4), 401–409 (2001)
Chen, Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Tech. report TR2000-381, Dept. of Computer Science, Dartmouth College, Hanover, N.H. (2000)
Gupta, R., Gopinath, P.: A Hierarchical Approach to Load Balancing in Distributed Systems. In: Proceedings of the Fifth Distributed Memory Computing Conference, vol. 2, pp. 1000–1005 (1990)
Barazandeh, I., Mortazavi, S.S.: Two Hierarchical Dynamic Load Balancing Algorithms in Distributed Systems. In: Second International Conference on Computer and Electrical Engineering (ICCEE 2009), vol. 1, pp. 516–521 (2009)
Schantz, R.E., Schmidt, D.C.: Research Advances in Middleware for Distributed Systems: State of the Art. In: Proc. IFIP World Computer Congress on Communications Systems: State of the Art (2002)
Groot, S., Goda, K., Kitsuregawa, M.: A Study on Workload Imbalance Issues in Data Intensive Distributed Computing. In: Kikuchi, S., Sachdeva, S., Bhalla, S. (eds.) DNIS 2010. LNCS, vol. 5999, pp. 27–32. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Huang, YM., Lai, YX. (2013). Distributed Energy Management System within Residential Sensor-Based Heterogeneous Network Structure. In: Mukhopadhyay, S., Jiang, JA. (eds) Wireless Sensor Networks and Ecological Monitoring. Smart Sensors, Measurement and Instrumentation, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36365-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-36365-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36364-1
Online ISBN: 978-3-642-36365-8
eBook Packages: EngineeringEngineering (R0)