The primary focus of the thesis is to study the Bayesian inference problem in distributed Wireless Sensors Networks with particular emphasis on the trade-off between estimation precision and energy-awareness. We have proposed to use a distributed statistical signal processing in Wireless Sensors Networks with quantized measurements. In particular, this thesis addresses the application of variational methods for solving localization and tracking problems under energy and power constraints in Wireless Sensors Networks. Our work addresses three issues in wireless sensors networks: smart quantization scheme, cluster management and application of multi-objective optimization under energy constraint. The thesis contributions can be summarized as follows: - Target position estimation with quantized measurements based on variational methods - Channel estimation between the candidates sensors and the cluster head for target tracking in Wireless Sensor Network. - Adaptive optimized quantization under fixed and variable transmission power for target tracking in Wireless Sensor Network. - Best sensors selection that participate in data collection for target tracking in Wireless Sensor Network. - Secure data aggregation in Wireless Sensor Network. - Optimal communication path selection between sensors. - Multi-objective optimization method in Wireless Sensor Network. - Application of the multi-criteria data aggregation for crisis management based on multi-agents system in Wireless Sensor Network.
