Problems In Distributed Signal Processing In Wireless Sensor Networks
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Problems in Distributed Signal Processing in Wireless Sensor Networks
In this thesis, we first consider the problem of distributed estimation in an energy and rate-constrained wireless sensor network. To this end, we study three estimators namely - (1) Best Linear Unbiased Estimator (BLUE-1) that accounts for the variance of noise in measurement, uniform quantization and channel, and derive its variance and its lower bound; (2) Best Linear Unbiased Estimator (BLUE-2) that accounts for the variance of noise in measurement and uniform quantization, and derive lower and upper bounds for its variance; (3) Best Linear Unbiased Estima- tor (BLUE-3) that incorporates the effects of probabilistic quantization noise and measurement noise, and derive an upper bound for its variance. Then using BLUE-1, we analyze the tradeoff between estimation error (BLUE variance) at the fusion center and the total amount of resources utilized (power and rate) using three different system design approaches or optimization formulations. For all the formulations, we determine optimum quantization bits and transmission power per bit (or optimum actions) for all sensors jointly. Unlike prior efforts, we in- corporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. We study the e®ect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit. In the sequel, we consider a problem in distributed detection and signal processing in the context of biomedical wireless sensors and more specifically pulse- oximeter devices that record photoplethysmographic data. We propose an automated, two-stage PPG data processing method to minimize the effect of motion artifact. Regarding stage one, we present novel and consistent techniques to detect the presence of motion artifact in photoplethysmograms given higher order statistical information present in the data. For stage two, we propose an effective motion artifact reduction method that involves enhanced PPG data preprocessing followed by frequency domain Independent Component Analysis (FD-ICA). Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method. Finally, we analyze a wireless ad hoc/sensor network where nodes are connected via random channels and information is transported in the network in a cooperative multihop fashion using amplify and forward relay strategy.
Wireless Sensor Networks
A wireless sensor network (WSN) uses a number of autonomous devices to cooperatively monitor physical or environmental conditions via a wireless network. Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection. Technological advancements, coupled with lowering costs, suggest that wireless sensor networks will have a significant impact on 21st century life. The design of wireless sensor networks requires consideration for several disciplines such as distributed signal processing, communications and cross-layer design. Wireless Sensor Networks: Signal Processing and Communications focuses on the theoretical aspects of wireless sensor networks and offers readers signal processing and communication perspectives on the design of large-scale networks. It explains state-of-the-art design theories and techniques to readers and places emphasis on the fundamental properties of large-scale sensor networks. Wireless Sensor Networks: Signal Processing and Communications : Approaches WSNs from a new angle – distributed signal processing, communication algorithms and novel cross-layer design paradigms. Applies ideas and illustrations from classical theory to an emerging field of WSN applications. Presents important analytical tools for use in the design of application-specific WSNs. Wireless Sensor Networks will be of use to signal processing and communications researchers and practitioners in applying classical theory to network design. It identifies research directions for senior undergraduate and graduate students and offers a rich bibliography for further reading and investigation.
Wireless Sensor Networks
Author: Siva Yellampalli
language: en
Publisher: BoD – Books on Demand
Release Date: 2021-09-15
Wireless sensor networks (WSNs) consist of tiny sensors capable of sensing, computing, and communicating. Due to advances in semiconductors, networking, and material science technologies, it is now possible to deploy large-scale WSNs. The advancement in these technologies has not only decreased the deployment and maintenance costs of networks but has also increased the life of networks and made them more rugged. As WSNs become more reliable with lower maintenance costs, they are being deployed and used across various sectors for multiple applications. This book discusses the applications, challenges, and design and deployment techniques of WSNs.