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(Research) Energy Efficient Signal Acquisition in Wireless Sensor Networks: A Compressive Sensing Framework / (Research) Particle filtering on GPU for indoor pedestrian localisation

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Energy Efficient Signal Acquisition in Wireless Sensor Networks: A Compressive Sensing Framework, Wei Chen

Wireless sensor networks (WSNs) provide the ability to monitor various physical characteristics of the real world, such as sound, temperature, humidity, etc., by distributing a large number of inexpensive small devices in the detected environment. We present a novel approach based on the compressive sensing (CS) framework to monitor 1-D environmental information in WSNs. The proposed method exploits the compressibility of the signal to reduce the number of samples required to recover the sampled signal at the fusion center (FC) and so reduce the energy consumption of the sensors in both sampling and transmission. An innovative feature of our approach is a new random sampling scheme that considers the causality of sampling, hardware limitations and the trade-off between the randomization scheme and computational complexity. In addition, a sampling rate indicator (SRI) feedback scheme is proposed to enable the sensor to adjust its sampling rate to maintain an acceptable reconstruction performance while minimizing the number of samples, which results in a reduced energy consumption in the sampling and transmission. A significant reduction in the number of samples required to achieve acceptable reconstruction error is demonstrated using real data gathered by a WSN located in the Hessle Anchorage of the Humber Bridge.

Particle filtering on GPU for indoor pedestrian localisation, Agata Bradjic

A number of approaches have been proposed over the years for localisation of people within the indoor environments. An approach that has been developed in Computer Laboratory and that has evidenced a great promise uses inertial sensing technology and a particle filter for eliminating the drift from inertial sensors. The problem with this approach lies in the difficulty to scale particle filters to larger environments due to larger number of particles needed to perform the localisation. In this talk, I will describe how to attack this problem and improve scalability of the particle filter approach by offloading particle filter processing onto a Graphics Processing Unit (GPU). I will discuss how I have dealt with different obstacles in GPU implementation and will present experimental results obtained for a localization case study performed in the William Gates building.

This talk is part of the Computer Laboratory Digital Technology Group (DTG) Meetings series.

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