Title: "Machine learning based distributed optimization of data fusion/aggregation schemas in opportunistic networks"
Summary :
This thesis focuses on the study of query optimization techniques for wireless sensor/actuator networks. The optimization is obtained using a distributed learning strategy applied within the hardware and environment of the networks. The aim is to finally offer access methods to the useful information required by the final users either immediately (reactive) or when it appears in the network (proactive). The optimization and learning methods are done regarding the constraints of the wireless communication and sensors/actuators networks.
Keywords: Query optimization, data network integration, artificial learning, addaptive query evaluation, autonomy
Lab: Grenoble Informatics Laboratory (LIG)/Grenoble University, France
and CEA Grenoble, France.
Advisors:
- Pr. Christine Collet. Manager of the HADAS group (database) from the Grenoble Informatics Laboratory (LIG), France.
- Dr. Christophe Bobineau, HADAS group (LIG, France).
- Dr. Jean-Benoît Pierrot. LCNA (CEA Grenoble, France).
Starting: September 2009
Funding: CEA contract
Duration: 3 years
Profile:
- good academic records
- in-depth knowledge in database (distributed databases, distributed query optimization, ubiquitous environments)
- good programing skills
- Knowledge in machine learning appreciated
To apply: contact ***ASAP*** christophe.bobineau@imag.fr and/or christine.collet@imag.fr
For formal application process, you will need to provide:
- CV and academic records
- motivation letter
- recommendation letter(s)
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