Modeling of Sensor Data and Context for the Real World Internet
Villalonga, P, Bauer, M, Huang, V, Bernat, J and Barnaghi, P (2010) Modeling of Sensor Data and Context for the Real World Internet In: 7th IEEE Workshop on Context Modeling and Reasoning (CoMoRea), 2010-03-29 - 2010-04-02, Mannheim, Germany.
comorea_camera_ready.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
The Internet is expanding to reach the real world, integrating the physical world into the digital world in what is called the Real World Internet (RWI). Sensor and actuator networks deployed all over the Internet will play the role of collecting sensor data and context information from the physical world and integrating it into the future RWI. In this paper we present the SENSEI architecture approach for the RWI; a layered architecture composed of one or several context frameworks on top of a sensor framework, which allows the collection of sensor data as well as context information from the real world. We focus our discussion on how the modeling of information is done for different levels (sensor and context data), present a multi-layered information model, its representation and the mapping between its layers.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Identification Number :||https://doi.org/10.1109/PERCOMW.2010.5470594|
|Related URLs :|
|Additional Information :||Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||21 May 2012 09:18|
|Last Modified :||09 Jun 2014 13:17|
Actions (login required)
Downloads per month over past year