Muhammad Hafeez CHAUDHARY

Spurred by ease of deployment provided by the wireless communication paradigm, wireless sensor networking is an emerging technology which finds application in many fields including military, environment monitoring, health care, and industrial automation, among others. A wireless sensor network (WSN) consists of spatially distributed sensors that cooperatively monitor physical conditions. The sensors are usually powered by limited capacity batteries which limit sensing, communication, and computational functionalities of the individual sensors and thereby of the overall network. These intrinsic limitations of the sensors and the reason that sensors produce measurements which are correlated due to their spatial proximity, energyefficient cooperative signal processing algorithms and communication protocols are required for information processing and dissemination in WSNs.

Aim of this work is to develop energy-efficient data processing algorithms and communication strategies in the context of realizing WSNs with acceptable sensing performance and operational lifetime. Specifically, we study optimization techniques and algorithms to realize energy-efficient parameter estimation in WSNs with spatially correlated data, analog and digital modulation schemes, perfect and imperfect knowledge of the communication channels, and under different network topologies.

Firstly, we consider an estimation problem in which a WSN is deployed to observe a source where sensors send their observations to a remote fusion center (FC). Due to the spatial correlation of the sensor observations, the underlying power allocation problem needs to be solved numerically. We show that using successive approximation approach, solution to the power allocation problem can be given by an iterative waterfilling-type solution. For this network setting, the impact of channel estimation errors on the performance of the power allocation scheme is also analyzed. Secondly, we investigate joint quantization and power allocation for powerconstrained estimation in WSNs. To this end, two quantization and transmission schemes are presented: one based on an optimal uniform quantization scheme and the other, an approximation, based on the pseudo-quantization noise model. We show that, compared to the former scheme, the later scheme is computationally simpler; whereas the distortion performance of the two schemes matches quite well. Thirdly, noting that the centralized WSN topology (all sensors directly send their observations to the FC) may not be optimal in the realm of energy-efficient estimation, we study the performance of power-constrained estimation in hierarchical WSNs. Finally, we study battery nonlinearities and their effect on network lifetime; and we also investigate the impact of degree of knowledge about the channel gains on the network lifetime.

Members of the jury :
Prof. Luc VANDENDORPE, promotor (UCL)
Prof. Christophe DE VLEESCHOUWER, chairperson (UCL)
Prof. Jérôme LOUVEAUX, secretary (UCL)
Prof. François HORLIN (ULB, Belgium)
Prof. Marc MOENECLAEY (UG, Belgium)
Prof. Ana Isabel PÉREZ-NEIRA (UPC, Spain)