On the Development of Distributed Estimation Techniques for Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately witnessed tremendous demand, as evidenced by the increasing number of day-to-day applications. The sensor nodes aim at estimating the parameters of their corresponding adaptive filters to achieve the desired response for the event of interest. Some of the burning issues related to linear parameter estimation in WSNs have been addressed in this thesis mainly focusing on reduction of communication overhead and latency, and robustness to noise. The first issue deals with the high communication overhead and latency in distributed parameter estimation techniques such as diffusion least mean squares (DLMS) and incremental least mean squares (ILMS) algorithms. Subsequently the poor performance demonstrated by these distributed techniques in presence of impulsive noise has been dealt separately. The issue of source localization i.e. estimation of source bearing in WSNs, where the existing decentralized algorithms fail to perform satisfactorily, has been resolved in this thesis. Further the same issue has been dealt separately independent of nodal connectivity in WSNs. This thesis proposes two algorithms namely the block diffusion least mean squares (BDLMS) and block incremental least mean squares (BILMS) algorithms for reducing the communication overhead in WSNs. The theoretical and simulation studies demonstrate that BDLMS and BILMS algorithms provide the same performances as that of DLMS and ILMS, but with significant reduction in communication overheads per node. The latency also reduces by a factor as high as the block-size used in the proposed algorithms. With an aim to develop robustness towards impulsive noise, this thesis proposes three robust distributed algorithms i.e. saturation nonlinearity incremental LMS (SNILMS), saturation nonlinearity diffusion LMS (SNDLMS) and Wilcoxon norm diffusion LMS (WNDLMS) algorithms. The steady-state analysis of SNILMS algorithm is carried out based on spatial-temporal energy conservation principle. The theoretical and simulation results show that these algorithms are robust to impulsive noise. The SNDLMS algorithm is found to provide better performance than SNILMS and WNDLMS algorithms. In order to develop a distributed source localization technique, a novel diffusion maximum likelihood (ML) bearing estimation algorithm is proposed in this thesis which needs less communication overhead than the centralized algorithms. After forming a random array with its neighbours, each sensor node estimates the source bearing by optimizing the ML function locally using a diffusion particle swarm optimization algorithm. The simulation results show that the proposed algorithm performs better than the centralized multiple signal classification (MUSIC) algorithm in terms of probability of resolution and root mean square error. Further, in order to make the proposed algorithm independent of nodal connectivity, a distributed in-cluster bearing estimation technique is proposed. Each cluster of sensors estimates the source bearing by optimizing the ML function locally in cooperation with other clusters. The simulation results demonstrate improved performance of the proposed method in comparison to the centralized and decentralized MUSIC algorithms, and the distributed in-network algorith

Privacy-Preserving Decentralized Optimization and Event Localization

This dissertation considers decentralized optimization and its applications. On the one hand, we address privacy preservation for decentralized optimization, where N agents cooperatively minimize the sum of N convex functions private to these individual agents. In most existing decentralized optimization approaches, participating agents exchange and disclose states explicitly, which may not be desirable when the states contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we first propose two privacy-preserving decentralized optimization approaches based on ADMM (alternating direction method of multipliers) and subgradient method, respectively, by leveraging partially homomorphic cryptography. To our knowledge, this is the first time that cryptographic techniques are incorporated in a fully decentralized setting to enable privacy preservation in decentralized optimization in the absence of any third party or aggregator. To facilitate the incorporation of encryption in a fully decentralized manner, we also introduce a new ADMM which allows time-varying penalty matrices and rigorously prove that it has a convergence rate of O(1/t). However, given that encryption-based algorithms unavoidably bring about extra computational and communication overhead in real-time optimization [61], we then propose another novel privacy solution for decentralized optimization based on function decomposition and ADMM which enables privacy without incurring large communication/computational overhead. On the other hand, we address the application of decentralized optimization to the event localization problem, which plays a fundamental role in many wireless sensor network applications such as environmental monitoring, homeland security, medical treatment, and health care. The event localization problem is essentially a non-convex and non-smooth problem. We address such a problem in two ways. First, a completely decentralized solution based on augmented Lagrangian methods and ADMM is proposed to solve the non-smooth and non-convex problem directly, rather than using conventional convex relaxation techniques. However, this algorithm requires the target event to be within the convex hull of the deployed sensors. To address this issue, we propose another two scalable distributed algorithms based on ADMM and convex relaxation, which do not require the target event to be within the convex hull of the deployed sensors. Simulation results confirm effectiveness of the proposed algorithms

Effect of frequency domain attributes of wavelet analysis filter banks for structural damage localization using the relative wavelet entropy index

A novel numerical study is undertaken to assess the influence of the frequency domain (FD) attributes of wavelet analysis filter banks for vibration-based structural damage detection and localization using the relative wavelet entropy (RWE): a damage-sensitive index derived by wavelet transforming linear response acceleration signals from a healthy/reference and a damaged state of a given structure subject to broadband excitations. Four different judicially defined energy-preserving wavelet analysis filter banks are employed to compute the RWE pertaining to two benchmark structures via algorithms which can efficiently run on wireless sensors for decentralized structural health monitoring. It is shown that filter banks of compactly supported in the FD wavelet bases (e.g., Meyer wavelets and harmonic wavelets) perform significantly better than the commonly used in the literature dyadic Haar discrete wavelet transform filter banks since they achieve enhanced frequency selectivity among scales (i.e., minimum overlapping of the frequency bands corresponding to adjacent scales) and, therefore, reduce energy leakage and facilitate the interpretation of numerical results in terms of scale/frequency dependent contributors to the RWE. Moreover, it is demonstrated that dyadic DWT filter banks with large constant Q values (i.e., ratio of effective frequency over effective bandwidth) are better qualified to capture damage information associated with high frequencies. Finally, it is concluded that wavelet analysis filter banks achieving nonconstant Q analysis are most effective for RWE-based stationary damage detection as they are not limited by the dyadic DWT discretization and can target the structural natural frequencies in cases these are a priori known

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Diffusion Based Distributed Detection in Wireless Sensor Network

Distributed wireless sensor networks find various remote sensing purposes like battleground monitoring, target localization, environmental monitoring, accurate cultivation, mobile communication and medicinal applications. Due to a wide variety of applications of wireless data, suitable design and implementation of data detection become the modern field of study and research. The distribution of the nodes in the network provides a spatial diversity, which includes the temporal dimension for the purpose of increase the robustness of the ongoing tasks and enhance the probability of data and event detection. In this area, we study the distributed network that contain the collection of a node connected to each other in the distributed manner. The node connected to each other is called neighbor node. In the problem of distributed detection of data, nodes have to decide based on the binary hypotheses of the measured data. In this detection problem we find the fully distributed and adaptive approach where all the node have to make own real time decision by cooperating with their immediate neighbour only and for this implementation no central processing node is required.For this distributed detection, we used diffusion based strategies Diffusion least mean square (DLMS) and Diffusion recursive least mean square(RLS) to find out distributed estimation of the parameter of interest. Distributed detection suitable in the wireless sensor network due to their robustness to node and link failure as compare to centralized scheme,,scalability and ability to save power and communication resources.The algorithm utilized is adaptive and track the variation in the active hypotheses.After the use for detection we analyze the performance of the proposed algorithm in term of probability og detection and probability of false alarm and find out the simulation result. We use some nonlinear techniques(huber loss , bi-square ) to reduce the effect of impulsive interference on the systems