1,931 research outputs found
Energy-efficient Communication and Estimation in Wireless Sensor Networks
This thesis focuses on the energy efficiency in wireless networks under
the transmission and information diffusion points of view. In particular,
on one hand, the communication efficiency is investigated,
attempting to reduce the consumption during transmissions, while on
the other hand the energy efficiency of the procedures required to distribute
the information among wireless nodes in complex networks is
taken into account.
For what concerns energy efficient communications, an innovative
transmission scheme reusing source of opportunity signals is introduced.
This kind of signals has never been previously studied in literature
for communication purposes. The scope is to provide a way for
transmitting information with energy consumption close to zero. On
the theoretical side, starting from a general communication channel
model subject to a limited input amplitude, the theme of low power
transmission signals is tackled under the perspective of stating sufficient
conditions for the capacity achieving input distribution to be
discrete.
Finally, the focus is shifted towards the design of energy efficient
algorithms for the diffusion of information. In particular, the endeavours
are aimed at solving an estimation problem distributed over a
wireless sensor network. The proposed solutions are deeply analyzed
both to ensure their energy efficiency and to guarantee their robustness
against losses during the diffusion of information (against information
diffusion truncation more in general)
Energy-aware Sparse Sensing of Spatial-temporally Correlated Random Fields
This dissertation focuses on the development of theories and practices of energy aware sparse sensing schemes of random fields that are correlated in the space and/or time domains. The objective of sparse sensing is to reduce the number of sensing samples in the space and/or time domains, thus reduce the energy consumption and complexity of the sensing system. Both centralized and decentralized sensing schemes are considered in this dissertation.
Firstly we study the problem of energy efficient Level set estimation (LSE) of random fields correlated in time and/or space under a total power constraint. We consider uniform sampling schemes of a sensing system with a single sensor and a linear sensor network with sensors distributed uniformly on a line where sensors employ a fixed sampling rate to minimize the LSE error probability in the long term. The exact analytical cost functions and their respective upper bounds of these sampling schemes are developed by using an optimum thresholding-based LSE algorithm. The design parameters of these sampling schemes are optimized by minimizing their
respective cost functions. With the analytical results, we can identify the optimum sampling period and/or node distance that can minimize the LSE error probability.
Secondly we propose active sparse sensing schemes with LSE of a spatial-temporally correlated random field by using a limited number of spatially distributed sensors. In these schemes a central controller is designed to dynamically select a limited number of sensing locations according to the information revealed from past measurements,and the objective is to minimize the expected level set estimation error.The expected estimation error probability is explicitly expressed as a function of the selected sensing locations, and the results are used to formulate the optimal sensing location selection problem as a combinatorial problem. Two low complexity greedy algorithms are developed by using analytical upper bounds of the expected estimation error probability.
Lastly we study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs).
We propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and local estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples
Deterministic Bayesian Information Fusion and the Analysis of its Performance
This paper develops a mathematical and computational framework for analyzing
the expected performance of Bayesian data fusion, or joint statistical
inference, within a sensor network. We use variational techniques to obtain the
posterior expectation as the optimal fusion rule under a deterministic
constraint and a quadratic cost, and study the smoothness and other properties
of its classification performance. For a certain class of fusion problems, we
prove that this fusion rule is also optimal in a much wider sense and satisfies
strong asymptotic convergence results. We show how these results apply to a
variety of examples with Gaussian, exponential and other statistics, and
discuss computational methods for determining the fusion system's performance
in more general, large-scale problems. These results are motivated by studying
the performance of fusing multi-modal radar and acoustic sensors for detecting
explosive substances, but have broad applicability to other Bayesian decision
problems
D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking
Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the
various Joint Research Activities (JRA) in WP1.3 and the results
that were developed up to the second year of the project. For
each activity there is a description, an illustration of the
adherence to and relevance with the identified fundamental
open issues, a short presentation of the main results, and a
roadmap for the future joint research. In the Annex, for each
JRA, the main technical details on specific scientific activities
are described in detail.Peer ReviewedPostprint (published version
Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006
Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model
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