Call for Papers

Call for papers (pdf)

Successful papers will clearly demonstrate how much energy is reduced by the authors' contribution, either through real-world results or credible simulation and analysis of an energy problem. Similarly, showing how the technology would be integrated into smart grid architectures and an analysis of its performance would also be valuable.
We solicit contributions that focus on the design of architectures that are capable of improving the global energy efficiency of buildings leveraging connected sensing systems, networks, and devices. We are particularly interested in contributions related to:
•    Sensor-based architecture for energy saving in buildings
•    Building energy monitoring, prediction and decision support
•    Technology integration into smart grid and alternative energy sources
•    Energy saving and peak leveling through energy metering
•    Energy efficiency for data centers
•    Application studies / field trials
•    Cross-systems power conservation
•    Architecture for integration and interoperability of sensor systems
        (e.g. 6LowPAN, IP, BacNET, Powerline, etc)
•    Experimental evaluation of low-power industrial communication standards
•    Distributed processing and reasoning
Topics of interest include but are not limited to:
•    Convergence of communication architectures and protocols
•    Cross-network energy-efficient protocols
•    Data gathering, transport, mining, dissemination across multiple networks
•    New application scenarios and use cases
•    Integration with existing communication systems
•    Web Services and databases for sensor systems
We expect concise papers (max. 6 pages), presenting results from field trials, theoretical and practical issues of reducing energy consumption in buildings by using embedded sensing systems written from both an academic and an industrial perspective.
Accepted papers will appear in the workshop proceedings adjuncted to SenSys and will be electronically published by ACM .
BuildSys-Ext-CFP.pdf386.67 KB