Monitoring user activities in smart home environments
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Wireless sensor networks (WSNs) enable smart environments to create pervasive and ubiquitous applications, which give context-aware and scalable services to the end users. In this paper, we propose an architecture and design of a web application for a sensor network monitoring. Further, the variation in received signal strength indicator values is used for knowledge extraction. Experiments are conducted in an in-door room environment to determine the activities of a person. For instance, a WSN consisting of Moteiv’s Tmote Sky sensors is deployed in a bedroom to determine the sleeping behavior and other activities of a person.
- Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on wireless sensor networks. IEEE Communication Magazine, 40, 102–114.
- Bahl, P., & Padmanabhan, V. (2000). Radar: An in-building rf-based user location and tracking system. In INFOCOM 2000. Tel Aviv, Israel.
- Cerpa, A., Busek, N., & Estrin, D. (2003). Scale: A tool for simple connectivity assessment in lossy environments. In CENS technical report 0021.
- De, P., Basu, K., & Das, S. K. (2004). An ubiquitous architectural framework and protocol for object tracking using rfid tags. In The first annual international conference on mobile and ubiquitous systems: Networking and services (MOBIQUITOUS), August.
- Edwards, W. K., & Grinter, R. E., (2001). At home with ubiquitous computing: Seven challenges. In Proceedings of third international conference ubiquitous computing, Atlanta, 2001. Springer, LNCS.
- Erdogan, S. Z., & Hussain, S. (2007). Using received signal strength variation for energy efficient data dissemination in wireless sensor networks. In Workshop proceedings of 18th international conference on database and expert systems applications (DEXA) (pp. 620–624). Regensburg.
- Estrin, D., Bulusu, N., Heidemann, J., & Tran, T. (2003). Self-configuring localization systems: Design and experimental evaluation. ACM Transactions on Embedded Computing Systems (ACM TECS).
- Gallais, A., Parvery, H., Carle, J., Gorce, J.-M., & Simplot-Ryl, D. (2006). Efficiency impairment of wireless sensor networks protocols under realistic physical layer conditions. In 10th IEEE international conference on communication systems (ICCS 2006). Singapore.
- Ganesan, D., Krishnamachari, B., Woo, A., Culler, D., Estrin, D., & Wicker, S. (2002). Impact of radio irregularity on wireless sensor networks. In Technical report UCLA/CSD-TR 02-0013.
- Gao, T., Greenspan, D., Welsh, M., Juang, R. R., & Alm, A. (2005). Vital signs monitoring and patient tracking over a wireless network. In The 27th annual international conference of the IEEE EMBS. Shanghai, China, September.
- Ghorbel, M., Segarra, M.-T., Kerdreux, J., Thepaut, A., & Mokhtari, M. (2004). Networking and communication in smart home for people with disabilities. In ICCHP (pp. 937–944).
- Gillespie, L., Gillespie, W., Robertson, M., Lamb, S., Cumming, R., & Rowe, B. (2003). Interventions for preventing falls in elderly people. In Cochrane database of systematic reviews 2003.
- Gu, L., Jia, D., Vicaire, P., Yan, T., Luo, L., Tirumala, A., et al. (2005). Lightweight detection and classification for wireless sensor networks in realistic environments. In The 3rd ACM conference on embedded networked sensor systems. San Diego, USA, November
- Halkidi, M., Kalogeraki, V., Gunopulos, D., Papadopoulos, D., Zeinalipour-Yazti, D., & Vlachos, M. (2006). Efficient online state tracking using sensor networks. In The 7th international conference on mobile data management (MDM’06).
- Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., et al. (2005). Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Processing Magazine, 22, 38–51. CrossRef
- Kahn, J., Katz, R., & Pister, K. (1999). Next century challenges: Mobile networking for smart dust. In The ACM international conference on mobile computing and networking (Mobi Com’99). Seattle, USA, August.
- Nasipuri, A., & Li, K. (2002). A directionality based location discovery scheme for wireless sensor networks. In First ACM international workshop on wireless sensor networks and applications. Atlanta, USA.
- Priyantha, N., Chakraborthy, A., & Balakrishnan, H. (2000). The cricket location-support system. In Proceedings of international conference on mobile computing and networking (pp. 32–43). Boston, USA.
- Savarese, C., Rabaey, J. M., & Beutel, J. (2001). Locationing in distributed ad-hoc wireless sensor networks. In Proceedings of ICASSP01 (pp. 2037–2040).
- Scott, T., Wu, K., & Hoffman, D. (2006). Radio propagation patterns in wireless sensor networks: New experimental results. In IEEE international wireless communications and mobile computing conference (IWCMC’06). Vancouver, Canada, July.
- Tabar, A. M., Keshavarz, A., & Aghajan, H. (2006). Smart home care network using sensor fusion and distributed vision-based reasoning. In WSSN 2006 (pp. 145–154). ACM.
- Vogt, H. (2002). Efficient object identification with passive rfid tags. Lecture Notes in Computer Science (LNCS), 2414, 98–113.
- Woo, A., Tong, T., & Culler, D. (2003). Taming the underlying challenges of reliable multihop routing in sensor networks. In SenSys 2003. Los Angeles, USA.
- Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient mac protocol for wireless sensor networks. In INFOCOM 2002.
- Zhou, G., He, T., Krishnamurthy, S., & Stankovic, J. A. (2004). Impact of radio irregularity on wireless sensor networks. In The international conference on mobile systems, applications, and services (MobiSys). Boston, USA, June.
- Monitoring user activities in smart home environments
Information Systems Frontiers
Volume 11, Issue 5 , pp 539-549
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Smart home environments
- Wireless sensor network
- Pervasive computing
- Ubiquitous computing
- Intelligent monitoring
- Industry Sectors
- Author Affiliations
- 1. Jodrey School of Computer Science, Acadia University, Wolfville, Nova Scotia, Canada
- 2. Faculty of Engineering, Maltepe University, Istanbul, Turkey
- 3. Department of Computer Science and Engineering, Kyungnam University, Masan, Korea