A Scalable Sensor Middleware for Social End-User Programming
A substantial amount of research has focused on developing sensor middleware targeted at various research communities such as networking and context awareness. This chapter presents SAWA, a sensor middleware based on Sensor Andrew aimed at social end-user programming. SAWA is designed to collect, present, share, and act on sensor data. First, it allows novice users to deploy a multitude of both physical and virtual sensors and actuators (e.g. temperature, light, unread email count, friend’s status on Facebook, SMS, Tweet) and to aggregate this data in a central server. Users are able to access an online portal to visualize and explore their recorded data. In addition, they can request and share access to other users’ sensor streams. Finally, they can create actions that are driven by sensor data—both physical and virtual, and both their own or any of their friends’. In addition to describing SAWA’s architecture, this chapter presents case studies where this middleware was used. It is shown that in addition to being robust and scalable, SAWA opens up a series of new applications by allowing users to program sensors and actuators in a shared social environment.
KeywordsSensor Network Sensor Data Dynamic Text Virtual Sensor Photo Resistor
This work is funded by the Portuguese Foundation for Science and Technology (FCT) grants CMU-PT/HuMach/0004/2008 (SINAIS) and CMU-PT/SE/0028/2008 (Web Security and Privacy).
- 1.Barrett, K. May 2009. http://fyi.oreilly.com/2009/05/what-can-you-do-with-xmpp.html.
- 2.Dickerson, R. F., et al. (2008). MetroNet: case study for collaborative data sharing on the world wide web. In 2008 international conference on information processing in sensor networks (IPSN 2008) (pp. 557–558). Google Scholar
- 3.Elahi, B. M., Romer, K., Ostermaier, B., Fahrmair, M., & Kellerer W. (2009). Sensor ranking: A primitive for efficient content-based sensor search. In Proceedings of the 2009 international conference on information processing in sensor networks, Washington (pp. 217–228). Google Scholar
- 4.Fletcher, B. (2006). XMPP & cross domain collaborative information environment. PowerPoint Slides. August 2006. Google Scholar
- 5.Newburry, N. (2008). http://www.frost.com/prod/servlet/market-insight-top.pag?docid=118964127. 22 January 2008.
- 6.Hammoudeh, M., Newman, R., Mount, S., & Dennett C. (2009). A combined inductive and deductive sense data extraction and visualisation service. In Proceedings of the 2009 international conference on pervasive services, London (pp. 159–168). Google Scholar
- 7.http://xmpp.org/about-xmpp/. January 2010.
- 8.Kenniche, H., & Ravelomananana, V. (2010). Random geometric graphs as model of wireless sensor networks. In The 2nd international conference on computer and automation engineering (ICCAE), 6 February 2010 (pp. 103–107). Google Scholar
- 9.Ribeiro, A., Silva, F., Freitas, L., Costa, J., & Frances, C. (2005). SensorBus: a middleware model for wireless sensor networks. In Proceedings of the 3rd international IFIP/ACM Latin American conference on networking, Cali, Columbia (pp. 1–9). Google Scholar
- 10.Rowe, A., et al. (2008). Sensor Andrew: Large-scale campus-wide (Technical Report). Carnegie Mellon University, Pittsburgh. Google Scholar
- 11.XMPP Software Foundation. http://xmpp.org/xsf/press/2003-09-22.shtml. September 2003.