Wireless Sensor Networks: Signal Processing and Communications Perspectives

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This scenario occurs in industrial environments, where heavy machinery has a detrimental impact on link quality and in multi-hop networks where congestion and duty cycling can also lead to dropped packets.

A significant number of missing measurements that can have a dramatic impact on subsequent tasks, such as detection of unusual events or clustering of the measurements. A third scenario is related to the temporal sampling frequency of a WSN. Either by design, or due to network clocks de-synchronization, each sensor may end-up sampling the underlying field at a different time instance.

We argue that such a limitation can actually be used in our advantage and achieve temporal super-resolution. To achieve strict performance requirements and overcome the limitations of challenging operating environments, we exploit redundancies in data collected over time, which are modeled by low rank measurement matrices. Related Projects: Hydrobionets.

Modern sensing applications have been facing up to the challenge of moving from episodic, periodic sampling to pervasive paradigms, relying on the resilient, long-term and unattended operation of Distributed Sensor Networks. From a network perspective, the problem at hand is how we can exploit the inherent redundancy of heterogeneous data e. Towards this direction, our research agenda moves along the following lines:.

Wireless Communications: Signal Processing Perspectives (Prentice Hall Signal Processing Series)

Our approach exploits the concept of Delaunay triangulations in order to establish a scalable framework for solving problems associated to topology control in polynomial time and a localized fashion that, against current state-of-art, eliminates the necessity of additional network traffic. Synthesis, implementation, and evaluation of unsupervised feature-level fusion algorithms that exploit the inherited redundancy of network information available at: a across different sides of the network and b across different layers of a fully functional protocol stack, ranging from the Physical to the Transport and Application layers.

Regardless of the type of deployment e. Related Projects: Hydrobionets , Sense. Selected Publications:. Latest trends in wearable-based Human Activity Recognition are challenged by the need to transit from off-line, centralized classification of a-priori known activities towards on-line processing and learning architectures, which are dictated by the need to analyse and interpret complex activities while in data capture.

To respond to this challenge, our research considers the combination of modern signal processing techniques and Body Sensor Networks architectures for:. Coping with the operating imperfections of the underlying sensing infrastructure, which lead to substantial losses of missing values.

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In a nutshell, we synthesize classification frameworks that consider the presence ofmissing values during runtime and propose the use of imputation methods matrix completion, tensor completion for reconstructing inertial sensing data streams. Our methodology additionally consider the effect of the reconstruction technique on the classification accuracy of landmark classifiers with respect to the persentage of missing values. Signals and Systems, 1st Edition Prentice-Hall signal processing series.

Signal Processing. Signal Processing for Digital Communications.

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Sensor Array Signal Processing. Statistical Signal Processing. Modern signal processing. Discrete-Time Signal Processing. Adaptive Signal Processing. Convex Optimization in Signal Processing and Communications. Signal Processing for Mobile Communications Handbook. Digital Signal Processing.

Detection in Sensor Networks — University of Illinois at Urbana-Champaign

Multirate Digital Signal Processing. Modern Digital Signal Processing. Multidimensional Digital Signal Processing.

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