The defense will take place on Monday 27th June at 14:30 in the Chappe amphitheatre, Chappe Building, INSA Lyon.
The presentation will be in English.
MINET Pascale, Inria
DIAS DE AMORIM Marcelo, CNRS
BEYLOT André-Luc, ENSEEIHT
ROUSSEAU Franck, ENSIMAG
BOUSSETTA Khaled, Université Paris 13
VALOIS Fabrice,INSA Lyon
Wireless Sensor Networks (WSNs) have been regarded as an emerging and promising field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. However, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, processes information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis.
We make four main contributions:
Firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capacity change due to data aggregation. Using these metrics, we confirm that data aggregation saves energy and capacity whatever the routing or MAC protocol is used.
Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better recovered fidelity.
Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology.
Finally, considering a given application, a target accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.